From 2cced11fa796214cba91993918c42d8d28e4bce1 Mon Sep 17 00:00:00 2001 From: David Thrower Date: Mon, 24 Nov 2025 19:14:27 -0500 Subject: [PATCH 1/4] Delete 2025_11_23_demo_train_an_llm_with_cerebros.ipynb Deleted file version with a typo. --- ...1_23_demo_train_an_llm_with_cerebros.ipynb | 6561 ----------------- 1 file changed, 6561 deletions(-) delete mode 100644 2025_11_23_demo_train_an_llm_with_cerebros.ipynb diff --git a/2025_11_23_demo_train_an_llm_with_cerebros.ipynb b/2025_11_23_demo_train_an_llm_with_cerebros.ipynb deleted file mode 100644 index 9004212..0000000 --- a/2025_11_23_demo_train_an_llm_with_cerebros.ipynb +++ /dev/null @@ -1,6561 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Build our LLM From Scratch -\n", - "\n", - "## How Cerebros NotGPT works under the hood:\n", - "\n", - "\n", - "### This notebook demonstrates the end-to-end training pipeline that builds a small scale generative LLM from scratch, a small scale proof of concept for our own Cerebros NotGPT model, then fine tunes it on additional data.\n", - "\n", - "The process is divided into two main phases:\n", - "\n", - "- Phase I-a: Neural Architecture Search (NAS) - We use SimpleCerebrosRandomSearch to automatically discover an effective neural network architecture from a small dataset.\n", - "- Phase I-b: Extended Training - The best architecture found in Phase I-a is then trained on a larger dataset to improve its performance.\n", - "\n", - "Finally, the trained model is evaluated and serialized for future use.\n", - "\n", - "\n", - "## Setup and Configuration\n", - "\n", - "Note: This script is configured as a vanilla-scale demo environment (4 CPU / 16 GB RAM Linux with Python 3.12). No GPU is needed, and this will run in the free version of Google Colab. \n", - "\n", - "## Vanilla Demo\n", - "\n", - "- For production use, you would significantly increase the sample sizes and adjust other parameters accordingly.\n", - "- The quality of the text generated by this minimal demo (trained on 30 text samples at a sequence length of 40) does not represent the quality of a full-scale model generated from the same code.\n", - "- A script that can be modified to do such as availible at: https://github.com/david-thrower/cerebros-core-algorithm-alpha/blob/main/train_a_generative_llm.py" - ], - "metadata": { - "id": "nnsAHoJyWLed" - } - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NzJF6_JuWElV", - "outputId": "a0f3246f-0ccd-48ea-da55-86479bc0f93c" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Python 3.12.12\n" - ] - } - ], - "source": [ - "! python --version" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Getting started: Download the repo and go to the main directory of the repo" - ], - "metadata": { - "id": "f6TD2XsKPJIY" - } - }, - { - "cell_type": "code", - "source": [ - "# Download the repo\n", - "! git clone https://github.com/david-thrower/cerebros-core-algorithm-alpha.git" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "AcECFSs7WVsi", - "outputId": "9fd59935-35d4-4a08-9c8a-fb01fd3e4f03" - }, - "execution_count": 25, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Cloning into 'cerebros-core-algorithm-alpha'...\n", - "remote: Enumerating objects: 8036, done.\u001b[K\n", - "remote: Counting objects: 100% (1737/1737), done.\u001b[K\n", - "remote: Compressing objects: 100% (321/321), done.\u001b[K\n", - "remote: Total 8036 (delta 1612), reused 1449 (delta 1411), pack-reused 6299 (from 2)\u001b[K\n", - "Receiving objects: 100% (8036/8036), 65.90 MiB | 21.67 MiB/s, done.\n", - "Resolving deltas: 100% (3116/3116), done.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# set the working directory\n", - "%cd cerebros-core-algorithm-alpha" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "mCpJGfD2WfLj", - "outputId": "e0fe8c05-6154-41cd-f489-08cfd2ad0fa8" - }, - "execution_count": 26, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content/cerebros-core-algorithm-alpha/cerebros-core-algorithm-alpha\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Next install all dependencies.\n", - "\n", - "There are 2 requirement files:\n", - " - requirements.txt: The core requirements of the neural architecture search\n", - " - cicd-requirements.txt: Requirements for NLP and text generation" - ], - "metadata": { - "id": "yT4hPXOKPU_8" - } - }, - { - "cell_type": "code", - "source": [ - "# Install the requirements for the core algorithm\n", - "! pip install -r requirements.txt; pip install -r cicd-requirements.txt" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "nwElyEdpW90P", - "outputId": "170e2158-b7a9-49f0-ce63-22c4c7410f33" - }, - "execution_count": 27, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: jax==0.5.3 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 1)) (0.5.3)\n", - "Requirement already satisfied: jaxlib==0.5.3 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 2)) (0.5.3)\n", - "Requirement already satisfied: pendulum==3.0.0 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 3)) (3.0.0)\n", - "Collecting tensorflow==2.20.0 (from -r requirements.txt (line 4))\n", - " Using cached tensorflow-2.20.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)\n", - "Collecting numpy==2.3.5 (from -r requirements.txt (line 5))\n", - " Using cached numpy-2.3.5-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (62 kB)\n", - 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" Uninstalling numpy-1.26.4:\n", - " Successfully uninstalled numpy-1.26.4\n", - " Attempting uninstall: tensorboard\n", - " Found existing installation: tensorboard 2.19.0\n", - " Uninstalling tensorboard-2.19.0:\n", - " Successfully uninstalled tensorboard-2.19.0\n", - " Attempting uninstall: tensorflow\n", - " Found existing installation: tensorflow 2.19.1\n", - " Uninstalling tensorflow-2.19.1:\n", - " Successfully uninstalled tensorflow-2.19.1\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "scikit-learn 1.4.1.post1 requires numpy<2.0,>=1.19.5, but you have numpy 2.3.5 which is incompatible.\n", - "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.3 which is incompatible.\n", - "tensorflow-text 2.19.0 requires tensorflow<2.20,>=2.19.0, but you have tensorflow 2.20.0 which is incompatible.\n", - "opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.3.5 which is incompatible.\n", - "numba 0.60.0 requires numpy<2.1,>=1.22, but you have numpy 2.3.5 which is incompatible.\n", - "opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.3.5 which is incompatible.\n", - "umap-learn 0.5.9.post2 requires scikit-learn>=1.6, but you have scikit-learn 1.4.1.post1 which is incompatible.\n", - "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.3.5 which is incompatible.\n", - "orbax-checkpoint 0.11.28 requires jax>=0.6.0, but you have jax 0.5.3 which is incompatible.\n", - "tensorflow-decision-forests 1.12.0 requires tensorflow==2.19.0, but you have tensorflow 2.20.0 which is incompatible.\n", - "flax 0.10.7 requires jax>=0.6.0, but you have jax 0.5.3 which is incompatible.\n", - "tf-keras 2.19.0 requires tensorflow<2.20,>=2.19, but you have tensorflow 2.20.0 which is incompatible.\n", - "imbalanced-learn 0.14.0 requires scikit-learn<2,>=1.4.2, but you have scikit-learn 1.4.1.post1 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed numpy-2.3.5 tensorboard-2.20.0 tensorflow-2.20.0\n", - "Requirement already satisfied: tensorflow-text==2.19.0 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 1)) (2.19.0)\n", - "Requirement already satisfied: keras-nlp==0.19.0 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 2)) (0.19.0)\n", - "Requirement already satisfied: scikit-learn==1.4.1.post1 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 3)) (1.4.1.post1)\n", - "Requirement already satisfied: tensorflow-hub==0.16.1 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 4)) (0.16.1)\n", - "Requirement already satisfied: transformers==4.54.0 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 5)) (4.54.0)\n", - "Collecting tensorflow<2.20,>=2.19.0 (from tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1))\n", - " Using cached tensorflow-2.19.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)\n", - "Requirement already satisfied: keras-hub==0.19.0 in /usr/local/lib/python3.12/dist-packages (from keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (0.19.0)\n", - "Collecting numpy<2.0,>=1.19.5 (from scikit-learn==1.4.1.post1->-r cicd-requirements.txt (line 3))\n", - " Using cached numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n", - "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.4.1.post1->-r cicd-requirements.txt (line 3)) (1.16.3)\n", - "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.4.1.post1->-r cicd-requirements.txt (line 3)) (1.5.2)\n", - "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.4.1.post1->-r cicd-requirements.txt (line 3)) (3.6.0)\n", - "Requirement already satisfied: protobuf>=3.19.6 in /usr/local/lib/python3.12/dist-packages (from tensorflow-hub==0.16.1->-r cicd-requirements.txt (line 4)) (5.29.5)\n", - "Requirement already satisfied: tf-keras>=2.14.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow-hub==0.16.1->-r cicd-requirements.txt (line 4)) (2.19.0)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (3.20.0)\n", - "Requirement already satisfied: huggingface-hub<1.0,>=0.34.0 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (0.36.0)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (25.0)\n", - "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (6.0.3)\n", - "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (2024.11.6)\n", - "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (2.32.4)\n", - "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (0.21.4)\n", - "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (0.7.0)\n", - "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (4.67.1)\n", - "Requirement already satisfied: keras>=3.5 in /usr/local/lib/python3.12/dist-packages (from keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (3.10.0)\n", - "Requirement already satisfied: absl-py in /usr/local/lib/python3.12/dist-packages (from keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (1.4.0)\n", - "Requirement already satisfied: rich in /usr/local/lib/python3.12/dist-packages (from keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (13.9.4)\n", - "Requirement already satisfied: kagglehub in /usr/local/lib/python3.12/dist-packages (from keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (0.3.13)\n", - "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (2025.3.0)\n", - "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (4.15.0)\n", - "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (1.2.0)\n", - "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (1.6.3)\n", - "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (25.9.23)\n", - "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.6.0)\n", - "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.2.0)\n", - "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (18.1.1)\n", - "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.4.0)\n", - "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (75.2.0)\n", - "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (1.17.0)\n", - "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.2.0)\n", - "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (2.0.1)\n", - "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (1.76.0)\n", - "Collecting tensorboard~=2.19.0 (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1))\n", - " Using cached tensorboard-2.19.0-py3-none-any.whl.metadata (1.8 kB)\n", - "Requirement already satisfied: h5py>=3.11.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.15.1)\n", - "Requirement already satisfied: ml-dtypes<1.0.0,>=0.5.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.5.4)\n", - "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (3.4.4)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (3.11)\n", - "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (2.5.0)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (2025.11.12)\n", - "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from astunparse>=1.6.0->tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.45.1)\n", - "Requirement already satisfied: namex in /usr/local/lib/python3.12/dist-packages (from keras>=3.5->keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (0.1.0)\n", - "Requirement already satisfied: optree in /usr/local/lib/python3.12/dist-packages (from keras>=3.5->keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (0.18.0)\n", - "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.10)\n", - "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.7.2)\n", - "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.1.3)\n", - "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (4.0.0)\n", - "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (2.19.2)\n", - "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (0.1.2)\n", - "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.12/dist-packages (from werkzeug>=1.0.1->tensorboard~=2.19.0->tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.0.3)\n", - "Using cached numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.0 MB)\n", - "Using cached tensorflow-2.19.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (645.0 MB)\n", - "Using cached tensorboard-2.19.0-py3-none-any.whl (5.5 MB)\n", - "Installing collected packages: numpy, tensorboard, tensorflow\n", - " Attempting uninstall: numpy\n", - " Found existing installation: numpy 2.3.5\n", - " Uninstalling numpy-2.3.5:\n", - " Successfully uninstalled numpy-2.3.5\n", - " Attempting uninstall: tensorboard\n", - " Found existing installation: tensorboard 2.20.0\n", - " Uninstalling tensorboard-2.20.0:\n", - " Successfully uninstalled tensorboard-2.20.0\n", - " Attempting uninstall: tensorflow\n", - " Found existing installation: tensorflow 2.20.0\n", - " Uninstalling tensorflow-2.20.0:\n", - " Successfully uninstalled tensorflow-2.20.0\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.3 which is incompatible.\n", - "opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", - "opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", - "pytensor 2.35.1 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", - "umap-learn 0.5.9.post2 requires scikit-learn>=1.6, but you have scikit-learn 1.4.1.post1 which is incompatible.\n", - "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", - "orbax-checkpoint 0.11.28 requires jax>=0.6.0, but you have jax 0.5.3 which is incompatible.\n", - "tensorflow-decision-forests 1.12.0 requires tensorflow==2.19.0, but you have tensorflow 2.19.1 which is incompatible.\n", - "flax 0.10.7 requires jax>=0.6.0, but you have jax 0.5.3 which is incompatible.\n", - "shap 0.50.0 requires numpy>=2, but you have numpy 1.26.4 which is incompatible.\n", - "imbalanced-learn 0.14.0 requires scikit-learn<2,>=1.4.2, but you have scikit-learn 1.4.1.post1 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed numpy-1.26.4 tensorboard-2.19.0 tensorflow-2.19.1\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.colab-display-data+json": { - "pip_warning": { - "packages": [ - "numpy", - "tensorflow" - ] - }, - "id": "d3a167bbbde043ef9a994c35060fda79" - } - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# **RESTART THE SESSION**\n", - "\n", - "Then proceed to the next cell which imports all necessary libraries and defines global constants and hyperparameters for the entire pipeline.\n" - ], - "metadata": { - "id": "v69rLBcmXyGD" - } - }, - { - "cell_type": "code", - "source": [ - "! ls" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ubtKyfBQzFEW", - "outputId": "6cbe44e6-3ce7-4227-982a-88d0d36d2205" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "cerebros-core-algorithm-alpha sample_data\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# 1. # **ONLY IF** the directory cerebros-core-algorithm-alpha is not still\n", - "# there, clone the directory again.\n", - "# ! git clone https://github.com/david-thrower/cerebros-core-algorithm-alpha.git\n", - "\n", - "# 2. Set the working directory (in the new session) - DO run this.\n", - "%cd cerebros-core-algorithm-alpha" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NemXTsYgfE0s", - "outputId": "ca92342f-1f82-42ee-8562-980b1c8dd849" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content/cerebros-core-algorithm-alpha\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Verify we are in the right place:\n", - "! pwd" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "D3K4dSVQhrIc", - "outputId": "5a45fa94-1bb3-46ce-c362-27f456221fd6" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content/cerebros-core-algorithm-alpha\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Standard library imports\n", - "import subprocess\n", - "import time\n", - "from gc import collect\n", - "\n", - "# Third-party library imports\n", - "import tensorflow as tf\n", - "import pandas as pd\n", - "import pendulum\n", - "from transformers import AutoTokenizer\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# Cerebros specific imports\n", - "from cerebros.units.units import DenseUnit\n", - "from cerebros.simplecerebrosrandomsearch.simple_cerebros_random_search import SimpleCerebrosRandomSearch\n", - "from cerebros.denseautomlstructuralcomponent.dense_automl_structural_component import (\n", - " zero_7_exp_decay,\n", - " zero_95_exp_decay,\n", - " simple_sigmoid\n", - ")\n", - "from cerebrosllmutils.llm_utils import (\n", - " prepare_data,\n", - " InterleavedRoPE,\n", - " Perplexity,\n", - " CerebrosNotGPTConfig,\n", - " CerebrosNotGPT,\n", - " WarmupCosineDecayRestarts\n", - ")\n", - "\n", - "# Import the data source: Format List[str]\n", - "from vanilladatasets.web_english_bible import samples as bible\n", - "\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WKCdCv96X4YX", - "outputId": "875f6626-4f4b-426c-c697-da9f186e440a" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.12/dist-packages/jaxlib/plugin_support.py:71: RuntimeWarning: JAX plugin jax_cuda12_plugin version 0.7.2 is installed, but it is not compatible with the installed jaxlib version 0.5.3, so it will not be used.\n", - " warnings.warn(\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Data and Training Constants\n", - "\n", - "These parameters control the amount of data used and the behavior of the training stages.\n", - "\n", - "- **PHASE_I_A_SAMPLES_TO_CREATE**: Size of the subset of the dataset used for the NAS (Neural Architecture Search) stage (number of text samples).\n", - "- **PHASE_I_B_SAMPLES_TO_CREATE**: Number of samples to use for the main training task stage after Neural Architecture Search is completed.\n", - "- **PHASE_I_B_VAL_SPLIT**: Fraction of data for validation in Phase I-b.\n", - "- **PHASE_I_B_SAMPLE_EXPANSION_BATCH_SIZE**: Batch size for preprocessing in Phase I-b to manage RAM.\n", - "- **PROMPT_LENGTH**: Number of tokens provided to the model to predict the next token. It is recommended to keep this as 1.\n" - ], - "metadata": { - "id": "rK0LZP7KbQqm" - } - }, - { - "cell_type": "code", - "source": [ - "# Samples to use for the neural architecture search stage\n", - "PHASE_I_A_SAMPLES_TO_CREATE = 10\n", - "\n", - "# Samples to use for the main training stage\n", - "PHASE_I_B_SAMPLES_TO_CREATE = 20\n", - "PHASE_I_B_VAL_SPLIT = 0.15\n", - "\n", - "# For Stage I-b, we preprocess in batches to avoid high RAM usage.\n", - "PHASE_I_B_SAMPLE_EXPANSION_BATCH_SIZE = 10\n", - "\n", - "# How many tokens to provide before expecting the next token to be predicted.\n", - "PROMPT_LENGTH = 1\n" - ], - "metadata": { - "id": "vywbZQxAZC9R" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# Model and Embedding Constants\n", - "\n", - "These constants define the size and shape of the model's text processing components.\n", - "\n", - "- **MAX_SEQ_LENGTH**: The maximum sequence length the model will handle. This has a linear relationship with RAM/CPU usage.\n", - "- **tokenizer_checkpoint**: The Hugging Face model to use for tokenization.\n", - "- **EMBEDDING_N**: A factor to determine the embedding dimensionality (EMBEDDING_DIM = EMBEDDING_N * 2). A factor to determine the embedding dimensionality (EMBEDDING_DIM = EMBEDDING_N * 2). The resulting embedding dimensionality (EMBEDDING_DIM) for InterleavedRoPE must be an even number. Using this parameter as a proxy, rather than setting EMBEDDING_DIM directly, acts as a guard rail to ensure this constraint is met.\n", - "- **PROJECTION_N**: Controls the size of a projection layer after embedding. Increasing this value can significantly increase RAM usage.\n" - ], - "metadata": { - "id": "5jK5wbA5b8se" - } - }, - { - "cell_type": "code", - "source": [ - "# Text encoding / embedding related constants\n", - "MAX_SEQ_LENGTH = 40\n", - "\n", - "# Tokenization\n", - "tokenizer_checkpoint = \"HuggingFaceTB/SmolLM3-3B\"\n", - "tokenizer = AutoTokenizer.from_pretrained(tokenizer_checkpoint)\n", - "\n", - "# Add special tokens for potential instruction-following formats\n", - "special_tokens = {\n", - " \"additional_special_tokens\": [\"\", \"\", \"\", \"\"]\n", - "}\n", - "tokenizer.add_special_tokens(special_tokens)\n", - "\n", - "VOCABULARY_SIZE = len(tokenizer)\n", - "\n", - "# For InterleavedRoPE, the embedding output dim must be an even number.\n", - "EMBEDDING_N = 6\n", - "EMBEDDING_DIM = int(EMBEDDING_N * 2)\n", - "\n", - "# Size of the projection layer. Keep low to manage RAM.\n", - "PROJECTION_N = 1\n" - ], - "metadata": { - "id": "4Kka_A4tb3aJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "6c85d1ae-52f4-4ddf-d768-ea5781b1b7da" - }, - "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Stage I-a (NAS) Hyperparameters\n", - "\n", - "These parameters control the Neural Architecture Search process.\n", - "\n", - "- **moities_to_try**: Number of different layer permutations to try.\n", - "- **tries_per_moity**: Number of topologies to try for each permutation.\n", - "- **epochs, batch_size, learning_rate**: Standard training parameters for the NAS stage.\n", - "- **predecessor_level_connection_affinity_factor_first**: Controls connectivity density between the Input layer and the first level of Dense layers.\n", - "- **predecessor_level_connection_affinity_factor_main**: Controls connectivity density between the Input layer and the first level of Dense layers and the subsequent level of Dense layers, as well as all subsequent vertical connectivity.\n", - "- **p_lateral_connection, num_lateral_connection_tries_per_unit**: Control the density of lateral connectivity between Dense layers on the same row.\n", - "- **minimum_levels, maximum_levels**: Number of **rows of** Dense layers in the architecture grid.\n", - "- **minimum_units_per_level, maximum_units_per_level**: Number of Dense layers per row.\n", - "- **minimum_neurons_per_unit, maximum_neurons_per_unit**: The number of neurons for each Dense layer unit.\n" - ], - "metadata": { - "id": "MeoWtePacWz_" - } - }, - { - "cell_type": "code", - "source": [ - "# Cerebros [non-HP-tunable] configurables for NAS\n", - "moities_to_try = 3\n", - "tries_per_moity = 1\n", - "\n", - "### Main tunable hyperparameters for NAS ##\n", - "\n", - "POSITIONAL_EMBEDDING_DROPOUT = 0.7651951380000674\n", - "activation = 'softplus'\n", - "\n", - "# Vertical connectivity hyperparameters\n", - "predecessor_level_connection_affinity_factor_first = 17.851026458010523\n", - "predecessor_level_connection_affinity_factor_main = 21.487301631581428\n", - "\n", - "# Lateral connectivity hyperparameters\n", - "max_consecutive_lateral_connections = 7\n", - "p_lateral_connection = 0.24927354102044022\n", - "num_lateral_connection_tries_per_unit = 32\n", - "learning_rate = 0.003025583248301791\n", - "epochs = 41\n", - "batch_size = 5\n", - "gradient_accumulation_steps = 4\n", - "\n", - "# Architecture grid constraints\n", - "minimum_levels = 2\n", - "maximum_levels = 2\n", - "minimum_units_per_level = 2\n", - "maximum_units_per_level = 2\n", - "minimum_neurons_per_unit = 2\n", - "maximum_neurons_per_unit = 2\n" - ], - "metadata": { - "id": "Wbowkxnbc4Zd" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# Phase I-b (Extended Training) Hyperparameters\n", - "\n", - "These parameters are for fine-tuning the best model from Stage I-a.\n", - "\n", - "- INITIAL_LR_STAGE_I_B: Initial learning rate for this phase.\n", - "- WARMUP_EPOCHS_STAGE_I_B, WARMUP_STEPS: Parameters for the learning rate scheduler.\n", - "- phase_i_b_epochs: Number of epochs for extended training.\n", - "- phase_i_b_weight_decay: Weight decay for the optimizer.\n" - ], - "metadata": { - "id": "fcGTs9ASdXps" - } - }, - { - "cell_type": "code", - "source": [ - "\n", - "## Training Stage I-b parameters:\n", - "INITIAL_LR_STAGE_I_B = 0.0039295722955565125\n", - "WARMUP_EPOCHS_STAGE_I_B = 7\n", - "WARMUP_STEPS = 1140\n", - "FIRST_DECAY_STEPS_STAGE_I_B = 1900\n", - "phase_i_b_epochs = 53\n", - "phase_i_b_gradient_accumulation_steps = 7\n", - "phase_i_b_weight_decay = 0.01647018768215773 # For AdamW\n" - ], - "metadata": { - "id": "-znwaddIdiKU" - }, - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "# Generation Constants\n", - "\n", - "Parameters used during the text generation evaluation phase." - ], - "metadata": { - "id": "vy5y6OXhdvzV" - } - }, - { - "cell_type": "code", - "source": [ - "## Generation time configurables:\n", - "GENERATION_PROMPT_LEN = 25\n", - "MAX_NEW_TOKENS = MAX_SEQ_LENGTH - GENERATION_PROMPT_LEN" - ], - "metadata": { - "id": "JHjCz9qXd5Gq" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# **Data Preparation**\n", - "\n", - "Here, we load and subset the dataset for both training Stages.\n", - "\n", - "\n", - "We first split the Bible text samples into two sets: one for Phase I-a (NAS) and a larger one for Phase I-b (extended training).\n" - ], - "metadata": { - "id": "N7fJIZ1md-0Y" - } - }, - { - "cell_type": "code", - "source": [ - "# Get training data from the bible text samples\n", - "non_instruct_samples = bible[:PHASE_I_A_SAMPLES_TO_CREATE]\n", - "phase_i_b_samples = bible[PHASE_I_A_SAMPLES_TO_CREATE:PHASE_I_B_SAMPLES_TO_CREATE + PHASE_I_A_SAMPLES_TO_CREATE]\n", - "\n", - "print(f\"Samples from KJV bible consisting of {len(non_instruct_samples)} look like this (sub-sample of 3): {non_instruct_samples[:3]}\")\n" - ], - "metadata": { - "id": "jIFxWcBzeLjN", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d46f8e34-3d7d-4fb4-dddc-bf1c45bae7ee" - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Samples from KJV bible consisting of 10 look like this (sub-sample of 3): ['In the beginning God created the heavens and the earth.', \"The earth was formless and empty, with darkness over the deep and God's Spirit hovering over the waters.\", \"God said, 'Let there be light,' and there was light.\"]\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Preprocess Data for Phase I-a (NAS)\n", - "\n", - "The Cerebros LLM is a single head model. This means that each time the model is called, it returns only the next token. It does not regurgitate the cumulative sequence, nor does it have a separate head for each position in the sequence.\n", - "\n", - "For both training stages, each text sample is expanded into multiple input/label pairs, which we call \"sub-samples.\" There is one \"sub-sample\" for each token in the range between the first token and the first occurrence of a padding token or the end of the sequence, whichever comes first.\n", - "\n", - "For example, the sequence [t1, t2, t3] becomes:\n", - "\n", - " Input: [t1, 2, 2, 2] Label: [t2] # One hot encoded to VOCABULARY_SIZE\n", - " Input: [t1, t2, 2, 2], Label: [t3]\n", - " Input: [t1, t2, t3, 2], Label: [2]\n", - "\n", - "For training Stage 1-a, we perform the entire expansion for its training data in memory. This is because the NAS does not yet support a tf.data.Dataset object. In the future, we may retrofit the NAS algorithm to support streaming preprocessing as well, allowing us to use a larger dataset for the NAS.\n", - "\n", - "For stage I-b, the extended training stage, the same operation is done in batches. This is because this operation significantly increases the amount of memory required. The main reason for this is the one-hot encoded label, where the vocabulary size is 128,260. Since we do this in batches, this allows for a virtually unlimited number of samples to be processed.\n", - "\n", - "For reference, this is the preprocessing being applied:\n", - "\n", - "```python\n", - "def prepare_data(\n", - " data_0: List[str],\n", - " tokenizer_0: Any,\n", - " max_seq_length: int = 1024,\n", - " prompt_length: int = 1) -> Tuple[List[List[int]], List[List[int]], int]:\n", - "\n", - "\n", - " all_input_ids = []\n", - " all_labels = []\n", - "\n", - " pad_token_id = tokenizer_0.pad_token_id\n", - "\n", - " # Tokenize all data at once for efficiency\n", - " tokenized_data = tokenizer_0(\n", - " data_0,\n", - " max_length=max_seq_length,\n", - " padding='max_length',\n", - " truncation=True,\n", - " add_special_tokens=False # We'll handle special tokens manually\n", - " )\n", - " vocab_size = len(tokenizer_0)\n", - "\n", - " # Get the token ID for \n", - " end_prompt_token_id = tokenizer_0.encode(\"\", add_special_tokens=False)[0]\n", - "\n", - " # Process each sample\n", - " for sample_tokens in tokenized_data['input_ids']:\n", - " # Find the index of token\n", - " try:\n", - " end_prompt_index = sample_tokens.index(end_prompt_token_id)\n", - " except ValueError:\n", - " # If not found, treat sample as a non-instruct sample\n", - " end_prompt_index = (\n", - " prompt_length - 1) # int(np.ceil(len(sample_tokens) * (1/3))) # 0 ## 1. Give it a fair starting place to predict the next word 2. reduce the number of expanded samples\n", - "\n", - " # Find first pad token after \n", - " first_pad_index = None\n", - " for i in range(end_prompt_index + 1, len(sample_tokens)):\n", - " if sample_tokens[i] == pad_token_id:\n", - " first_pad_index = i\n", - " break\n", - "\n", - " # If no pad token found, use the end of sequence\n", - " if first_pad_index is None:\n", - " first_pad_index = len(sample_tokens)\n", - "\n", - " # Apply sliding window from after to first pad token\n", - " # Start from end_prompt_index + 1 (first token to predict)\n", - " # End at first_pad_index - 1 (last token to predict)\n", - " for i in range(end_prompt_index + 1, first_pad_index):\n", - " # Input: from start up to (but not including) token i\n", - " input_ids = sample_tokens[:i]\n", - "\n", - " # Pad or truncate to max_seq_length\n", - " if len(input_ids) > max_seq_length:\n", - " input_ids = input_ids[:max_seq_length]\n", - " else:\n", - " input_ids = input_ids + [pad_token_id] * (max_seq_length - len(input_ids))\n", - "\n", - " # Label: one-hot encoding of token at position i\n", - " next_token = sample_tokens[i]\n", - " label = [0] * vocab_size\n", - " label[next_token] = 1\n", - "\n", - " all_input_ids.append(input_ids)\n", - " all_labels.append(label)\n", - "\n", - " # Add final sample with pad token as label to indicate termination\n", - " if first_pad_index < len(sample_tokens): # Only if there's actually a pad token\n", - " input_ids = sample_tokens[:first_pad_index]\n", - "\n", - " # Pad or truncate to max_seq_length\n", - " if len(input_ids) > max_seq_length:\n", - " input_ids = input_ids[:max_seq_length]\n", - " else:\n", - " input_ids = input_ids + [pad_token_id] * (max_seq_length - len(input_ids))\n", - "\n", - " # Label: one-hot encoding of pad token\n", - " label = [0] * vocab_size\n", - " label[pad_token_id] = 1\n", - "\n", - " all_input_ids.append(input_ids)\n", - " all_labels.append(label)\n", - "\n", - " return all_input_ids, all_labels, vocab_size\n", - "```\n" - ], - "metadata": { - "id": "8Tu8X9cVeQVD" - } - }, - { - "cell_type": "code", - "source": [ - "\n", - "# Preprocess data for Stage I-a training\n", - "x, y, vocab_size = prepare_data(data_0=non_instruct_samples,\n", - " tokenizer_0=tokenizer,\n", - " max_seq_length=MAX_SEQ_LENGTH,\n", - " prompt_length=PROMPT_LENGTH)\n", - "\n", - "# Split the preprocessed data for NAS training and validation\n", - "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.85, shuffle=False)\n", - "\n", - "# Package data into lists for the Cerebros AutoML component\n", - "x_train_tf = tf.constant(X_train, tf.int32)\n", - "y_train_tf = tf.constant(y_train, tf.float32)\n", - "x_train_packaged = [x_train_tf]\n", - "y_train_packaged = [y_train_tf]\n", - "\n", - "# Do the same for the validation data\n", - "x_test_tf = tf.constant(X_test, tf.int32)\n", - "y_test_tf = tf.constant(y_test, tf.float32)\n", - "x_test_packaged = [x_test_tf]\n", - "y_test_packaged = [y_test_tf]\n", - "\n", - "# Define input and output shapes for the AutoML model\n", - "INPUT_SHAPES = [(MAX_SEQ_LENGTH,)]\n", - "OUTPUT_SHAPES = [(VOCABULARY_SIZE)]\n" - ], - "metadata": { - "id": "EDyuTMLufYvs" - }, - "execution_count": 11, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# Train, Test Split of the Data for Stage I-b training\n", - "\n", - "We split the larger Phase I-b dataset into training and validation sets. Again, this dataset will be processed by a streaming generator in batches to avoid memory saturation and make the training more scalable. We will revisit that later." - ], - "metadata": { - "id": "zX60zcpykasl" - } - }, - { - "cell_type": "code", - "source": [ - "\n", - "# Split the phase I-b data set for training and validation\n", - "phase_i_b_train_samples, phase_i_b_val_samples = train_test_split(\n", - " phase_i_b_samples,\n", - " test_size=PHASE_I_B_VAL_SPLIT,\n", - " shuffle=False\n", - ")\n" - ], - "metadata": { - "id": "SMSdkFRPkg7D" - }, - "execution_count": 12, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "phase_i_b_train_samples[:3]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Oqw-T7bOo1GD", - "outputId": "2e8f24fc-24c2-4a06-babb-550b676b7751" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[\"God said, 'Let the earth produce vegetation, seed-bearing plants, and fruit trees, each according to its kind,' and it was so.\",\n", - " 'The earth brought forth grass, seed-bearing herbs, and fruit trees, each with its seed, and God saw that it was good.',\n", - " 'There was evening and morning, the third day.']" - ] - }, - "metadata": {}, - "execution_count": 13 - } - ] - }, - { - "cell_type": "code", - "source": [ - "X_train[:2]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Hv_52izIjOQ7", - "outputId": "e2972924-0190-4f16-9317-c00100486203" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[[644,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012],\n", - " [644,\n", - " 279,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012,\n", - " 128012]]" - ] - }, - "metadata": {}, - "execution_count": 14 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Base Text Embedding Model Definition\n", - "\n", - "- Before we run the NAS, we define a base model that handles token embeddings and positional embeddings.\n", - "- The SimpleCerebrosRandomSearch will then attach its auto-generated lattice of dense layers on top of this base model.\n", - "- The Cerebros NAS takes an init parameter base_models: List[tf.keras.Model]\n" - ], - "metadata": { - "id": "11Ri4PtKktih" - } - }, - { - "cell_type": "code", - "source": [ - "####### Text embedding base model #####################\n", - "\n", - "inp = tf.keras.layers.Input(shape=(MAX_SEQ_LENGTH,), dtype=tf.int32)\n", - "\n", - "# Token embedding layer\n", - "embedded = tf.keras.layers.Embedding(\n", - " input_dim=VOCABULARY_SIZE,\n", - " output_dim=EMBEDDING_DIM,\n", - " input_length=MAX_SEQ_LENGTH,\n", - " mask_zero=False\n", - ")(inp)\n", - "\n", - "# Interleaved Rotary Positional Embedding (iRoPE)\n", - "position_embedding = InterleavedRoPE(\n", - " dim=EMBEDDING_DIM,\n", - " max_seq_len=MAX_SEQ_LENGTH,\n", - ")(embedded)\n", - "\n", - "# Concatenate token and positional embeddings\n", - "x = tf.keras.layers.Concatenate()([embedded, position_embedding])\n", - "x = tf.keras.layers.Dropout(POSITIONAL_EMBEDDING_DROPOUT)(x)\n", - "\n", - "# Flatten and project to the desired dimension\n", - "flattened = tf.keras.layers.Flatten()(x)\n", - "projected = tf.keras.layers.Dense(EMBEDDING_DIM * PROJECTION_N)(flattened)\n", - "\n", - "# Create the base Keras model\n", - "cerebros_base_model = tf.keras.Model(\n", - " inputs=inp,\n", - " outputs=projected\n", - ")\n" - ], - "metadata": { - "id": "tn1qrGISn_Pe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "e76e091c-6e7f-4820-ef79-15143f1e6b64" - }, - "execution_count": 15, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.\n", - " warnings.warn(\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## FYI: The iRoPE Embedding:\n", - "\n", - "The RoPE embedding, and helper functions it depends on (previously imported from the local package cerebrosllmutils):\n", - "\n", - "- iRoPE: Interleaved Rotary Positional Embedding\n", - "- RoPE: Rotary Positional Embedding\n", - "- The Rotary Positional Embedding expresses positional relationships as angles, extends feasible context window.\n", - "- iRoPE: iRoPE applies the rotation in an interleaved manner and enables capturing more nuance and extending context windows feasible to around 2 million tokens.\n", - "\n", - "```python\n", - "# --- Base Rotary Positional Embedding\n", - "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='RotaryEmbedding')\n", - "class RotaryEmbedding(tf.keras.layers.Layer):\n", - " def __init__(self, dim, max_seq_len=1024, temperature=10000.0, **kwargs):\n", - " super().__init__(**kwargs)\n", - " self.dim = dim\n", - " # Ensure dim is even right at initialization\n", - " if self.dim % 2 != 0:\n", - " raise ValueError(f\"Embedding dimension `dim` ({self.dim}) must be even for RotaryEmbedding.\")\n", - " self.max_seq_len = max_seq_len\n", - " self.temperature = temperature\n", - " # *** No calculation or storage of inv_freq here or in build ***\n", - "\n", - " def build(self, input_shape):\n", - " # Build should primarily be for creating trainable weights, which we don't have.\n", - " # Call super().build() for Keras compatibility.\n", - " super().build(input_shape)\n", - "\n", - " def call(self, x): # Removed seq_len argument, calculate from x\n", - " shape = tf.shape(x)\n", - " batch_size = shape[0]\n", - " actual_seq_len = shape[1]\n", - "\n", - " # *** Calculate inv_freq inside call ***\n", - " inv_freq_base = tf.range(0, self.dim, 2, dtype=tf.float32)\n", - " inv_freq = 1.0 / (self.temperature ** (inv_freq_base / self.dim))\n", - " # Ensure inv_freq has the correct shape [dim/2]\n", - " inv_freq = tf.cast(inv_freq, dtype=x.dtype) # Match dtype early\n", - "\n", - " # Use actual_seq_len for calculations\n", - " position = tf.range(actual_seq_len, dtype=x.dtype) # Match dtype\n", - "\n", - " # Calculate sinusoid input using einsum or broadcasting\n", - " # Einsum approach: Ensure correct dimensions [seq_len, dim/2]\n", - " sinusoid_inp = tf.einsum(\"i,j->ij\", position, inv_freq)\n", - "\n", - " # Calculate sin and cos based on the actual sequence length\n", - " sin = tf.sin(sinusoid_inp)\n", - " cos = tf.cos(sinusoid_inp)\n", - "\n", - " # Repeat sin/cos for interleaving: [a, b] -> [a, a, b, b]\n", - " # Result needs shape [actual_seq_len, dim]\n", - " sin = tf.repeat(sin, 2, axis=-1)\n", - " cos = tf.repeat(cos, 2, axis=-1)\n", - "\n", - " # Expand dims for batch and tile\n", - " # Output shape needs to be [batch_size, actual_seq_len, dim]\n", - " # Add batch dimension: [1, actual_seq_len, dim]\n", - " sin = tf.expand_dims(sin, axis=0)\n", - " cos = tf.expand_dims(cos, axis=0)\n", - "\n", - " # Tile to match the batch size: [batch_size, actual_seq_len, dim]\n", - " sin = tf.tile(sin, [batch_size, 1, 1])\n", - " cos = tf.tile(cos, [batch_size, 1, 1])\n", - "\n", - " # Casting to x.dtype was already done for inv_freq, sin/cos will inherit\n", - " # sin = tf.cast(sin, x.dtype) # Already done via calculation chain\n", - " # cos = tf.cast(cos, x.dtype) # Already done via calculation chain\n", - "\n", - " # Return sin and cos needed by InterleavedRoPE\n", - " return sin, cos\n", - "\n", - " def get_config(self):\n", - " config = super().get_config()\n", - " config.update({\n", - " \"dim\": self.dim,\n", - " \"max_seq_len\": self.max_seq_len,\n", - " \"temperature\": self.temperature,\n", - " })\n", - " return config\n", - "\n", - " @classmethod\n", - " def from_config(cls, config):\n", - " return cls(**config)\n", - "\n", - "\n", - "# iRoPE helper functions\n", - "\n", - "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='split_alternate')\n", - "def split_alternate(x):\n", - " shape = tf.shape(x)\n", - " x = tf.reshape(x, [shape[0], shape[1], shape[2] // 2, 2])\n", - " x = tf.transpose(x, [0, 1, 3, 2])\n", - " x = tf.reshape(x, [shape[0], shape[1], -1])\n", - " return x\n", - "\n", - "\n", - "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='rotate_half')\n", - "def rotate_half(x):\n", - " x = split_alternate(x)\n", - " d = tf.shape(x)[-1]\n", - " rotated_x = tf.concat([-x[..., d // 2:], x[..., :d // 2]], axis=-1)\n", - " return tf.reshape(rotated_x, tf.shape(x))\n", - "\n", - "\n", - "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='apply_rotary_pos_emb')\n", - "def apply_rotary_pos_emb(x, sin, cos):\n", - " cos = tf.reshape(cos, [tf.shape(cos)[0], tf.shape(cos)[1], -1])\n", - " sin = tf.reshape(sin, [tf.shape(sin)[0], tf.shape(sin)[1], -1])\n", - " x_rotated = x * cos + rotate_half(x) * sin\n", - " return x_rotated\n", - "\n", - "\n", - "# interleaved Rotary Postional Embedding (iRoPE)\n", - "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='InterleavedRoPE')\n", - "class InterleavedRoPE(tf.keras.layers.Layer):\n", - " def __init__(self, dim, max_seq_len=1024, **kwargs):\n", - " super().__init__(**kwargs)\n", - " if dim % 2 != 0:\n", - " raise ValueError(f\"Embedding dimension `dim` ({dim}) must be even for InterleavedRoPE.\")\n", - " self.dim = dim\n", - " self.max_seq_len = max_seq_len\n", - " # Instantiate the RotaryEmbedding layer\n", - " # Ensure the name is consistent if needed for saving/loading\n", - " self.rotary_emb = RotaryEmbedding(dim, max_seq_len, name=\"rotary_embedding\")\n", - "\n", - " def call(self, x):\n", - " # Get sin and cos from the RotaryEmbedding layer's call method\n", - " # *** Pass only 'x'. RotaryEmbedding calculates seq_len internally. ***\n", - " sin, cos = self.rotary_emb(x)\n", - "\n", - " # Apply the positional embeddings\n", - " x_embedded = apply_rotary_pos_emb(x, sin, cos)\n", - " return x_embedded\n", - "\n", - " def get_config(self):\n", - " config = super().get_config()\n", - " config.update({\n", - " \"dim\": self.dim,\n", - " \"max_seq_len\": self.max_seq_len,\n", - " })\n", - " # Keras handles nested layer serialization automatically\n", - " return config\n", - "\n", - " @classmethod\n", - " def from_config(cls, config):\n", - " # Keras handles nested layer restoration automatically\n", - " return cls(**config)\n", - "```" - ], - "metadata": { - "id": "CXtYv20vpkMY" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Custom metric Perplexity (previously imported from the local package cerebrosllmutils):\n", - "\n", - "Since there is not a Perplexity metric in tensorflow.keras.metrics, we created our own, and one designed for this single - head model.\n", - "\n", - "## This is what it looks like:\n", - "\n", - "```python\n", - "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='Perplexity')\n", - "class Perplexity(tf.keras.metrics.Metric):\n", - " \"\"\"\n", - " Computes perplexity, defined as e^(categorical crossentropy).\n", - " \"\"\"\n", - "\n", - " def __init__(self, name='perplexity', **kwargs):\n", - " super().__init__(name=name, **kwargs)\n", - " self.total_crossentropy = self.add_weight(name='total_crossentropy', initializer='zeros')\n", - " self.count = self.add_weight(name='count', initializer='zeros')\n", - "\n", - " def update_state(self, y_true, y_pred, sample_weight=None):\n", - " # Calculate categorical crossentropy\n", - " crossentropy = tf.keras.losses.categorical_crossentropy(y_true, y_pred)\n", - "\n", - " # Update the running sum of crossentropy and the count of samples\n", - " self.total_crossentropy.assign_add(tf.reduce_sum(crossentropy))\n", - " self.count.assign_add(tf.cast(tf.shape(y_true)[0], dtype=tf.float32))\n", - "\n", - " def result(self):\n", - " # Compute the average crossentropy\n", - " average_crossentropy = self.total_crossentropy / self.count\n", - " # Compute perplexity as e^(average crossentropy)\n", - " return tf.exp(average_crossentropy)\n", - "\n", - " def reset_state(self):\n", - " # Reset the state variables\n", - " self.total_crossentropy.assign(0.0)\n", - " self.count.assign(0.0)\n", - "```\n" - ], - "metadata": { - "id": "uN3adqRLo61X" - } - }, - { - "cell_type": "code", - "source": [ - "# Custom metric: Perplexity\n", - "perplexity_metric = Perplexity()" - ], - "metadata": { - "id": "_8uTBW_to7iQ" - }, - "execution_count": 16, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "# Stage I-a training: Neural Architecture Search (NAS)\n", - "\n", - "We now run the SimpleCerebrosRandomSearch to find the best performing architecture based on the training data and the base model. The search aims to minimize the perplexity in the train set. The search aims to minimize the perplexity in the training set. Obviously, in a full - scale run, we would use the validation set's value.\n", - "\n", - "- The Cerebros NAS will parse a block composed of rows (Levels) of multiple Dense layers (Units) with an overlapping, interleaved, interwoven topology both laterally between Dense layers on the same row and vertically between layers on different levels.\n", - "- This topology emulates the neuroscience principle of modularity.\n", - "- This topology allows local clusters of densely connected neurons to learn specialized fragments of a problem, while allowing efficient communication between these clusters to coordinate among themselves to compose a solution to a complex problem.\n", - "\n", - "For the deep technical details of how Cerebros NAS works: [How Cerebros NAS Works](https://github.com/david-thrower/cerebros-core-algorithm-alpha/blob/277-attempt-to-imporve-parameters-on--dev-branch-275/documentation/cerebros-technical-details.md)\n", - "\n", - "## This is what a neural network parsed by Cerebros looks like:\n", - "\n", - "- Green triangles: Input layers\n", - "- Blue squares: Concatenate layer -> [BatchNormalization | Dropout]\n", - "- Pink ovals: Hidden Dense layers\n", - "- Red oval: Output Dense layer\n" - ], - "metadata": { - "id": "tWjbHiHRMhR4" - } - }, - { - "cell_type": "markdown", - "source": [ - "![Brain-lookalike1.png](data:image/png;base64,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)" - ], - "metadata": { - "id": "1wR8EVItNNh_" - } - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "## For a more readable view of that this looks like\n", - "\n", - "![image.png](data:image/png;base64,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)\n" - ], - "metadata": { - "id": "_bXR1QxaLPiq" - } - }, - { - "cell_type": "code", - "source": [ - "######## Instantiate Cerebros Neural Architecture Search #######\n", - "\n", - "# Project metadata\n", - "TIME = pendulum.now(tz='America/New_York').__str__()[:16].replace('T', '_').replace(':', '_').replace('-', '_')\n", - "PROJECT_NAME = f'{TIME}_cerebros_not-gpt'\n", - "meta_trial_number = 42\n", - "\n", - "# Initialize the AutoML search\n", - "cerebros_automl = SimpleCerebrosRandomSearch(\n", - " unit_type=DenseUnit,\n", - " input_shapes=INPUT_SHAPES,\n", - " output_shapes=OUTPUT_SHAPES,\n", - " training_data=x_train_packaged,\n", - " labels=y_train_packaged,\n", - " validation_split=0.2,\n", - " direction='minimize',\n", - " metric_to_rank_by=\"perplexity\",\n", - " minimum_levels=minimum_levels,\n", - " maximum_levels=maximum_levels,\n", - " minimum_units_per_level=minimum_units_per_level,\n", - " maximum_units_per_level=maximum_units_per_level,\n", - " minimum_neurons_per_unit=minimum_neurons_per_unit,\n", - " maximum_neurons_per_unit=maximum_neurons_per_unit,\n", - " activation=activation,\n", - " final_activation='softmax',\n", - " number_of_architecture_moities_to_try=moities_to_try,\n", - " number_of_tries_per_architecture_moity=tries_per_moity,\n", - " predecessor_level_connection_affinity_factor_first=predecessor_level_connection_affinity_factor_first,\n", - " predecessor_level_connection_affinity_factor_main=predecessor_level_connection_affinity_factor_main,\n", - " predecessor_level_connection_affinity_factor_decay_main=zero_7_exp_decay,\n", - " max_consecutive_lateral_connections=max_consecutive_lateral_connections,\n", - " p_lateral_connection=p_lateral_connection,\n", - " p_lateral_connection_decay=zero_95_exp_decay,\n", - " num_lateral_connection_tries_per_unit=num_lateral_connection_tries_per_unit,\n", - " learning_rate=learning_rate,\n", - " loss=tf.keras.losses.CategoricalCrossentropy(),\n", - " metrics=[tf.keras.metrics.CategoricalAccuracy(), perplexity_metric],\n", - " epochs=epochs,\n", - " project_name=f\"{PROJECT_NAME}_meta_{meta_trial_number}\",\n", - " model_graphs='model_graphs',\n", - " batch_size=batch_size,\n", - " gradient_accumulation_steps=gradient_accumulation_steps,\n", - " meta_trial_number=meta_trial_number,\n", - " base_models=[cerebros_base_model],\n", - " train_data_dtype=tf.int32\n", - ")" - ], - "metadata": { - "id": "XV2q_5WEwBJ0" - }, - "execution_count": 17, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# Run the Cerebros Neural Architecture Search\n" - ], - "metadata": { - "id": "TJVLfmJ2virA" - } - }, - { - "cell_type": "code", - "source": [ - "cerebros_t0 = time.time()\n", - "phase_i_a_result_0 = cerebros_automl.run_random_search()\n", - "cerebros_t1 = time.time()\n", - "\n", - "# Report results\n", - "cerebros_time_all_models_min = (cerebros_t1 - cerebros_t0) / 60\n", - "models_tried = moities_to_try * tries_per_moity\n", - "cerebros_time_per_model = cerebros_time_all_models_min / models_tried\n", - "phase_i_a_result = float(phase_i_a_result_0)\n", - "\n", - "print(f\"Cerebros trained {models_tried} models in {cerebros_time_all_models_min:.2f} min. Average time per model: {cerebros_time_per_model:.2f} min.\")\n", - "print(f'Cerebros best perplexity achieved in Phase I-a is {phase_i_a_result}')" - ], - "metadata": { - "id": "ulL0EGnow5L7", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "d56dd1ec-2f7b-4a3c-ecc6-75e595910367" - }, - "execution_count": 18, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "\rGlobal task progress: 0%|\u001b[38;2;22;206;235m \u001b[0m| 0/3 [00:00nnf>ceil\n", - "k is: 0 value is: [{'1': }]\n", - "0\n", - "k is: 1 value is: [{'2': }, {'2': }]\n", - "1\n", - "Trying to create level 1\n", - "We think level 1's predecessors are: [0]\n", - "k is: 2 value is: [{'128260': }]\n", - "2\n", - "Trying to create Final level 2\n", - "Trying to create level 2\n", - "We think level final level 2's predecessors are: [0, 1]\n", - "levels:\n", - "[0, 1, 2]\n", - "{'0': 'InputUnitModule'}\n", - "InputLevel.input_shapes [(40,)]\n", - "{'2': }\n", - "{'2': }\n", - "Debug: I am 2 selecting 1\n", - "debug: meta_level_number\n", - "debug: meta_level_number\n", - "debug: meta_level_number\n", - "Setting levels_unmaterialized[0] level_number 0 to have first successor: levels_unmaterialized[:1], having level_numbers of [1, 2]\n", - "Setting levels_unmaterialized[1] level_number 1 to have first successor: levels_unmaterialized[:2], having level_numbers of [2]\n", - "Debug: successor_connectivity_errors_2d []\n", - "$$$$$$>>>>> Base model: \n", - "InputUnit.input_shape: (40,)\n", - "{'2': }\n", - "{'2': }\n", - "debug: meta_level_number\n", - "debug: meta_level_number\n", - "Debug: successor_connectivity_errors_2d []\n", - "Debug: successor_connectivity_errors_2d []\n", - "materialize:_NeuralNetworkFuture_0000000000000nan_tr_0_DenseLevel_0000000000000001_tr_0_DenseUnit_0000000000000001_tr_0_0 called\n", - "materialized network layers\n", - "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "materialize:_NeuralNetworkFuture_0000000000000nan_tr_0_DenseLevel_0000000000000001_tr_0_DenseUnit_0000000000000001_tr_0_1 called\n", - "materialized network layers\n", - "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "{'128260': }\n", - "debug: meta_level_number\n", - "Debug: successor_connectivity_errors_2d []\n", - "materialize:_NeuralNetworkFuture_0000000000000nan_tr_0_FinalDenseLevel_0000000000000002_tr_0_FinalDenseUnit_0000000000000002_tr_0_0 called\n", - "materialized network layers\n", - "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "inputs\n", - "\n", - "\n", - "outputs\n", - "\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "\u001b[1mModel: \"NeuralNetworkFuture_0000000000000nan_tr_0_nn_materialized\"\u001b[0m\n" - ], - "text/html": [ - "
Model: \"NeuralNetworkFuture_0000000000000nan_tr_0_nn_materialized\"\n",
-              "
\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", - "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", - "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ - โ”‚\n", - "โ”‚ (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ functional โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m) โ”‚ \u001b[38;5;34m1,550,652\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mFunctional\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m1,584\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128260\u001b[0m) โ”‚ \u001b[38;5;34m50,919,220\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" - ], - "text/html": [ - "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
-              "โ”ƒ Layer (type)        โ”ƒ Output Shape      โ”ƒ    Param # โ”ƒ Connected to      โ”ƒ\n",
-              "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 40)        โ”‚          0 โ”‚ -                 โ”‚\n",
-              "โ”‚ (InputLayer)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ functional          โ”‚ (None, 12)        โ”‚  1,550,652 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Functional)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0]  โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0]  โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚          0 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚      1,584 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 128260)    โ”‚ 50,919,220 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
-              "
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categorical_accuracy: 0.0839 - loss: 5.3843 - perplexity: 352.6757 - val_categorical_accuracy: 0.1667 - val_loss: 12.7511 - val_perplexity: 344943.8125\n", - "Epoch 27/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 516ms/step - categorical_accuracy: 0.2120 - loss: 5.6307 - perplexity: 300.5551 - val_categorical_accuracy: 0.1667 - val_loss: 12.8756 - val_perplexity: 390664.9688\n", - "Epoch 28/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 614ms/step - categorical_accuracy: 0.1685 - loss: 4.4140 - perplexity: 99.5634 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.1954 - val_perplexity: 537862.5000\n", - "Epoch 29/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 517ms/step - categorical_accuracy: 0.0568 - loss: 5.8209 - perplexity: 412.2969 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.3590 - val_perplexity: 633498.9375\n", - "Epoch 30/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 748ms/step - categorical_accuracy: 0.1864 - loss: 4.9144 - perplexity: 157.2443 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.5253 - val_perplexity: 748103.1875\n", - "Epoch 31/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 507ms/step - categorical_accuracy: 0.1052 - loss: 8.2503 - perplexity: 22384.6094 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.6228 - val_perplexity: 824754.3750\n", - "Epoch 32/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 612ms/step - categorical_accuracy: 0.4431 - loss: 4.0581 - perplexity: 75.1973 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.0377 - val_perplexity: 1248790.8750\n", - "Epoch 33/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 766ms/step - categorical_accuracy: 0.2086 - loss: 5.6123 - perplexity: 301.7467 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.2131 - val_perplexity: 1488169.6250\n", - "Epoch 34/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 569ms/step - categorical_accuracy: 0.2919 - loss: 4.4319 - perplexity: 154.5172 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.2928 - val_perplexity: 1611684.3750\n", - "Epoch 35/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 585ms/step - categorical_accuracy: 0.1802 - loss: 5.1381 - perplexity: 190.7273 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.4868 - val_perplexity: 1956789.0000\n", - "Epoch 36/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 639ms/step - categorical_accuracy: 0.1719 - loss: 4.6314 - perplexity: 111.0518 - val_categorical_accuracy: 0.1667 - val_loss: 14.5656 - val_perplexity: 2117109.5000\n", - "Epoch 37/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 693ms/step - categorical_accuracy: 0.0839 - loss: 6.8925 - perplexity: 1205.9113 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.6420 - val_perplexity: 2285232.2500\n", - "Epoch 38/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 594ms/step - categorical_accuracy: 0.2530 - loss: 5.8083 - perplexity: 927.8478 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.6140 - val_perplexity: 2222210.0000\n", - "Epoch 39/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 557ms/step - categorical_accuracy: 0.1913 - loss: 4.0802 - perplexity: 62.5591 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.5886 - val_perplexity: 2166540.2500\n", - "Epoch 40/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 757ms/step - categorical_accuracy: 0.2987 - loss: 4.2323 - perplexity: 90.9604 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.6538 - val_perplexity: 2312421.2500\n", - "Epoch 41/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 529ms/step - categorical_accuracy: 0.2453 - loss: 3.7488 - perplexity: 50.1163 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.6478 - val_perplexity: 2298534.5000\n", - "this is neural_network_spec_file 2025_11_23 16_55_cerebros_not-gpt_meta_42/model_architectures/tr_0000000000000000_subtrial_0000000000000000.txt\n", - "returning trial 0 oracles\n", - " categorical_accuracy loss perplexity val_categorical_accuracy \\\n", - "0 0.000000 11.769061 129192.796875 0.000000 \n", - "1 0.000000 11.635833 113077.960938 0.000000 \n", - "2 0.130435 11.652204 114944.367188 0.000000 \n", - "3 0.130435 11.464634 95285.593750 0.000000 \n", - "4 0.000000 11.768666 129141.796875 0.000000 \n", - "5 0.130435 10.994949 59572.500000 0.000000 \n", - "6 0.043478 11.276978 78982.257812 0.000000 \n", - "7 0.000000 11.120511 67542.414062 0.000000 \n", - "8 0.173913 10.726726 45557.273438 0.000000 \n", - "9 0.217391 10.059676 23380.933594 0.166667 \n", - "10 0.173913 10.355123 31417.570312 0.000000 \n", - "11 0.000000 10.472779 35340.300781 0.000000 \n", - "12 0.086957 10.171259 26140.964844 0.000000 \n", - "13 0.217391 9.254299 10449.392578 0.000000 \n", - "14 0.217391 8.896774 7308.360840 0.000000 \n", - "15 0.130435 9.018457 8254.035156 0.000000 \n", - "16 0.130435 8.039083 3099.770996 0.000000 \n", - "17 0.217391 7.848331 2561.456787 0.000000 \n", - "18 0.173913 7.948806 2832.192139 0.000000 \n", - "19 0.043478 7.698378 2204.769043 0.000000 \n", - "20 0.173913 7.669386 2141.766846 0.000000 \n", - "21 0.260870 6.773150 874.061218 0.166667 \n", - "22 0.217391 7.382279 1607.248413 0.166667 \n", - "23 0.130435 6.034015 417.387543 0.166667 \n", - "24 0.130435 6.000526 403.641022 0.166667 \n", - "25 0.043478 6.586512 725.246826 0.166667 \n", - "26 0.260870 5.741646 311.576935 0.166667 \n", - "27 0.130435 5.138083 170.388733 0.000000 \n", - "28 0.086957 5.670679 290.231415 0.000000 \n", - "29 0.217391 5.602477 271.096985 0.000000 \n", - "30 0.173913 6.986033 1081.422852 0.000000 \n", - "31 0.304348 4.127844 62.044033 0.000000 \n", - "32 0.217391 5.934126 377.709869 0.000000 \n", - "33 0.217391 5.564253 260.930054 0.000000 \n", - "34 0.173913 5.642823 282.258331 0.000000 \n", - "35 0.173913 4.475579 87.845474 0.166667 \n", - "36 0.043478 6.194771 490.179321 0.000000 \n", - "37 0.217391 5.472395 238.029572 0.000000 \n", - "38 0.173913 4.001881 54.700928 0.000000 \n", - "39 0.304348 3.707729 40.761116 0.000000 \n", - "40 0.260870 4.130568 62.213223 0.000000 \n", - "\n", - " val_loss val_perplexity trial_number subtrial_number \\\n", - "0 11.755738 1.274830e+05 0 0 \n", - "1 11.755464 1.274480e+05 0 0 \n", - "2 11.755542 1.274580e+05 0 0 \n", - "3 11.739563 1.254375e+05 0 0 \n", - "4 11.729648 1.241999e+05 0 0 \n", - "5 11.724008 1.235014e+05 0 0 \n", - "6 11.714873 1.223784e+05 0 0 \n", - "7 11.718637 1.228398e+05 0 0 \n", - "8 11.724952 1.236180e+05 0 0 \n", - "9 11.730713 1.243322e+05 0 0 \n", - "10 11.739503 1.254300e+05 0 0 \n", - "11 11.750234 1.267832e+05 0 0 \n", - "12 11.752646 1.270895e+05 0 0 \n", - "13 11.755273 1.274237e+05 0 0 \n", - "14 11.762258 1.283168e+05 0 0 \n", - "15 11.835355 1.380477e+05 0 0 \n", - "16 11.884326 1.449764e+05 0 0 \n", - "17 11.947881 1.544894e+05 0 0 \n", - "18 12.035375 1.686152e+05 0 0 \n", - "19 12.174404 1.937655e+05 0 0 \n", - "20 12.258733 2.108143e+05 0 0 \n", - "21 12.340481 2.287719e+05 0 0 \n", - "22 12.413980 2.462197e+05 0 0 \n", - "23 12.586273 2.925156e+05 0 0 \n", - "24 12.676117 3.200130e+05 0 0 \n", - "25 12.751137 3.449438e+05 0 0 \n", - "26 12.875606 3.906650e+05 0 0 \n", - "27 13.195358 5.378625e+05 0 0 \n", - "28 13.359014 6.334989e+05 0 0 \n", - "29 13.525296 7.481032e+05 0 0 \n", - "30 13.622841 8.247544e+05 0 0 \n", - "31 14.037686 1.248791e+06 0 0 \n", - "32 14.213058 1.488170e+06 0 0 \n", - "33 14.292789 1.611684e+06 0 0 \n", - "34 14.486815 1.956789e+06 0 0 \n", - "35 14.565562 2.117110e+06 0 0 \n", - "36 14.641978 2.285232e+06 0 0 \n", - "37 14.614013 2.222210e+06 0 0 \n", - "38 14.588642 2.166540e+06 0 0 \n", - "39 14.653806 2.312421e+06 0 0 \n", - "40 14.647781 2.298534e+06 0 0 \n", - "\n", - " model_name \n", - 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"34 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - "35 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - "36 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - "37 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - "38 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - "39 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - "40 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", - " self.pid = os.fork()\n", - "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", - " self.pid = os.fork()\n", - "Global task progress: 33%|\u001b[38;2;22;206;235mโ–ˆโ–ˆโ–ˆโ–Ž \u001b[0m| 1/3 [03:54<07:49, 234.85s/it]" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "SimpleCerebrosRandomSearch.input_shapes: [(40,)]\n", - "nan\n", - ">nnf>ceil\n", - "k is: 0 value is: [{'1': }]\n", - "0\n", - "k is: 1 value is: [{'2': }, {'2': }]\n", - "1\n", - "Trying to create level 1\n", - "We think level 1's predecessors are: [0]\n", - "k is: 2 value is: [{'128260': }]\n", - "2\n", - "Trying to create Final level 2\n", - "Trying to create level 2\n", - "We think level final level 2's predecessors are: [0, 1]\n", - "levels:\n", - "[0, 1, 2]\n", - "{'0': 'InputUnitModule'}\n", - "InputLevel.input_shapes [(40,)]\n", - "{'2': }\n", - "{'2': }\n", - "Debug: I am 2 selecting 1\n", - "debug: meta_level_number\n", - "debug: meta_level_number\n", - "debug: meta_level_number\n", - "Setting levels_unmaterialized[0] level_number 0 to have first successor: levels_unmaterialized[:1], having level_numbers of [1, 2]\n", - "Setting levels_unmaterialized[1] level_number 1 to have first successor: levels_unmaterialized[:2], having level_numbers of [2]\n", - "Debug: successor_connectivity_errors_2d []\n", - "$$$$$$>>>>> Base model: \n", - "InputUnit.input_shape: (40,)\n", - "{'2': }\n", - "{'2': }\n", - "debug: meta_level_number\n", - "debug: meta_level_number\n", - "Debug: successor_connectivity_errors_2d []\n", - "Debug: successor_connectivity_errors_2d []\n", - "materialize:_NeuralNetworkFuture_0000000000000nan_tr_1_DenseLevel_0000000000000001_tr_1_DenseUnit_0000000000000001_tr_1_0 called\n", - "materialized network layers\n", - "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "materialize:_NeuralNetworkFuture_0000000000000nan_tr_1_DenseLevel_0000000000000001_tr_1_DenseUnit_0000000000000001_tr_1_1 called\n", - "materialized network layers\n", - "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "{'128260': }\n", - "debug: meta_level_number\n", - "Debug: successor_connectivity_errors_2d []\n", - "materialize:_NeuralNetworkFuture_0000000000000nan_tr_1_FinalDenseLevel_0000000000000002_tr_1_FinalDenseUnit_0000000000000002_tr_1_0 called\n", - "materialized network layers\n", - "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "inputs\n", - "\n", - "\n", - "outputs\n", - "\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "\u001b[1mModel: \"NeuralNetworkFuture_0000000000000nan_tr_1_nn_materialized\"\u001b[0m\n" - ], - "text/html": [ - "
Model: \"NeuralNetworkFuture_0000000000000nan_tr_1_nn_materialized\"\n",
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\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", - "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", - "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ - โ”‚\n", - "โ”‚ (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ functional โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m) โ”‚ \u001b[38;5;34m1,550,652\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mFunctional\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - 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โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
-              "โ”ƒ Layer (type)        โ”ƒ Output Shape      โ”ƒ    Param # โ”ƒ Connected to      โ”ƒ\n",
-              "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 40)        โ”‚          0 โ”‚ -                 โ”‚\n",
-              "โ”‚ (InputLayer)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ functional          โ”‚ (None, 12)        โ”‚  1,550,652 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Functional)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0]  โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0]  โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚          0 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚      1,584 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 128260)    โ”‚ 50,919,220 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
-              "
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categorical_accuracy: 0.1913 - loss: 11.2528 - perplexity: 77375.8594 - val_categorical_accuracy: 0.1667 - val_loss: 11.7502 - val_perplexity: 126778.7031\n", - "Epoch 3/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 518ms/step - categorical_accuracy: 0.2191 - loss: 10.8135 - perplexity: 50491.5156 - val_categorical_accuracy: 0.1667 - val_loss: 11.7425 - val_perplexity: 125805.1797\n", - "Epoch 4/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 522ms/step - categorical_accuracy: 0.1864 - loss: 10.2940 - perplexity: 30868.9629 - val_categorical_accuracy: 0.0000e+00 - val_loss: 11.7451 - val_perplexity: 126128.5781\n", - "Epoch 5/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 820ms/step - categorical_accuracy: 0.1913 - loss: 9.7216 - perplexity: 16997.6719 - val_categorical_accuracy: 0.0000e+00 - val_loss: 11.7362 - val_perplexity: 125020.5859\n", - "Epoch 6/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 645ms/step - categorical_accuracy: 0.1407 - loss: 8.9741 - perplexity: 8181.5312 - val_categorical_accuracy: 0.0000e+00 - val_loss: 11.7171 - val_perplexity: 122652.5234\n", - "Epoch 7/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 593ms/step - categorical_accuracy: 0.1830 - loss: 8.4567 - perplexity: 4759.8066 - val_categorical_accuracy: 0.0000e+00 - val_loss: 11.6908 - val_perplexity: 119465.5703\n", - "Epoch 8/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 685ms/step - 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categorical_accuracy: 0.2669 - loss: 6.6371 - perplexity: 870.7467 - val_categorical_accuracy: 0.0000e+00 - val_loss: 11.6597 - val_perplexity: 115809.4375\n", - "Epoch 12/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 653ms/step - categorical_accuracy: 0.1719 - loss: 5.9232 - perplexity: 380.9991 - val_categorical_accuracy: 0.0000e+00 - val_loss: 11.7503 - val_perplexity: 126796.4766\n", - "Epoch 13/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 626ms/step - categorical_accuracy: 0.0839 - loss: 7.4954 - perplexity: 2688.5974 - val_categorical_accuracy: 0.1667 - val_loss: 11.8025 - val_perplexity: 133587.0156\n", - "Epoch 14/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 555ms/step - 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categorical_accuracy: 0.2364 - loss: 6.1440 - perplexity: 556.4460 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.1211 - val_perplexity: 499357.5000\n", - "Epoch 21/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 566ms/step - categorical_accuracy: 0.2752 - loss: 4.0937 - perplexity: 103.8000 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.2722 - val_perplexity: 580840.3125\n", - "Epoch 22/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 584ms/step - categorical_accuracy: 0.3582 - loss: 3.5086 - perplexity: 42.0227 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.3929 - val_perplexity: 655350.3750\n", - "Epoch 23/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 574ms/step - categorical_accuracy: 0.2357 - loss: 3.6651 - perplexity: 42.0124 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.5131 - val_perplexity: 739037.1875\n", - "Epoch 24/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 555ms/step - categorical_accuracy: 0.3743 - loss: 4.2759 - perplexity: 78.8337 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.6073 - val_perplexity: 812073.0625\n", - "Epoch 25/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 619ms/step - categorical_accuracy: 0.2814 - loss: 6.1106 - perplexity: 702.3881 - val_categorical_accuracy: 0.1667 - val_loss: 13.6209 - val_perplexity: 823172.5625\n", - "Epoch 26/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 689ms/step - categorical_accuracy: 0.2647 - loss: 6.2123 - perplexity: 835.9423 - val_categorical_accuracy: 0.1667 - val_loss: 13.5922 - val_perplexity: 799900.9375\n", - "Epoch 27/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 824ms/step - categorical_accuracy: 0.3014 - loss: 3.9091 - perplexity: 57.1766 - val_categorical_accuracy: 0.1667 - val_loss: 13.5968 - val_perplexity: 803528.0625\n", - "Epoch 28/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 594ms/step - categorical_accuracy: 0.2864 - loss: 5.1544 - perplexity: 186.2288 - val_categorical_accuracy: 0.1667 - val_loss: 13.5879 - val_perplexity: 796426.8750\n", - "Epoch 29/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 973ms/step - categorical_accuracy: 0.2314 - loss: 5.0346 - perplexity: 261.9535 - val_categorical_accuracy: 0.1667 - val_loss: 13.5785 - val_perplexity: 788957.9375\n", - "Epoch 30/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 1s/step - categorical_accuracy: 0.3508 - loss: 3.9460 - perplexity: 55.9352 - val_categorical_accuracy: 0.1667 - val_loss: 13.5878 - val_perplexity: 796315.2500\n", - "Epoch 31/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 684ms/step - categorical_accuracy: 0.2141 - loss: 3.3061 - perplexity: 29.5618 - val_categorical_accuracy: 0.1667 - val_loss: 13.5959 - val_perplexity: 802850.9375\n", - "Epoch 32/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 659ms/step - categorical_accuracy: 0.1719 - loss: 4.1759 - 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perplexity: 96.5757 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.3218 - val_perplexity: 1659098.5000\n", - "Epoch 39/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 606ms/step - categorical_accuracy: 0.3638 - loss: 3.2328 - perplexity: 26.5526 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.3728 - val_perplexity: 1745870.1250\n", - "Epoch 40/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 745ms/step - categorical_accuracy: 0.5727 - loss: 1.9158 - perplexity: 9.5471 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.5209 - val_perplexity: 2024707.8750\n", - "Epoch 41/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 671ms/step - categorical_accuracy: 0.1068 - loss: 5.0107 - perplexity: 172.7614 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.5204 - val_perplexity: 2023570.8750\n", - "this is neural_network_spec_file 2025_11_23 16_55_cerebros_not-gpt_meta_42/model_architectures/tr_0000000000000001_subtrial_0000000000000000.txt\n", - "returning trial 1 oracles\n", - " categorical_accuracy loss perplexity val_categorical_accuracy \\\n", - "0 0.000000 11.719700 225372.531250 0.166667 \n", - "1 0.173913 11.155995 69982.140625 0.166667 \n", - "2 0.173913 10.995764 59621.039062 0.166667 \n", - "3 0.217391 10.042144 22974.583984 0.000000 \n", - "4 0.173913 9.805058 18125.181641 0.000000 \n", - "5 0.130435 9.198784 9885.100586 0.000000 \n", - "6 0.173913 8.641828 5663.671387 0.000000 \n", - "7 0.043478 8.808529 6691.075195 0.000000 \n", - "8 0.217391 7.256882 1417.828491 0.000000 \n", - "9 0.173913 6.904544 996.794250 0.000000 \n", - "10 0.217391 6.873430 966.256958 0.000000 \n", - "11 0.173913 5.982946 396.607025 0.000000 \n", - "12 0.043478 6.824471 920.089539 0.166667 \n", - "13 0.130435 6.259269 522.836731 0.166667 \n", - "14 0.217391 5.205779 182.322769 0.166667 \n", - "15 0.130435 5.462027 235.574463 0.000000 \n", - "16 0.217391 6.074162 434.485474 0.000000 \n", - "17 0.217391 5.354462 211.550262 0.000000 \n", - "18 0.304348 4.318021 75.040001 0.000000 \n", - "19 0.217391 5.875260 356.117035 0.000000 \n", - "20 0.217391 5.246053 189.815536 0.000000 \n", - "21 0.391304 4.035575 56.575462 0.000000 \n", - "22 0.173913 3.672752 39.360092 0.000000 \n", - "23 0.347826 4.800797 121.607239 0.000000 \n", - "24 0.260870 6.058529 427.745911 0.166667 \n", - "25 0.260870 6.874752 967.535400 0.166667 \n", - "26 0.304348 3.871903 48.033691 0.166667 \n", - "27 0.217391 5.597022 269.622284 0.166667 \n", - "28 0.260870 4.006342 54.945507 0.166667 \n", - "29 0.260870 4.286894 72.740173 0.166667 \n", - "30 0.217391 3.180355 24.055300 0.166667 \n", - "31 0.173913 4.073040 58.735218 0.000000 \n", - "32 0.173913 5.302594 200.857193 0.000000 \n", - "33 0.217391 3.763384 43.094006 0.000000 \n", - "34 0.217391 4.363249 78.511826 0.000000 \n", - "35 0.217391 5.450110 232.783875 0.000000 \n", - "36 0.260870 3.634080 37.866989 0.000000 \n", - "37 0.391304 5.082735 161.214310 0.000000 \n", - "38 0.391304 3.312840 27.463017 0.000000 \n", - "39 0.434783 2.846823 17.232950 0.000000 \n", - "40 0.086957 5.169964 175.908478 0.000000 \n", - "\n", - " val_loss val_perplexity trial_number subtrial_number \\\n", - "0 11.768844 1.291648e+05 1 0 \n", - "1 11.750198 1.267787e+05 1 0 \n", - "2 11.742490 1.258052e+05 1 0 \n", - "3 11.745057 1.261286e+05 1 0 \n", - "4 11.736234 1.250206e+05 1 0 \n", - "5 11.717111 1.226525e+05 1 0 \n", - "6 11.690784 1.194656e+05 1 0 \n", - "7 11.644112 1.140180e+05 1 0 \n", - "8 11.626712 1.120513e+05 1 0 \n", - "9 11.637473 1.132634e+05 1 0 \n", - "10 11.659701 1.158094e+05 1 0 \n", - "11 11.750339 1.267965e+05 1 0 \n", - "12 11.802508 1.335870e+05 1 0 \n", - "13 11.897525 1.469026e+05 1 0 \n", - "14 11.997716 1.623835e+05 1 0 \n", - "15 12.270196 2.132448e+05 1 0 \n", - "16 12.433421 2.510535e+05 1 0 \n", - "17 12.588585 2.931926e+05 1 0 \n", - "18 12.766930 3.504347e+05 1 0 \n", - "19 13.121078 4.993575e+05 1 0 \n", - "20 13.272231 5.808403e+05 1 0 \n", - "21 13.392925 6.553504e+05 1 0 \n", - "22 13.513103 7.390372e+05 1 0 \n", - "23 13.607346 8.120731e+05 1 0 \n", - "24 13.620921 8.231726e+05 1 0 \n", - "25 13.592243 7.999009e+05 1 0 \n", - "26 13.596767 8.035281e+05 1 0 \n", - "27 13.587891 7.964269e+05 1 0 \n", - "28 13.578468 7.889579e+05 1 0 \n", - "29 13.587750 7.963152e+05 1 0 \n", - "30 13.595924 8.028509e+05 1 0 \n", - "31 13.705730 8.960311e+05 1 0 \n", - "32 13.788544 9.733935e+05 1 0 \n", - "33 13.923733 1.114295e+06 1 0 \n", - "34 14.089040 1.314598e+06 1 0 \n", - "35 14.174176 1.431418e+06 1 0 \n", - "36 14.239779 1.528470e+06 1 0 \n", - "37 14.321785 1.659098e+06 1 0 \n", - "38 14.372764 1.745870e+06 1 0 \n", - "39 14.520935 2.024708e+06 1 0 \n", - "40 14.520374 2.023571e+06 1 0 \n", - "\n", - " model_name \n", - "0 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - 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"35 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - "36 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - "37 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - "38 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - "39 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - "40 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", - " self.pid = os.fork()\n", - "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", - " self.pid = os.fork()\n", - "Global task progress: 67%|\u001b[38;2;22;206;235mโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹ \u001b[0m| 2/3 [07:42<03:50, 230.58s/it]" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "SimpleCerebrosRandomSearch.input_shapes: [(40,)]\n", - "nan\n", - ">nnf>ceil\n", - "k is: 0 value is: [{'1': }]\n", - "0\n", - "k is: 1 value is: [{'2': }, {'2': }]\n", - "1\n", - "Trying to create level 1\n", - "We think level 1's predecessors are: [0]\n", - "k is: 2 value is: [{'128260': }]\n", - "2\n", - "Trying to create Final level 2\n", - "Trying to create level 2\n", - "We think level final level 2's predecessors are: [0, 1]\n", - "levels:\n", - "[0, 1, 2]\n", - "{'0': 'InputUnitModule'}\n", - "InputLevel.input_shapes [(40,)]\n", - "{'2': }\n", - "{'2': }\n", - "Debug: I am 2 selecting 1\n", - "debug: meta_level_number\n", - "debug: meta_level_number\n", - "debug: meta_level_number\n", - "Setting levels_unmaterialized[0] level_number 0 to have first successor: levels_unmaterialized[:1], having level_numbers of [1, 2]\n", - "Setting levels_unmaterialized[1] level_number 1 to have first successor: levels_unmaterialized[:2], having level_numbers of [2]\n", - "Debug: successor_connectivity_errors_2d []\n", - "$$$$$$>>>>> Base model: \n", - "InputUnit.input_shape: (40,)\n", - "{'2': }\n", - "{'2': }\n", - "debug: meta_level_number\n", - "debug: meta_level_number\n", - "Debug: successor_connectivity_errors_2d []\n", - "Debug: successor_connectivity_errors_2d []\n", - "materialize:_NeuralNetworkFuture_0000000000000nan_tr_2_DenseLevel_0000000000000001_tr_2_DenseUnit_0000000000000001_tr_2_0 called\n", - "materialized network layers\n", - "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "materialize:_NeuralNetworkFuture_0000000000000nan_tr_2_DenseLevel_0000000000000001_tr_2_DenseUnit_0000000000000001_tr_2_1 called\n", - "materialized network layers\n", - "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", - 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\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", - "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", - "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ - โ”‚\n", - "โ”‚ (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ functional โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m) โ”‚ \u001b[38;5;34m1,550,652\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mFunctional\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m1,584\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", - "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128260\u001b[0m) โ”‚ \u001b[38;5;34m50,919,220\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", - "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", - "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" - ], - "text/html": [ - "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
-              "โ”ƒ Layer (type)        โ”ƒ Output Shape      โ”ƒ    Param # โ”ƒ Connected to      โ”ƒ\n",
-              "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 40)        โ”‚          0 โ”‚ -                 โ”‚\n",
-              "โ”‚ (InputLayer)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ functional          โ”‚ (None, 12)        โ”‚  1,550,652 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Functional)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0]  โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0]  โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚          0 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚      1,584 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
-              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 128260)    โ”‚ 50,919,220 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
-              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
-              "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
-              "
\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m52,476,644\u001b[0m (200.18 MB)\n" - ], - "text/html": [ - "
 Total params: 52,476,644 (200.18 MB)\n",
-              "
\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m52,474,124\u001b[0m (200.17 MB)\n" - ], - "text/html": [ - "
 Trainable params: 52,474,124 (200.17 MB)\n",
-              "
\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m2,520\u001b[0m (9.84 KB)\n" - ], - "text/html": [ - "
 Non-trainable params: 2,520 (9.84 KB)\n",
-              "
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perplexity: 27.8907 - val_categorical_accuracy: 0.1667 - val_loss: 14.7196 - val_perplexity: 2469625.0000\n", - "Epoch 30/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 660ms/step - categorical_accuracy: 0.2752 - loss: 5.4753 - perplexity: 249.7908 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.8572 - val_perplexity: 2834024.2500\n", - "Epoch 31/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 748ms/step - categorical_accuracy: 0.2925 - loss: 5.2035 - perplexity: 302.8727 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.9761 - val_perplexity: 3191841.5000\n", - "Epoch 32/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 585ms/step - categorical_accuracy: 0.2715 - loss: 3.0830 - perplexity: 22.1130 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.0934 - val_perplexity: 3589043.2500\n", - "Epoch 33/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 837ms/step - categorical_accuracy: 0.3638 - loss: 2.0138 - perplexity: 7.6831 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.1927 - val_perplexity: 3963894.5000\n", - "Epoch 34/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 622ms/step - categorical_accuracy: 0.4165 - loss: 2.3430 - perplexity: 12.4422 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.2933 - val_perplexity: 4383348.5000\n", - "Epoch 35/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 730ms/step - categorical_accuracy: 0.4832 - loss: 3.8156 - perplexity: 57.3130 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4055 - val_perplexity: 4903895.5000\n", - "Epoch 36/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 831ms/step - categorical_accuracy: 0.1641 - loss: 4.5182 - perplexity: 317.0210 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4245 - val_perplexity: 4998003.5000\n", - "Epoch 37/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 621ms/step - categorical_accuracy: 0.2752 - loss: 2.9753 - perplexity: 25.5228 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4590 - val_perplexity: 5173094.0000\n", - "Epoch 38/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 666ms/step - categorical_accuracy: 0.2280 - loss: 2.4058 - perplexity: 11.6680 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4232 - val_perplexity: 4991435.0000\n", - "Epoch 39/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 565ms/step - categorical_accuracy: 0.3592 - loss: 3.6356 - perplexity: 40.6227 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4089 - val_perplexity: 4920268.0000\n", - "Epoch 40/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 665ms/step - categorical_accuracy: 0.2691 - loss: 3.4659 - perplexity: 47.4784 - val_categorical_accuracy: 0.1667 - val_loss: 15.3797 - val_perplexity: 4778703.0000\n", - "Epoch 41/41\n", - "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 693ms/step - categorical_accuracy: 0.4310 - loss: 2.7924 - perplexity: 20.8595 - val_categorical_accuracy: 0.1667 - val_loss: 15.4324 - val_perplexity: 5037467.5000\n", - "this is neural_network_spec_file 2025_11_23 16_55_cerebros_not-gpt_meta_42/model_architectures/tr_0000000000000002_subtrial_0000000000000000.txt\n", - "returning trial 2 oracles\n", - " categorical_accuracy loss perplexity val_categorical_accuracy \\\n", - "0 0.000000 11.761698 226943.984375 0.000000 \n", - "1 0.260870 11.090140 65521.941406 0.166667 \n", - "2 0.086957 10.702163 44451.890625 0.166667 \n", - "3 0.173913 9.990288 21813.576172 0.166667 \n", - "4 0.347826 9.246581 10369.053711 0.166667 \n", - "5 0.260870 8.266317 3890.594971 0.166667 \n", - "6 0.347826 7.704062 2217.337646 0.166667 \n", - "7 0.304348 7.122604 1239.675415 0.166667 \n", - "8 0.347826 6.225540 505.495789 0.166667 \n", - "9 0.086957 6.562615 708.120972 0.166667 \n", - "10 0.260870 5.511858 247.610764 0.166667 \n", - "11 0.217391 5.295715 199.480270 0.166667 \n", - "12 0.130435 6.463620 641.378784 0.000000 \n", - "13 0.260870 5.026217 152.355484 0.000000 \n", - "14 0.217391 5.910099 368.742676 0.000000 \n", - "15 0.217391 4.887461 132.616455 0.000000 \n", - "16 0.434783 3.347833 28.441027 0.000000 \n", - "17 0.347826 4.313054 74.668182 0.000000 \n", - "18 0.304348 4.665129 106.179253 0.000000 \n", - "19 0.217391 4.334057 76.253006 0.000000 \n", - "20 0.391304 2.807739 16.572411 0.000000 \n", - "21 0.391304 4.215992 67.761353 0.000000 \n", - "22 0.391304 3.522572 33.871445 0.000000 \n", - "23 0.478261 2.265072 9.631822 0.000000 \n", - "24 0.347826 5.091538 162.639801 0.000000 \n", - "25 0.347826 2.982907 19.745134 0.000000 \n", - "26 0.130435 3.861120 47.518566 0.000000 \n", - "27 0.434783 4.315707 74.866554 0.166667 \n", - "28 0.304348 3.004366 20.173416 0.166667 \n", - "29 0.217391 5.262289 192.922501 0.000000 \n", - "30 0.260870 5.697386 298.087250 0.000000 \n", - "31 0.347826 3.149921 23.334219 0.000000 \n", - "32 0.391304 2.063896 7.876601 0.000000 \n", - "33 0.391304 3.141111 23.129541 0.000000 \n", - "34 0.391304 3.663168 38.984657 0.000000 \n", - "35 0.217391 3.455597 31.677193 0.000000 \n", - "36 0.217391 3.796592 44.549091 0.000000 \n", - "37 0.217391 2.545129 12.744876 0.000000 \n", - "38 0.260870 4.018140 55.597588 0.000000 \n", - "39 0.173913 3.072442 21.594568 0.166667 \n", - "40 0.434783 2.709372 15.019834 0.166667 \n", - "\n", - " val_loss val_perplexity trial_number subtrial_number \\\n", - "0 11.722857 1.233594e+05 2 0 \n", - "1 11.644334 1.140433e+05 2 0 \n", - "2 11.609864 1.101793e+05 2 0 \n", - "3 11.582150 1.071679e+05 2 0 \n", - "4 11.588885 1.078919e+05 2 0 \n", - "5 11.611365 1.103448e+05 2 0 \n", - "6 11.635376 1.130263e+05 2 0 \n", - "7 11.736249 1.250225e+05 2 0 \n", - "8 11.836572 1.382158e+05 2 0 \n", - "9 11.940872 1.534104e+05 2 0 \n", - "10 12.069603 1.744865e+05 2 0 \n", - "11 12.378307 2.375913e+05 2 0 \n", - "12 12.568721 2.874260e+05 2 0 \n", - "13 12.738580 3.406394e+05 2 0 \n", - "14 12.861950 3.853664e+05 2 0 \n", - "15 13.139782 5.087856e+05 2 0 \n", - "16 13.314763 6.060774e+05 2 0 \n", - "17 13.506835 7.344190e+05 2 0 \n", - "18 13.664001 8.594092e+05 2 0 \n", - "19 13.938638 1.131028e+06 2 0 \n", - "20 14.083958 1.307933e+06 2 0 \n", - "21 14.162007 1.414105e+06 2 0 \n", - "22 14.258838 1.557883e+06 2 0 \n", - "23 14.377925 1.754904e+06 2 0 \n", - "24 14.447185 1.880756e+06 2 0 \n", - "25 14.536368 2.056196e+06 2 0 \n", - "26 14.590198 2.169913e+06 2 0 \n", - "27 14.657779 2.321627e+06 2 0 \n", - "28 14.719577 2.469625e+06 2 0 \n", - "29 14.857208 2.834024e+06 2 0 \n", - "30 14.976109 3.191842e+06 2 0 \n", - "31 15.093396 3.589043e+06 2 0 \n", - "32 15.192738 3.963894e+06 2 0 \n", - "33 15.293323 4.383348e+06 2 0 \n", - "34 15.405540 4.903896e+06 2 0 \n", - "35 15.424549 4.998004e+06 2 0 \n", - "36 15.458982 5.173094e+06 2 0 \n", - "37 15.423234 4.991435e+06 2 0 \n", - "38 15.408874 4.920268e+06 2 0 \n", - "39 15.379680 4.778703e+06 2 0 \n", - "40 15.432412 5.037468e+06 2 0 \n", - "\n", - " model_name \n", - "0 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", - 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] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Index(['categorical_accuracy', 'loss', 'perplexity',\n", - " 'val_categorical_accuracy', 'val_loss', 'val_perplexity',\n", - " 'trial_number', 'subtrial_number', 'model_name'],\n", - " dtype='object')\n", - "metric_to_rank_by is: 'perplexity'\n", - "Type of metric_to_rank_by is: \n", - "metric_to_rank_by is: 'perplexity'\n", - "Type of metric_to_rank_by is: \n", - "Best result this trial was: 7.876600742340088\n", - "Type of best result: \n", - "Best model name: 2025_11_23 16_55_cerebros_not-gpt_meta_42/models/tr_0000000000000002_subtrial_0000000000000000.keras\n", - "Cerebros trained 3 models in 12.19 min. Average time per model: 4.06 min.\n", - "Cerebros best perplexity achieved in Phase I-a is 7.876600742340088\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Training Stage I-a - Model Evaluation (Subjective):\n", - "\n", - "- We retrieve the best model found during the NAS phase and test its text generation capabilities from a subjective standpoint.\n", - "- Keep in mind, this is trained on 10 text samples. It is impressive that it can generate anything, especially subjects and verbs that are on-topic and agree, and is otherwise sensible, despite being grammatically gibberish.\n", - "\n", - "FYI: The generative components we imported from cerebrosllmutils:\n", - "\n", - "## Model config\n", - "```python\n", - "\n", - "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='CerebrosNotGPTConfig')\n", - "class CerebrosNotGPTConfig:\n", - " def __init__(self, max_sequence_length=1536, padding_token=None):\n", - " self.max_sequence_length = max_sequence_length\n", - " self.padding_token = padding_token\n", - "\n", - " def get_config(self):\n", - " return {\n", - " 'max_sequence_length': self.max_sequence_length,\n", - " 'padding_token': self.padding_token\n", - " }\n", - "\n", - " @classmethod\n", - " def from_config(cls, config):\n", - " return cls(**config)\n", - "```\n", - "\n", - "## Model class we imported from cerebrosllmutil, having:\n", - "\n", - "- Greedy sampling\n", - "- Temperature scaling\n", - "- Top p sampling\n", - "- Top k sampling\n", - "- Presence penlaty\n", - "- Frequency penalty\n", - "- Repetition penalty\n", - "\n", - "```python\n", - "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='CerebrosNotGPT')\n", - "class CerebrosNotGPT(tf.keras.Model):\n", - " def __init__(self, config: Any, model: Any = None, **kwargs):\n", - " # 1. Store the nested model argument.\n", - " self.config = config\n", - " self.model = model\n", - " \n", - " # 2. Extract and remove custom kwargs (like 'model') before calling super.\n", - " # This is important to prevent 'unrecognized keyword argument' errors.\n", - " # The nested model is already extracted and stored, so it can be safely removed.\n", - " kwargs.pop('model', None)\n", - " \n", - " # 3. Call the parent constructor with the cleaned kwargs.\n", - " super().__init__(**kwargs)\n", - "\n", - " self.max_sequence_length = config.max_sequence_length\n", - " self.padding_token = config.padding_token\n", - "\n", - " def get_config(self):\n", - " base_config = super().get_config()\n", - " config_dict = {\n", - " 'config': self.config.get_config(),\n", - " }\n", - " \n", - " # Explicitly handle nested model serialization.\n", - " # This is required if Keras's automatic tracking fails.\n", - " if self.model is not None:\n", - " # Note: This approach might still suffer from weight loss.\n", - " # The recommended way is to let Keras handle it automatically.\n", - " config_dict['model'] = tf.keras.utils.serialize_keras_object(self.model)\n", - "\n", - " base_config.update(config_dict)\n", - " return base_config\n", - "\n", - " @classmethod\n", - " def from_config(cls, config):\n", - " # Separate the custom config.\n", - " config_obj_dict = config.pop('config')\n", - " config_obj = CerebrosNotGPTConfig.from_config(config_obj_dict)\n", - " \n", - " # Manually extract and load the nested model.\n", - " nested_model_config = config.pop('model', None)\n", - " if nested_model_config:\n", - " nested_model = tf.keras.utils.deserialize_keras_object(nested_model_config)\n", - " else:\n", - " nested_model = None\n", - " \n", - " # Reconstruct the outer model by passing the restored parts.\n", - " return cls(config=config_obj, model=nested_model, **config)\n", - "\n", - " def call(self, inputs, training=False):\n", - " if self.model is None:\n", - " raise ValueError(\"Inner model not initialized properly\")\n", - " return self.model(inputs, training=training)\n", - "\n", - " @staticmethod\n", - " def apply_top_k_probs(probs, k):\n", - " if k is None or k <= 0:\n", - " return probs\n", - " # Flatten and argsort for indices\n", - " sorted_indices = tf.argsort(probs, direction='DESCENDING')\n", - " keep_indices = sorted_indices[:k]\n", - " mask = tf.zeros_like(probs, dtype=tf.bool)\n", - " mask = tf.tensor_scatter_nd_update(mask, tf.reshape(keep_indices, (-1, 1)),\n", - " tf.ones((k,), dtype=tf.bool))\n", - " filtered_probs = tf.where(mask, probs, tf.zeros_like(probs))\n", - " # Renormalize\n", - " filtered_probs = filtered_probs / tf.reduce_sum(filtered_probs)\n", - " return filtered_probs\n", - "\n", - " @staticmethod\n", - " def apply_top_p_probs(probs, p):\n", - " if p is None or p >= 1.0:\n", - " return probs\n", - " sorted_indices = tf.argsort(probs, direction='DESCENDING')\n", - " sorted_probs = tf.gather(probs, sorted_indices)\n", - " cumulative_probs = tf.cumsum(sorted_probs)\n", - " mask = cumulative_probs <= p\n", - " # Always keep at least 1 token\n", - " mask = tf.concat([tf.constant([True]), mask[1:]], axis=0)\n", - " keep_indices = tf.boolean_mask(sorted_indices, mask)\n", - " filtered_probs = tf.where(\n", - " tf.reduce_any(tf.equal(tf.range(tf.shape(probs)[0])[:, None], keep_indices), axis=1), probs,\n", - " tf.zeros_like(probs))\n", - " # Renormalize\n", - " filtered_probs = filtered_probs / tf.reduce_sum(filtered_probs)\n", - " return filtered_probs\n", - "\n", - " def generate(self,\n", - " token_ids,\n", - " do_sample=False,\n", - " max_new_tokens=None,\n", - " temperature=1.0,\n", - " top_k=None,\n", - " top_p=None,\n", - " frequency_penalty=None,\n", - " presence_penalty=None,\n", - " repetition_penalty=None):\n", - " \"\"\"\n", - " Generate text autoregressively from token IDs.\n", - " Applies filtering in sequence: penalties -> temperature -> top-k -> top-p\n", - " \"\"\"\n", - " # Convert token_ids to list if it's not already\n", - " if not isinstance(token_ids, list):\n", - " token_ids = list(token_ids)\n", - "\n", - " # Determine the actual maximum number of new tokens\n", - " if max_new_tokens is None:\n", - " max_new_tokens = self.max_sequence_length - len(token_ids)\n", - " else:\n", - " max_new_tokens = min(max_new_tokens, self.max_sequence_length - len(token_ids))\n", - "\n", - " # Initialize the generated tokens list\n", - " generated_tokens = []\n", - " current_tokens = token_ids.copy()\n", - "\n", - " # Autoregressive generation loop\n", - " for _ in range(max_new_tokens):\n", - " # Pad or truncate to max_sequence_length\n", - " if len(current_tokens) > self.max_sequence_length:\n", - " input_tokens = current_tokens[-self.max_sequence_length:]\n", - " else:\n", - " padding_needed = self.max_sequence_length - len(current_tokens)\n", - " input_tokens = current_tokens + [self.padding_token] * padding_needed\n", - "\n", - " # Convert to tensor and get model prediction\n", - " input_tensor = tf.constant([input_tokens], dtype=tf.int32)\n", - " probs_nested = self.model(input_tensor)\n", - " probs = probs_nested[0] # Already softmax probabilities (NOT logits as comment says)\n", - " logits = tf.math.log(probs + 10 ** -20) # Convert to logits for penalty application\n", - "\n", - " if do_sample:\n", - " # Apply repetition/frequency/presence penalties to logits\n", - " if frequency_penalty is not None or presence_penalty is not None:\n", - " # Collect token counts from current_tokens\n", - " token_counts = {}\n", - " for t in current_tokens:\n", - " token_counts[t] = token_counts.get(t, 0) + 1\n", - "\n", - " # Prepare penalty tensor\n", - " vocab_size = tf.shape(logits)[0]\n", - " penalties = tf.zeros_like(logits)\n", - "\n", - " for token_id, count in token_counts.items():\n", - " if token_id >= vocab_size:\n", - " continue\n", - " penalty = 0.0\n", - " if presence_penalty is not None:\n", - " penalty += presence_penalty\n", - " if frequency_penalty is not None:\n", - " penalty += frequency_penalty * count\n", - "\n", - " penalties = tf.tensor_scatter_nd_add(\n", - " penalties,\n", - " [[token_id]],\n", - " [penalty]\n", - " )\n", - "\n", - " # Subtract penalties from logits\n", - " logits = logits - penalties\n", - "\n", - " # Apply repetition penalty (standard approach)\n", - " if repetition_penalty is not None and repetition_penalty != 1.0:\n", - " # Collect unique tokens that have appeared\n", - " unique_tokens = list(set(current_tokens))\n", - " vocab_size = tf.shape(logits)[0]\n", - "\n", - " for token_id in unique_tokens:\n", - " if token_id < vocab_size:\n", - " # Divide logits of repeated tokens by penalty\n", - " logits = tf.tensor_scatter_nd_update(\n", - " logits,\n", - " [[token_id]],\n", - " [logits[token_id] / repetition_penalty]\n", - " )\n", - "\n", - " # Apply temperature\n", - " if temperature != 1.0:\n", - " logits = logits / temperature\n", - "\n", - " # Convert to probabilities\n", - " probs = tf.nn.softmax(logits)\n", - "\n", - " # Apply top-k filtering (if specified)\n", - " if top_k is not None and top_k > 0:\n", - " k = min(top_k, tf.shape(probs)[0])\n", - " # Get top-k values and indices\n", - " top_k_values, top_k_indices = tf.nn.top_k(probs, k=k, sorted=False)\n", - " # Create mask for top-k positions\n", - " top_k_mask = tf.scatter_nd(\n", - " tf.expand_dims(top_k_indices, 1),\n", - " tf.ones_like(top_k_values, dtype=tf.bool),\n", - " tf.shape(probs)\n", - " )\n", - " # Zero out non-top-k probabilities\n", - " probs = tf.where(top_k_mask, probs, tf.zeros_like(probs))\n", - " # Renormalize\n", - " probs = probs / tf.reduce_sum(probs)\n", - " print(\n", - " f\">>> After top_k: {tf.shape(probs)} shape, {tf.reduce_sum(tf.cast(probs > 1e-8, tf.int32))} non-zero probs\")\n", - "\n", - " # Apply top-p filtering (if specified)\n", - " if top_p is not None and top_p < 1.0:\n", - " # Sort probabilities in descending order\n", - " sorted_indices = tf.argsort(probs, direction='DESCENDING')\n", - " sorted_probs = tf.gather(probs, sorted_indices)\n", - " cumulative_probs = tf.cumsum(sorted_probs)\n", - " # Create mask for top-p\n", - " mask = cumulative_probs <= top_p\n", - " # Always keep at least one token\n", - " mask = tf.concat([tf.constant([True]), mask[1:]], axis=0)\n", - " # Get indices to keep\n", - " keep_indices = tf.boolean_mask(sorted_indices, mask)\n", - " # Create mask for original indices\n", - " filter_mask = tf.scatter_nd(\n", - " tf.expand_dims(keep_indices, 1),\n", - " tf.ones_like(keep_indices, dtype=tf.bool),\n", - " tf.shape(probs)\n", - " )\n", - " # Apply mask and renormalize\n", - " probs = tf.where(filter_mask, probs, tf.zeros_like(probs))\n", - " probs = probs / tf.reduce_sum(probs)\n", - " print(\n", - " f\">>> After top_p: {tf.shape(probs)} shape, {tf.reduce_sum(tf.cast(probs > 1e-8, tf.int32))} non-zero probs\")\n", - "\n", - " # Sample from the final filtered distribution\n", - " # Get non-zero indices and their probabilities\n", - " non_zero_mask = probs > 1e-8\n", - " if tf.reduce_any(non_zero_mask):\n", - " filtered_indices = tf.where(non_zero_mask)[:, 0] # Get indices\n", - " filtered_probs = tf.boolean_mask(probs, non_zero_mask) # Get probabilities\n", - " # Sample\n", - " sampled_local_index = tf.random.categorical(tf.math.log(filtered_probs)[None, :], 1)[0, 0]\n", - " # Map back to vocabulary index\n", - " next_token_id = int(filtered_indices[sampled_local_index].numpy())\n", - " else:\n", - " # Fallback if all probabilities are zero\n", - " warn(\n", - " \"Token sampling had to revert to greedy sampling, because no probs had a value > 0, unexpected\")\n", - " next_token_id = int(tf.argmax(probs, axis=-1).numpy())\n", - "\n", - " else:\n", - " # Greedy sampling (argmax) - apply repetition penalty if needed\n", - " if repetition_penalty is not None and repetition_penalty != 1.0:\n", - " unique_tokens = list(set(current_tokens))\n", - " vocab_size = tf.shape(logits)[0]\n", - " for token_id in unique_tokens:\n", - " if token_id < vocab_size:\n", - " logits = tf.tensor_scatter_nd_update(\n", - " logits,\n", - " [[token_id]],\n", - " [logits[token_id] / repetition_penalty]\n", - " )\n", - "\n", - " next_token_id = int(tf.argmax(logits, axis=-1).numpy())\n", - "\n", - " # Check for termination condition\n", - " if next_token_id == self.padding_token:\n", - " break\n", - "\n", - " # Add to generated tokens and update current tokens\n", - " generated_tokens.append(int(next_token_id))\n", - " current_tokens.append(int(next_token_id))\n", - "\n", - " # Check if we've reached max sequence length\n", - " if len(current_tokens) >= self.max_sequence_length:\n", - " break\n", - "\n", - " return token_ids + generated_tokens\n", - "\n", - "```" - ], - "metadata": { - "id": "96KSf1hKoe0H" - } - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "## How this LLM wrapper works under the hood: A Simple Overview\n", - "\n", - "- Think of a Large Language Model like the \"autocomplete\" on your cell phone's keyboard that suggests the next word.\n", - "- Now, imagine you continuously click the suggested next word.\n", - "- The model picks the mathematically most likely next word, and you just go with it, and pick the next, then the next ...\n", - "\n", - "### Here is the step-by-step flow of how it generates text.\n", - "\n", - "1. INPUT: The Prompt\n", - "\n", - "The process always starts with a piece of text from you, the user.\n", - "\n", - "\"Write a story\"\n", - "\n", - "2. STEP 1: Tokenization โ€” From Words to Numbers\n", - "\n", - "A computer doesn't understand letters or words; it understands numbers. The first step is to convert the prompt into a sequence of numbers the model can process. The tokenizer is a specialized dictionary for this job.\n", - "\n", - " What comes in: A string of text (\"Write a story\").\n", - " What goes out: A list of numerical IDs ([92, 21, 54, 21, 63, ...]).\n", - "\n", - "To make processing consistent, the input is always padded to a fixed length (e.g., 40 tokens). Any empty slots are filled with a special ID that is assigned by the tokenizer.\n", - "\n", - "\"Write a story\" -> tokenizer -> [92, 21, 54, 21, 63, 1234, 1234, ... (length 40)]\n", - "\n", - "For example it may look like:\n", - "```\n", - "92 = \"Write\"\n", - "21 = \" \"\n", - "54 = \"a\"\n", - "63 = \"story\"\n", - "1234 = \"\" (Repeated until there are 40 numbers)\n", - "```\n", - "\n", - "3. The Model's Core: Going From Token IDs to the Predicted Next Token:\n", - "\n", - "This is the \"black box\" part. Inside the model, 4 basic things happen:\n", - "\n", - " 1. Embedding (Converts the discrete, high-dimensional sequence of tokens into a continuous distribution of a smaller dimensionality).\n", - " 2. Positional embedding: Positional embedding: Takes the output of the embedding layer and represents their relative sequential order as a continuous distribution with a clear mathematical relationship.\n", - " 3. Prediction: Prediction: A lattice of Dense layers, arranged as columns and rows, each having randomized lateral connectivity with other Dense layers on the same row, and randomized vertical connectivity with Dense layers on other rows. This takes the positional embedding's output and returns a numerical answer from its head layer. This element, produced by the Cerebros NAS, serves as a more computationally efficient alternative to the attention block used in other LLMs. The output is of shape (BATCH_SIZE, VOCABULARY_SIZE) as logits.\n", - " 4. Output activation (Scales the output to a valid range). In this case, the raw output is a tensor of shape (BATCH_SIZE, VOCABULARY_SIZE). The numbers need to be cast as probabilities, so the valid range is:\n", - " - Each element in the list must be in the range between 0 and 1 (inclusive).\n", - " - The entire list of numbers must add up to 1.\n", - " - Softmax is used to accomplish this.\n", - "\n", - "As mentioned before, this is a \"Single Head\" model, unlike most LLMs (like GPT-3/4). Each call returns **only** the next token expected in the sequence, expressed as a list of probabilities (probs) of shape (BATCH_SIZE, VOCABULARY_SIZE).\n", - "\n", - "\n", - "4. Predicting the Next Word From the Output of The Final Layer:\n", - "\n", - "After the model returns a list of probabilities, we must **pick the next word** from this. There are VOCABULARY_SIZE words in the vocabulary, each assigned an index position on this list.\n", - "\n", - "5. Sampling\n", - "\n", - "- **Greedy Sampling** The naive strategy is to just pick the highest probability in this distribution (we call this greedy sampling) and assume it is the correct next token. You then decode that token ID and use it as the next word. Then de-code that toekn id and use that as the next word. Naively assuming the highest probability is correct makes for a few problems, including:\n", - " - The output will be identical every time you write the same prompt.\n", - " - Common words like \"the\", \"and\", ... will be used too often and used out of place.\n", - " - The text will seem \"dry\" and lack creative appeal.\n", - "- **Beam Sampling**: The better approach, is scaling then sampling from a few of the top choices. We apply scaling to the logits and recalculate the probabilities. Then, we eliminate unlikely possibilities. This leaves a smaller set of plausible tokens, from which we randomly select the next word. The methods we use are:\n", - " - **Presence penalty:** Steeply penalizes the logit for a token that has already been used recently or as the last word in the sequence, making it very unlikely to survive sampling and be selected. **Its purpose:** Mainly prevents the same word from being used twice **in immediate succession** \"This is **the the the** problem which **this this** scaling technique should fix.\"\n", - " - **Frequency penalty:** Mildly penalizes the logit for a token that has been **overused** in the text, but **not necessarily** the last or recent word, making it less likely to be chosen repeatedly but still possible. **For an example:** \"This technique **like** fixes **like** this from **like** happening. It's **like** really really annoying.\"\n", - " - **Repetition penalty scaling**: A penalty that balances the effects of both presence and frequency penalties, attempting to fix both problems at the same time.\n", - " - **Temperature scaling:** Temperature scaling divides logits by a number you set for 'tempterature' to control output \"creativity\" vs \"precision\". Low temperatures less than 1 make the model's top choices more likely, creating predictable text. High temperatures greater than 1, give less likely words a better chance, leading to more diverse and random text. Basically the higher you set it, the more creative and less factual the LLM's writing will be, the lower, the more precice and factual.\n", - " - After applying all scaling, we convert the logits back to probabilities using softmax. We then proceed to sampling:\n", - " - **Top k sampling**: Set a number 'k'. Eliminate all but the highest k numbers on this list of scaled probabilities.\n", - " - **Top p sampling:** Set a number 'p'. Starting from the most likely token, add up the probabilities until the sum reaches or exceeds 'p'. Keep only this cumulative set of tokens.\n", - " \n", - "Now that we have scaled and filtered the list of tokens, we randomly pick one from the remaining options.\n", - "\n", - "\n", - "6. ## The Generation Loop: We just do this on repeat.\n", - "\n", - "The model only predicts one word at a time. To complete text, we repeat this with the result of the original prompt + the result of predicting the next. We call this an **autoregressive** loop.\n", - "\n", - "\n", - " Start with a prompt \"\"Write, a, story\"\n", - " \n", - " Input: [Write, a, story]\n", - " Model predicts the token that decodes to: \"about\"\n", - "\n", - " Repeat 1: New Input: The appended sequence is fed back into the model.\n", - " New Input: [Write, a, story, about]\n", - " Model predicts: \"a\"\n", - "\n", - " Repeat 2:New Input:\n", - " New Input: [Write, a, story, about, a]\n", - " Model predicts: \"fox\"\n", - "\n", - "This loop continues until the model generates a special \"end-of-sequence\" token / pad token or it reaches its maximum length limit (40 tokens in our example).\n", - "\n", - "\n", - "\n", - "## Revisiting the analogy of the auto complete on repeat, this is what this looks like:\n" - ], - "metadata": { - "id": "kMW_6Vrq_Yi9" - } - }, - { - "cell_type": "markdown", - "source": [ - 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)" - ], - "metadata": { - "id": "NaMi9QniKqdO" - } - }, - { - "cell_type": "code", - "source": [ - "# Get the best model from the search\n", - "best_model_found = cerebros_automl.get_best_model(purge_model_storage_files='slate')\n", - "\n", - "# Create config and generative model wrapper\n", - "config = CerebrosNotGPTConfig(\n", - " max_sequence_length=MAX_SEQ_LENGTH,\n", - " padding_token=tokenizer.pad_token_id\n", - ")\n", - "generator = CerebrosNotGPT(config, model=best_model_found)\n", - "\n", - "# Test if the model can be built successfully\n", - "text = \"This is a test ...\"\n", - "input_ids = tokenizer(text, add_special_tokens=False)['input_ids']\n", - "current_tokens = input_ids.copy()\n", - "PADDING_TOKEN = tokenizer.pad_token_id\n", - "\n", - "if len(current_tokens) > MAX_SEQ_LENGTH:\n", - " input_tokens = current_tokens[-MAX_SEQ_LENGTH:]\n", - "else:\n", - " padding_needed = MAX_SEQ_LENGTH - len(current_tokens)\n", - " input_tokens = current_tokens + [PADDING_TOKEN] * padding_needed\n", - "\n", - "# A dummy pass to force the model to build\n", - "\n", - "input_tensor = tf.constant([input_tokens], dtype=tf.int32)\n", - "\n", - "try:\n", - " _ = generator(input_tensor)\n", - " print(\"โœ… Building LLM Model Successful!\")\n", - "except Exception as exc:\n", - " error_message = f\"โŒ Building model returned the error: {exc}\"\n", - " print(error_message)\n" - ], - "metadata": { - "id": "AEk-TtPCxleV", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d253eeeb-831e-48ce-f256-c8f10540064a" - }, - "execution_count": 19, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.12/dist-packages/keras/src/layers/layer.py:421: UserWarning: `build()` was called on layer 'interleaved_ro_pe', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.\n", - " warnings.warn(\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "โœ… Building LLM Model Successful!\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Text Generation Utilities\n", - "\n", - "We define two helper functions for text generation:\n", - "\n", - "- One for greedy sampling\n", - "- One for beam sampling with various parameters." - ], - "metadata": { - "id": "u6-wAM0XyUZC" - } - }, - { - "cell_type": "code", - "source": [ - "\n", - "# Required parameter\n", - "\n", - "trial_number =1\n", - "\n", - "\n", - "# Utility function for greedy sampling\n", - "def complete_text_greedy(text: str, max_new_tokens: int = 10) -> str:\n", - " input_ids = tokenizer(text, add_special_tokens=False)['input_ids']\n", - " generated_tokens = generator.generate(\n", - " token_ids=input_ids,\n", - " do_sample=False,\n", - " max_new_tokens=max_new_tokens\n", - " )\n", - " generated_text = tokenizer.decode(generated_tokens).replace(text, \"\")\n", - " return generated_text\n", - "\n", - "# Utility function for beam sampling\n", - "def complete_text_beam(text: str,\n", - " max_new_tokens: int = 10,\n", - " temperature: float = 0.75,\n", - " top_k: int = 75,\n", - " top_p: float = 0.98,\n", - " repetition_penalty: float = None,\n", - " presence_penalty: float = 1.3,\n", - " frequency_penalty: float = 1.4) -> str:\n", - " input_ids = tokenizer(text, add_special_tokens=False)['input_ids']\n", - " generated_tokens = generator.generate(\n", - " token_ids=input_ids,\n", - " do_sample=True,\n", - " max_new_tokens=max_new_tokens,\n", - " temperature=temperature,\n", - " top_k=top_k,\n", - " top_p=top_p,\n", - " presence_penalty=presence_penalty,\n", - " frequency_penalty=frequency_penalty\n", - " )\n", - " generated_text = tokenizer.decode(generated_tokens).replace(text, \"\")\n", - " return generated_text\n" - ], - "metadata": { - "id": "f8XigcJcykLn" - }, - "execution_count": 20, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# Running Generation Tests\n", - "\n", - "We run a series of tests with different prompts and sampling parameters to evaluate the quality of the model from Stage I-a." - ], - "metadata": { - "id": "HG0IjcWEyrXn" - } - }, - { - "cell_type": "code", - "source": [ - "def test_text(test_prompt: str, max_new_tokens: int, result_cutoff: float, trial_id: int,\n", - " test_sample_number: int, result_0: float) -> None:\n", - " \"\"\"\n", - " If the result_0 < result_cutoff, this will run a matrix of different sampling values and print out the resulting text for human subjective evaluation.\n", - "\n", - " Parameters:\n", - " - test_prompt: a string to prompt generation\n", - " - max_new_tokens: int, number of tokens to generate unless we generate a stop token.\n", - " - sample_number: Metadata for sample...\n", - " - result_0: Perplexity score from this run\n", - " - result_cutoff: Perplexity score that would be expected to indicate a trial worth running this pn\n", - "\n", - " \"\"\"\n", - " if result_0 < result_cutoff:\n", - " generation_param_permutations = [\n", - " # #3\n", - " {\n", - " 'max_new_tokens': max_new_tokens,\n", - " 'temperature': 0.6,\n", - " 'top_k': 75,\n", - " 'top_p': 0.98,\n", - " 'repetition_penalty': None,\n", - " 'presence_penalty': 1.3,\n", - " 'frequency_penalty': 1.4\n", - " },\n", - " # #4\n", - " {\n", - " 'max_new_tokens': max_new_tokens,\n", - " 'temperature': 0.7,\n", - " 'top_k': 75,\n", - " 'top_p': 0.98,\n", - " 'repetition_penalty': None,\n", - " 'presence_penalty': 1.3,\n", - " 'frequency_penalty': 1.4\n", - " },\n", - " # #5\n", - " {\n", - " 'max_new_tokens': max_new_tokens,\n", - " 'temperature': 0.7,\n", - " 'top_k': 75,\n", - " 'top_p': 0.97,\n", - " 'repetition_penalty': None,\n", - " 'presence_penalty': 1.3,\n", - " 'frequency_penalty': 1.4},\n", - " # #6\n", - " {\n", - " 'max_new_tokens': max_new_tokens,\n", - " 'temperature': 0.75,\n", - " 'top_k': 75,\n", - " 'top_p': 0.98,\n", - " 'repetition_penalty': None,\n", - " 'presence_penalty': 1.4,\n", - " 'frequency_penalty': 1.4},\n", - " # #7\n", - " {\n", - " 'max_new_tokens': max_new_tokens,\n", - " 'temperature': 0.7,\n", - " 'top_k': 75,\n", - " 'top_p': 0.98,\n", - " 'repetition_penalty': None,\n", - " 'presence_penalty': 1.4,\n", - " 'frequency_penalty': 1.4},\n", - " # #8\n", - " {\n", - " 'max_new_tokens': max_new_tokens,\n", - " 'temperature': 0.6,\n", - " 'top_k': 75,\n", - " 'top_p': 0.98,\n", - " 'repetition_penalty': None,\n", - " 'presence_penalty': 1.4,\n", - " 'frequency_penalty': 1.4\n", - " },\n", - " {\n", - " 'max_new_tokens': max_new_tokens,\n", - " 'temperature': 0.6,\n", - " 'top_k': 40,\n", - " 'top_p': 0.96,\n", - " 'repetition_penalty': None,\n", - " 'presence_penalty': 1.4,\n", - " 'frequency_penalty': 1.4\n", - " },\n", - " {\n", - " 'max_new_tokens': max_new_tokens,\n", - " 'temperature': 0.7,\n", - " 'top_k': 45,\n", - " 'top_p': 0.97,\n", - " 'repetition_penalty': None,\n", - " 'presence_penalty': 1.4,\n", - " 'frequency_penalty': 1.3\n", - " }, #\n", - " {\n", - " 'max_new_tokens': max_new_tokens,\n", - " 'temperature': 0.6,\n", - " 'top_k': 75,\n", - " 'top_p': 0.99,\n", - " 'repetition_penalty': None,\n", - " 'presence_penalty': 1.4,\n", - " 'frequency_penalty': 1.4\n", - " },\n", - " {\n", - " 'max_new_tokens': max_new_tokens,\n", - " 'temperature': 0.65,\n", - " 'top_k': 75,\n", - " 'top_p': 0.985,\n", - " 'repetition_penalty': None,\n", - " 'presence_penalty': 1.4,\n", - " 'frequency_penalty': 1.4\n", - " },\n", - " {\n", - " 'max_new_tokens': max_new_tokens,\n", - " 'temperature': 0.8,\n", - " 'top_k': 75,\n", - " 'top_p': 0.99,\n", - " 'repetition_penalty': None,\n", - " 'presence_penalty': 0.7,\n", - " 'frequency_penalty': 0.7\n", - " }\n", - " ]\n", - " # Default cases, no params\n", - " response_1 = complete_text_greedy(text=test_prompt, max_new_tokens=max_new_tokens)\n", - " print(\n", - " f\"Trial #: {trial_id} Text Sample #: {test_sample_number} Perplexity: {result_0} GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: '{test_prompt}' RESPONSE: '{response_1}'\")\n", - " # print(f\"Sample {sample_number}: I ask the generator (greedy): {test_prompt}... It responds: '{response_1}'.\")\n", - " response_2 = complete_text_beam(text=test_prompt, max_new_tokens=max_new_tokens)\n", - " print(\n", - " f\"Trial #: {trial_id} Text Sample #: {test_sample_number} Perplexity: {result_0} GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: '{test_prompt}' RESPONSE: '{response_2}'.\")\n", - " # print(f\"Sample {sample_number}: I ask the generator (Beam defaults - max_new_tokens: 10, temperature: 0.75, top_k: 75, top_p: 0.98, repetition_penalty: None, presence_penalty: 1.3, frequency_penalty: 1.4): {test_prompt}... It responds: '{response_2}'.\")\n", - "\n", - " for perm_0 in generation_param_permutations:\n", - " response_0 = complete_text_beam(text=test_prompt,\n", - " max_new_tokens=max_new_tokens,\n", - " temperature=perm_0['temperature'],\n", - " top_k=perm_0['top_k'],\n", - " top_p=perm_0['top_p'],\n", - " repetition_penalty=perm_0['repetition_penalty'],\n", - " presence_penalty=perm_0['presence_penalty'],\n", - " frequency_penalty=perm_0['frequency_penalty'])\n", - " print(\n", - " f\"Trial #: {trial_id} Text Sample #: {test_sample_number} Perplexity: {result_0} GENERATE PARAMS: max_new_tokens={perm_0['max_new_tokens']} temperature={perm_0['temperature']}, top_k={perm_0['top_k']}, top_p={perm_0['top_p']}, repetition_penalty={perm_0['repetition_penalty']} presence_penalty={perm_0['presence_penalty']} frequency_penalty{perm_0['frequency_penalty']} PROMPT: '{test_prompt}' RESPONSE: '{response_0}'\")\n", - "\n", - "\n", - "prompt_samples = [\n", - " \"I saw the sun and it was as shining on the\",\n", - " \"And God said, Let there be light: and there \",\n", - " \"In the beginning God created the heavens\"\n", - "]\n", - "\n", - "\n", - "counter = 0\n", - "for sample in prompt_samples:\n", - " test_text(\n", - " test_prompt=sample,\n", - " max_new_tokens=MAX_NEW_TOKENS,\n", - " result_cutoff=15,\n", - " trial_id=trial_number,\n", - " test_sample_number=counter,\n", - " result_0=phase_i_a_result)\n", - " counter += 1\n", - "\n", - "\n", - "collect()\n" - ], - "metadata": { - "id": "hut-HAJjyvn-", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "e05a9fb1-706e-4f26-e668-825f7df940c2" - }, - "execution_count": 21, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth the the the the the the the the the the the the the the'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 6 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 6 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 7 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 7 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth God beginning'.\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 8 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth. beginning created God'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 13 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 31 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 9 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 14 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' created. beginning God earthless earth beginning'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 6 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth God.'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 6 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 6 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 7 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 8 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 10 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 16 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 8 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 8 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' God earth beginning created heavens. earth created'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' beginning created earth'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 9 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 7 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' created beginning earth heavens. God earth'\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 7 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 6 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' created earth beginning God heavens. earth'\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 7 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 6 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 8 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 10 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 9 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 8 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 8 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 6 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' God earth beginning created beginning God. earth heavens created beginning heavens earth. earth'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 6 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 7 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 9 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 10 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 18 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 27 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' created earth beginning God heavens. created earth'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 6 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 7 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 9 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 8 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 12 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' beginning earth God created earth heavens'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 8 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 26 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 16 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 26 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 29 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 34 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 46 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 42 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 57 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 60 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 60 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth created heavens earth\\Order.cpt. the beginning'\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the the.. the the the the the the the the the the.'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' earth. the'.\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' earth. the.'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: 'And God said, Let there be light: and there. earth the'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the. the earth'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the.'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the earth'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: 'And God said, Let there be light: and there. the earth'\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the. earth'\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the. earth'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: 'And God said, Let there be light: and there. the earth'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the earth.'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ''\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'In the beginning God created the heavens' RESPONSE: ' heavens heavens heavens heavens and heavens heavens heavens heavens heavens heavens heavens heavens and and'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth'.\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and. earth'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 6 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' was earth'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and. earth'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and. earth'\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and. earth'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 6 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 3 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 2 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and created earth.'\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "5885" - ] - }, - "metadata": {}, - "execution_count": 21 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Syage I-b: Extended Training\n", - "\n", - "- Now, we take the best model from Stage I-a and continue training it on a larger dataset.\n", - "- This uses a streaming `tf.data.Dataset` generator to allow handling of much larger data sets without using more RAM.\n", - "- This would allow us to select far more samples, but for now, we select a small subset for this small scale environment.\n", - "\n", - "## Streaming Data Generator for Large Datasets\n", - "\n", - "\n", - "The **SampleExpansionGenerator** class, which we create below:\n", - "\n", - " - Applies and streams the same preprocessing logic to the raw text samples as we did in Stage I-a.\n", - " - However, it preprocesses one **sample expansion batch** at a time and stores the resulting expanded samples in memory.\n", - " - It then feeds the resulting expanded samples to the model in batches matching the **model's BATCH_SIZE** as requested by the training loop.\n", - " - **sample expansion batch** is not the same as **the model's BATCH_SIZE**.\n", - "\n", - "For example, we could train on a dataset of 10 \\** 6 samples, while setting the **sample expansion batch size** to 100 while the **model's batch size** is 10.\n", - " - 100 raw text samples will be expoanded at a time.\n", - " - This results in thousands of expanded sub-samples being queued and ready for the model to take.\n", - " - The model will take 10 of these at a time until it does not have 10 left to provide.\n", - " - Then, the generator will then preprocess another 100 text samples and garbage collect.\n", - "\n", - "This allows training on datasets that would be much larger than available memory after expansion, making the training scalable.\n", - "\n", - "\n", - "### The sample expansion batch size should be optimized to balance two opposing forces:\n", - "\n", - " - Memory pressure increases with the number of expanded samples held in memory.\n", - " - Delays are caused by switching back and forth between tensor operations and preprocessing when batches are too small.\n", - "\n" - ], - "metadata": { - "id": "tuhQx2kjy4nn" - } - }, - { - "cell_type": "code", - "source": [ - "# Replace your existing class and function with these:\n", - "class SampleExpansionGenerator:\n", - " def __init__(self,\n", - " raw_text_samples,\n", - " tokenizer,\n", - " sample_expansion_batch_size=50,\n", - " model_batch_size=10,\n", - " prompt_length_0=PROMPT_LENGTH,\n", - " max_seq_length=MAX_SEQ_LENGTH,\n", - " vocabulary_size=VOCABULARY_SIZE):\n", - "\n", - " self.raw_text_samples = raw_text_samples\n", - " self.tokenizer = tokenizer\n", - " self.sample_expansion_batch_size = sample_expansion_batch_size\n", - " self.model_batch_size = model_batch_size\n", - " self.prompt_length_0 = prompt_length_0\n", - " self.max_seq_length = max_seq_length\n", - " self.vocabulary_size = vocabulary_size\n", - " self.data = []\n", - " self.labels = []\n", - " self.current_index = 0\n", - "\n", - " def _expand_next_batch(self):\n", - " # If we've already processed all raw samples for this epoch, do nothing.\n", - " if self.current_index >= len(self.raw_text_samples):\n", - " return\n", - "\n", - " # Determine the next meta-batch\n", - " start_idx = self.current_index\n", - " end_idx = min(start_idx + self.sample_expansion_batch_size, len(self.raw_text_samples))\n", - "\n", - " batch_samples = self.raw_text_samples[start_idx:end_idx]\n", - " self.current_index = end_idx\n", - "\n", - " # Run prepare_data on this batch\n", - " input_ids_list, labels_list, _ = prepare_data(\n", - " data_0=batch_samples,\n", - " tokenizer_0=self.tokenizer,\n", - " max_seq_length=self.max_seq_length,\n", - " prompt_length=self.prompt_length_0)\n", - "\n", - " # Add the new data to our internal queues\n", - " self.data.extend(input_ids_list)\n", - " self.labels.extend(labels_list)\n", - "\n", - " def __iter__(self):\n", - " # Reset to initial state for new epoch\n", - " self.current_index = 0\n", - " self.data = []\n", - " self.labels = []\n", - " return self\n", - "\n", - " def __next__(self):\n", - " # If queues are empty, try to expand them from raw samples\n", - " if not self.data:\n", - " self._expand_next_batch()\n", - "\n", - " # If they are STILL empty after trying to expand, the epoch is over.\n", - " if not self.data:\n", - " raise StopIteration\n", - "\n", - " # Pop and return one sample\n", - " input_sample = self.data.pop(0)\n", - " label_sample = self.labels.pop(0)\n", - "\n", - " return ((input_sample,), label_sample)\n", - "\n", - "\n", - "# Create the tf.data.Dataset\n", - "def create_dataset(raw_text_samples, tokenizer, sample_expansion_batch_size=50, model_batch_size=10) -> tf.data.Dataset:\n", - " generator_0 = SampleExpansionGenerator(\n", - " raw_text_samples=raw_text_samples,\n", - " tokenizer=tokenizer,\n", - " sample_expansion_batch_size=sample_expansion_batch_size,\n", - " model_batch_size=model_batch_size # Pass this parameter\n", - " )\n", - "\n", - " dataset = tf.data.Dataset.from_generator(\n", - " lambda: generator_0,\n", - " # output_signature=(\n", - " # (tf.TensorSpec(shape=(generator_0.max_seq_length,), dtype=tf.int32),),\n", - " # # tf.TensorSpec(shape=(generator_0.max_seq_length,), dtype=tf.int32), # Use generator's parameter\n", - " # tf.TensorSpec(shape=(generator_0.vocabulary_size,), dtype=tf.float32) # Use generator's parameter\n", - " # )\n", - " output_signature=(\n", - " (tf.TensorSpec(shape=(generator_0.max_seq_length,), dtype=tf.int32),), # A tuple containing ONE TensorSpec\n", - " tf.TensorSpec(shape=(generator_0.vocabulary_size,), dtype=tf.float32) # A single TensorSpec\n", - " )\n", - " )\n", - "\n", - " # Batch it\n", - " dataset = dataset.batch(model_batch_size)\n", - " dataset = dataset.prefetch(tf.data.AUTOTUNE) # Prefetch for performance\n", - " return dataset\n", - "\n", - "# Create training and validation datasets\n", - "phase_i_b_train_dataset = create_dataset(\n", - " raw_text_samples=phase_i_b_train_samples,\n", - " tokenizer=tokenizer,\n", - " sample_expansion_batch_size=PHASE_I_B_SAMPLE_EXPANSION_BATCH_SIZE,\n", - " model_batch_size=batch_size\n", - ")\n", - "\n", - "phase_i_b_val_dataset = create_dataset(\n", - " raw_text_samples=phase_i_b_val_samples,\n", - " tokenizer=tokenizer,\n", - " sample_expansion_batch_size=PHASE_I_B_SAMPLE_EXPANSION_BATCH_SIZE,\n", - " model_batch_size=batch_size\n", - ")\n" - ], - "metadata": { - "id": "MHWWE0xIzLRD" - }, - "execution_count": 22, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "type(phase_i_b_train_dataset)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 121 - }, - "id": "HxwyQzSppQwp", - "outputId": "89a48aa5-c364-4057-98c4-fc4a291f448e" - }, - "execution_count": 23, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "tensorflow.python.data.ops.prefetch_op._PrefetchDataset" - ], - "text/html": [ - "
\n", - " 
tensorflow.python.data.ops.prefetch_op._PrefetchDataset
def __init__(input_dataset, buffer_size, slack_period=None, name=None)
/usr/local/lib/python3.12/dist-packages/tensorflow/python/data/ops/prefetch_op.pyA `Dataset` that asynchronously prefetches its input.
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" - ] - }, - "metadata": {}, - "execution_count": 23 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "## Model Compilation for Phase I-b\n", - "\n", - "- We recompile the model with the same base optimizer (AdamW), however this time with a custom learning rate scheduler (WarmupCosineDecayRestarts), and for disambiguation, relevant metrics for this training phase. We also add an EarlyStopping callback which is mainly being used to restore the weights from the best epoch, if that turns out to not be the last epoch.\n", - "\n", - "\n", - "## For those wanting to scale this up, a word to point out:\n", - "\n", - "The parameters for the learning rate scheduler may need to be optimized. They will be different for your data. Alternatively, you can remove the learning rate scheduler if this is too much trail and error.\n", - "\n", - "- We set the starting learning rate at: 0.0039295722955565125\n", - "- We set warmup steps to 1140, which for the data selected is 15 epochs.\n", - "- We set first decay steps to 1900, which for this data set is about 25 epochs.\n", - "\n", - "Also:\n", - "\n", - "Additionally, the early stopping callback will likely need to be adjusted. When training at scale, you may use a lower learning rate and a larger number of epochs, as well as a larger value for the start_from_epoch parameter (which specifies when to begin tracking the metric for early stopping).\n", - "\n", - "FYI, this is the custom scheduler we imported from cerebrosllmutils (CosineDecayRestarts augmented with warmup steps):\n", - "\n", - "\n", - "```python\n", - "# A custom schedule: Cosine decay with some warm - up steps\n", - "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='WarmupCosineDecayRestarts')\n", - "class WarmupCosineDecayRestarts(tf.keras.optimizers.schedules.LearningRateSchedule):\n", - " \"\"\"\n", - " A learning rate schedule that combines a linear warmup with cosine decay restarts.\n", - " \"\"\"\n", - "\n", - " def __init__(self, initial_learning_rate, warmup_steps, first_decay_steps, t_mul=2.0, m_mul=1.0, alpha=0.0):\n", - " super().__init__()\n", - "\n", - " # Store all parameters as public attributes for get_config serialization\n", - " self.initial_learning_rate = initial_learning_rate\n", - " self.warmup_steps = warmup_steps\n", - " self.first_decay_steps = first_decay_steps\n", - " self.t_mul = t_mul\n", - " self.m_mul = m_mul\n", - " self.alpha = alpha\n", - "\n", - " # Create the CosineDecayRestarts schedule for internal logic.\n", - " # The parameters passed here are the same ones we just stored.\n", - " self.cosine_restarts_schedule = tf.keras.optimizers.schedules.CosineDecayRestarts(\n", - " initial_learning_rate=initial_learning_rate,\n", - " first_decay_steps=first_decay_steps,\n", - " t_mul=t_mul,\n", - " m_mul=m_mul,\n", - " alpha=alpha\n", - " )\n", - "\n", - "\n", - " def __call__(self, step):\n", - " step = tf.cast(step, dtype=tf.float32)\n", - "\n", - " # Calculate the learning rate for both phases unconditionally\n", - " warmup_lr = self.initial_learning_rate * step / self.warmup_steps\n", - "\n", - " # The cosine schedule is designed to start from step 0, so we give it\n", - " # the \"post-warmup\" step count.\n", - " decay_lr = self.cosine_restarts_schedule(step - self.warmup_steps)\n", - "\n", - " # Create a multiplier that is 1.0 during warmup and 0.0 after.\n", - " # tf.cast(condition, tf.float32) converts a boolean tensor to 1.0 or 0.0.\n", - " warmup_multiplier = tf.cast(step < self.warmup_steps, tf.float32)\n", - "\n", - " # The decay multiplier is the opposite.\n", - " decay_multiplier = 1.0 - warmup_multiplier\n", - "\n", - " # Combine the two learning rates. Only one will be active at a time.\n", - " return (warmup_multiplier * warmup_lr) + (decay_multiplier * decay_lr)\n", - "\n", - " def get_config(self):\n", - " # Use the stored public attributes for the config.\n", - " # This bypasses the issue of accessing private attributes (_t_mul) from\n", - " # the nested Keras object, which can be brittle.\n", - " config = {\n", - " \"initial_learning_rate\": self.initial_learning_rate,\n", - " \"warmup_steps\": self.warmup_steps,\n", - " \"first_decay_steps\": self.first_decay_steps,\n", - " \"t_mul\": self.t_mul,\n", - " \"m_mul\": self.m_mul,\n", - " \"alpha\": self.alpha,\n", - " }\n", - "\n", - " # Use from_config to properly allow deserialization\n", - " return config\n", - "```\n", - "\n" - ], - "metadata": { - "id": "DPaeJKEzzlPw" - } - }, - { - "cell_type": "code", - "source": [ - "# Define loss and metrics for Phase I-b\n", - "phase_i_b_loss = tf.keras.losses.CategoricalCrossentropy()\n", - "phase_i_b_categorical_accuracy = tf.keras.metrics.CategoricalAccuracy()\n", - "phase_i_b_perplexity = Perplexity(name=\"perplexity_phase_i_b\")\n", - "\n", - "# Create the learning rate schedule instance\n", - "lr_scheduler = WarmupCosineDecayRestarts(\n", - " initial_learning_rate=INITIAL_LR_STAGE_I_B,\n", - " warmup_steps=WARMUP_STEPS,\n", - " first_decay_steps=FIRST_DECAY_STEPS_STAGE_I_B,\n", - " t_mul=1.0,\n", - " m_mul=0.9,\n", - " alpha=0.01\n", - ")\n", - "\n", - "# Recompile the existing model\n", - "generator.model.compile(\n", - " loss=phase_i_b_loss,\n", - " metrics=[phase_i_b_categorical_accuracy, phase_i_b_perplexity],\n", - " optimizer=tf.keras.optimizers.AdamW(\n", - " learning_rate=lr_scheduler,\n", - " weight_decay=phase_i_b_weight_decay,\n", - " gradient_accumulation_steps=phase_i_b_gradient_accumulation_steps\n", - " ),\n", - " jit_compile=True\n", - ")\n", - "\n", - "# Define the Early Stopping callback\n", - "early_stopping = tf.keras.callbacks.EarlyStopping(\n", - " monitor='perplexity_phase_i_b', # Monitor validation perplexity\n", - " patience=10, # Number of epochs with no improvement after which training will be stopped.\n", - " verbose=1,\n", - " restore_best_weights=True, # Restores model weights from the epoch with the best value of the monitored metric.\n", - " mode='min',\n", - " start_from_epoch=40\n", - ")\n", - "\n", - "\n", - "callbacks_list = [early_stopping]\n" - ], - "metadata": { - "id": "GGkEVa2dzOtf" - }, - "execution_count": 24, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# Run Stage I-b Training\n", - "\n", - "- We start the training process using the model.fit method with the new datasets and callbacks to continue training the same model on another dataset. In our at scale runs, both the previous stage and this stage are dene on far more data." - ], - "metadata": { - "id": "y_K5nLzVz_-b" - } - }, - { - "cell_type": "code", - "source": [ - "\n", - "\n", - "\n", - "\n", - "# print(\"Calculating steps per epoch...\")\n", - "# train_steps = sum(1 for _ in phase_i_b_train_dataset)\n", - "# val_steps = sum(1 for _ in phase_i_b_val_dataset)\n", - "# print(f\"Calculated training steps per epoch: {train_steps}\")\n", - "# print(f\"Calculated validation steps: {val_steps}\")\n", - "\n", - "# Train the model\n", - "phase_i_b_history = generator.model.fit(\n", - " x=phase_i_b_train_dataset,\n", - " validation_data=phase_i_b_val_dataset,\n", - " epochs=phase_i_b_epochs,\n", - " callbacks=callbacks_list\n", - ")\n", - "\n", - "# Store history and get the best validation perplexity\n", - "phase_i_b_history = pd.DataFrame(phase_i_b_history.history)\n", - "result_phase_i_b = float(phase_i_b_history['perplexity_phase_i_b'].min())\n", - "f\"Result of Stage 1-b training {result_phase_i_b}\"\n" - ], - "metadata": { - "id": "3GGqvlIl0FvV", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "0daf05b2-7072-4818-8b47-a05558b33470" - }, - "execution_count": 25, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1/53\n", - " 76/Unknown \u001b[1m69s\u001b[0m 636ms/step - categorical_accuracy: 0.0389 - loss: 13.6508 - perplexity_phase_i_b: 966782.2500" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.12/dist-packages/keras/src/trainers/epoch_iterator.py:160: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n", - " self._interrupted_warning()\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 690ms/step - categorical_accuracy: 0.0388 - loss: 13.6471 - perplexity_phase_i_b: 962529.6250 - val_categorical_accuracy: 0.0492 - val_loss: 11.5516 - val_perplexity_phase_i_b: 103939.8906\n", - "Epoch 2/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 609ms/step - categorical_accuracy: 0.0164 - loss: 13.8992 - perplexity_phase_i_b: 2969392.7500 - val_categorical_accuracy: 0.0492 - val_loss: 12.0771 - val_perplexity_phase_i_b: 175791.3594\n", - "Epoch 3/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 609ms/step - categorical_accuracy: 0.0250 - loss: 12.8039 - perplexity_phase_i_b: 402124.0625 - val_categorical_accuracy: 0.0656 - val_loss: 12.3528 - val_perplexity_phase_i_b: 231597.3438\n", - "Epoch 4/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 591ms/step - categorical_accuracy: 0.0415 - loss: 11.6595 - perplexity_phase_i_b: 140648.8125 - val_categorical_accuracy: 0.0492 - val_loss: 12.4123 - val_perplexity_phase_i_b: 245801.6250\n", - "Epoch 5/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 611ms/step - categorical_accuracy: 0.0439 - loss: 11.1954 - perplexity_phase_i_b: 73950.6797 - val_categorical_accuracy: 0.0492 - val_loss: 12.3395 - val_perplexity_phase_i_b: 228538.3750\n", - "Epoch 6/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 589ms/step - categorical_accuracy: 0.0816 - loss: 10.2579 - perplexity_phase_i_b: 29194.4102 - val_categorical_accuracy: 0.0656 - val_loss: 12.1179 - val_perplexity_phase_i_b: 183113.2031\n", - "Epoch 7/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 590ms/step - categorical_accuracy: 0.0590 - loss: 9.9608 - perplexity_phase_i_b: 22667.8711 - val_categorical_accuracy: 0.0492 - val_loss: 11.8740 - val_perplexity_phase_i_b: 143489.0312\n", - "Epoch 8/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 599ms/step - categorical_accuracy: 0.0593 - loss: 8.9806 - perplexity_phase_i_b: 8207.2861 - val_categorical_accuracy: 0.0328 - val_loss: 12.3863 - val_perplexity_phase_i_b: 239495.6562\n", - "Epoch 9/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 558ms/step - categorical_accuracy: 0.0661 - loss: 7.8740 - perplexity_phase_i_b: 2828.0859 - val_categorical_accuracy: 0.0164 - val_loss: 11.9790 - val_perplexity_phase_i_b: 159370.9219\n", - "Epoch 10/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 630ms/step - categorical_accuracy: 0.1062 - loss: 6.8127 - perplexity_phase_i_b: 987.0147 - val_categorical_accuracy: 0.0328 - val_loss: 11.2031 - val_perplexity_phase_i_b: 73360.1719\n", - "Epoch 11/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 604ms/step - categorical_accuracy: 0.0687 - loss: 5.7574 - perplexity_phase_i_b: 324.8636 - val_categorical_accuracy: 0.0164 - val_loss: 9.6458 - val_perplexity_phase_i_b: 15456.3154\n", - "Epoch 12/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 686ms/step - categorical_accuracy: 0.0943 - loss: 4.8160 - perplexity_phase_i_b: 124.1660 - val_categorical_accuracy: 0.0492 - val_loss: 8.6260 - val_perplexity_phase_i_b: 5574.9229\n", - "Epoch 13/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 608ms/step - categorical_accuracy: 0.1206 - loss: 4.4321 - perplexity_phase_i_b: 84.3652 - val_categorical_accuracy: 0.0328 - val_loss: 8.1588 - val_perplexity_phase_i_b: 3493.8950\n", - "Epoch 14/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 597ms/step - categorical_accuracy: 0.1237 - loss: 4.4953 - perplexity_phase_i_b: 91.3969 - val_categorical_accuracy: 0.0328 - val_loss: 8.3403 - val_perplexity_phase_i_b: 4189.2686\n", - "Epoch 15/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 579ms/step - categorical_accuracy: 0.0997 - loss: 4.2491 - perplexity_phase_i_b: 70.9299 - val_categorical_accuracy: 0.0656 - val_loss: 8.6163 - val_perplexity_phase_i_b: 5520.8823\n", - "Epoch 16/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 585ms/step - categorical_accuracy: 0.1204 - loss: 4.2542 - perplexity_phase_i_b: 70.9240 - val_categorical_accuracy: 0.0656 - val_loss: 8.7940 - val_perplexity_phase_i_b: 6594.3228\n", - "Epoch 17/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 577ms/step - categorical_accuracy: 0.1386 - loss: 4.2547 - perplexity_phase_i_b: 70.8944 - val_categorical_accuracy: 0.0984 - val_loss: 8.7318 - val_perplexity_phase_i_b: 6196.8022\n", - "Epoch 18/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 597ms/step - categorical_accuracy: 0.1209 - loss: 4.2489 - perplexity_phase_i_b: 70.4136 - val_categorical_accuracy: 0.0984 - val_loss: 8.9164 - val_perplexity_phase_i_b: 7453.2446\n", - "Epoch 19/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 567ms/step - categorical_accuracy: 0.1236 - loss: 4.2367 - perplexity_phase_i_b: 69.5275 - val_categorical_accuracy: 0.0656 - val_loss: 8.8083 - val_perplexity_phase_i_b: 6689.4990\n", - "Epoch 20/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 596ms/step - categorical_accuracy: 0.1506 - loss: 4.1450 - perplexity_phase_i_b: 63.6329 - val_categorical_accuracy: 0.0656 - val_loss: 8.6605 - val_perplexity_phase_i_b: 5770.2129\n", - "Epoch 21/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 564ms/step - categorical_accuracy: 0.1424 - loss: 4.0012 - perplexity_phase_i_b: 55.2548 - val_categorical_accuracy: 0.0820 - val_loss: 8.6945 - val_perplexity_phase_i_b: 5970.1401\n", - "Epoch 22/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 589ms/step - categorical_accuracy: 0.1520 - loss: 4.1843 - perplexity_phase_i_b: 66.0555 - val_categorical_accuracy: 0.0656 - val_loss: 8.3286 - val_perplexity_phase_i_b: 4140.4941\n", - "Epoch 23/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 557ms/step - categorical_accuracy: 0.1807 - loss: 3.8604 - perplexity_phase_i_b: 48.0663 - val_categorical_accuracy: 0.0656 - val_loss: 8.6137 - val_perplexity_phase_i_b: 5506.4224\n", - "Epoch 24/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 608ms/step - categorical_accuracy: 0.1533 - loss: 3.9858 - perplexity_phase_i_b: 54.5812 - val_categorical_accuracy: 0.1148 - val_loss: 8.5935 - val_perplexity_phase_i_b: 5396.4331\n", - "Epoch 25/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 573ms/step - categorical_accuracy: 0.1230 - loss: 4.0118 - perplexity_phase_i_b: 55.6288 - val_categorical_accuracy: 0.1475 - val_loss: 8.6210 - val_perplexity_phase_i_b: 5547.1172\n", - "Epoch 26/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 564ms/step - categorical_accuracy: 0.1588 - loss: 3.8591 - perplexity_phase_i_b: 47.8675 - val_categorical_accuracy: 0.1148 - val_loss: 8.4999 - val_perplexity_phase_i_b: 4914.4688\n", - "Epoch 27/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 582ms/step - categorical_accuracy: 0.1900 - loss: 3.8535 - perplexity_phase_i_b: 47.2824 - val_categorical_accuracy: 0.0820 - val_loss: 8.7680 - val_perplexity_phase_i_b: 6425.2207\n", - "Epoch 28/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 593ms/step - categorical_accuracy: 0.1927 - loss: 3.6720 - perplexity_phase_i_b: 39.7386 - val_categorical_accuracy: 0.0656 - val_loss: 8.7999 - val_perplexity_phase_i_b: 6633.3721\n", - "Epoch 29/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 594ms/step - categorical_accuracy: 0.1848 - loss: 3.8259 - perplexity_phase_i_b: 46.2804 - val_categorical_accuracy: 0.0656 - val_loss: 8.6051 - val_perplexity_phase_i_b: 5459.4458\n", - "Epoch 30/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 597ms/step - categorical_accuracy: 0.1691 - loss: 3.6890 - perplexity_phase_i_b: 40.3801 - val_categorical_accuracy: 0.0984 - val_loss: 8.5689 - val_perplexity_phase_i_b: 5265.4810\n", - "Epoch 31/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 587ms/step - categorical_accuracy: 0.1774 - loss: 3.6971 - perplexity_phase_i_b: 40.6956 - val_categorical_accuracy: 0.0984 - val_loss: 8.7037 - val_perplexity_phase_i_b: 6025.3599\n", - "Epoch 32/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 573ms/step - categorical_accuracy: 0.1597 - loss: 3.6218 - perplexity_phase_i_b: 37.8592 - val_categorical_accuracy: 0.0984 - val_loss: 8.7827 - val_perplexity_phase_i_b: 6520.5991\n", - "Epoch 33/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 573ms/step - categorical_accuracy: 0.2066 - loss: 3.6265 - perplexity_phase_i_b: 38.0441 - val_categorical_accuracy: 0.0984 - val_loss: 8.7695 - val_perplexity_phase_i_b: 6434.8853\n", - "Epoch 34/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 550ms/step - categorical_accuracy: 0.1622 - loss: 3.7388 - perplexity_phase_i_b: 42.4272 - val_categorical_accuracy: 0.1148 - val_loss: 8.6601 - val_perplexity_phase_i_b: 5768.0454\n", - "Epoch 35/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 537ms/step - categorical_accuracy: 0.1974 - loss: 3.4737 - perplexity_phase_i_b: 32.6702 - val_categorical_accuracy: 0.1148 - val_loss: 8.6486 - val_perplexity_phase_i_b: 5702.0361\n", - "Epoch 36/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 603ms/step - categorical_accuracy: 0.1640 - loss: 3.5527 - perplexity_phase_i_b: 35.4395 - val_categorical_accuracy: 0.1148 - val_loss: 8.7015 - val_perplexity_phase_i_b: 6011.7910\n", - "Epoch 37/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 590ms/step - categorical_accuracy: 0.1779 - loss: 3.5903 - perplexity_phase_i_b: 36.4963 - val_categorical_accuracy: 0.1148 - val_loss: 8.7223 - val_perplexity_phase_i_b: 6138.1729\n", - "Epoch 38/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 598ms/step - categorical_accuracy: 0.1935 - loss: 3.5401 - perplexity_phase_i_b: 34.7298 - val_categorical_accuracy: 0.1148 - val_loss: 8.6995 - val_perplexity_phase_i_b: 5999.7402\n", - "Epoch 39/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 622ms/step - categorical_accuracy: 0.2109 - loss: 3.5383 - perplexity_phase_i_b: 34.5639 - val_categorical_accuracy: 0.1148 - val_loss: 8.6650 - val_perplexity_phase_i_b: 5796.6436\n", - "Epoch 40/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 555ms/step - categorical_accuracy: 0.2047 - loss: 3.5124 - perplexity_phase_i_b: 33.9720 - val_categorical_accuracy: 0.1148 - val_loss: 8.7431 - val_perplexity_phase_i_b: 6267.4624\n", - "Epoch 41/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 576ms/step - categorical_accuracy: 0.1514 - loss: 3.5711 - perplexity_phase_i_b: 35.7887 - val_categorical_accuracy: 0.0656 - val_loss: 8.9814 - val_perplexity_phase_i_b: 7953.5283\n", - "Epoch 42/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 590ms/step - categorical_accuracy: 0.1761 - loss: 3.6074 - perplexity_phase_i_b: 37.1983 - val_categorical_accuracy: 0.0984 - val_loss: 9.0303 - val_perplexity_phase_i_b: 8352.2227\n", - "Epoch 43/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 571ms/step - categorical_accuracy: 0.1727 - loss: 3.6003 - perplexity_phase_i_b: 36.7872 - val_categorical_accuracy: 0.0328 - val_loss: 8.9927 - val_perplexity_phase_i_b: 8044.2207\n", - "Epoch 44/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 619ms/step - categorical_accuracy: 0.1786 - loss: 3.7416 - perplexity_phase_i_b: 42.6958 - val_categorical_accuracy: 0.1148 - val_loss: 9.1039 - val_perplexity_phase_i_b: 8990.1494\n", - "Epoch 45/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 594ms/step - categorical_accuracy: 0.2062 - loss: 3.6020 - perplexity_phase_i_b: 37.0046 - val_categorical_accuracy: 0.0984 - val_loss: 9.3867 - val_perplexity_phase_i_b: 11928.1768\n", - "Epoch 46/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 583ms/step - categorical_accuracy: 0.2035 - loss: 3.6276 - perplexity_phase_i_b: 37.9026 - val_categorical_accuracy: 0.0820 - val_loss: 9.5581 - val_perplexity_phase_i_b: 14159.1719\n", - "Epoch 47/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 590ms/step - categorical_accuracy: 0.1784 - loss: 3.4276 - perplexity_phase_i_b: 31.0932 - val_categorical_accuracy: 0.1148 - val_loss: 9.1575 - val_perplexity_phase_i_b: 9485.0088\n", - "Epoch 48/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 583ms/step - categorical_accuracy: 0.1864 - loss: 3.4227 - perplexity_phase_i_b: 31.1301 - val_categorical_accuracy: 0.1148 - val_loss: 9.1156 - val_perplexity_phase_i_b: 9095.7666\n", - "Epoch 49/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 622ms/step - categorical_accuracy: 0.2266 - loss: 3.4226 - perplexity_phase_i_b: 30.7439 - val_categorical_accuracy: 0.0820 - val_loss: 9.4648 - val_perplexity_phase_i_b: 12897.0039\n", - "Epoch 50/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 589ms/step - categorical_accuracy: 0.2455 - loss: 3.4171 - perplexity_phase_i_b: 30.9408 - val_categorical_accuracy: 0.0820 - val_loss: 9.4194 - val_perplexity_phase_i_b: 12325.3525\n", - "Epoch 51/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 596ms/step - categorical_accuracy: 0.2168 - loss: 3.2941 - perplexity_phase_i_b: 27.1144 - val_categorical_accuracy: 0.0984 - val_loss: 9.3049 - val_perplexity_phase_i_b: 10991.2559\n", - "Epoch 52/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 642ms/step - categorical_accuracy: 0.1940 - loss: 3.3548 - perplexity_phase_i_b: 28.8572 - val_categorical_accuracy: 0.0984 - val_loss: 9.1126 - val_perplexity_phase_i_b: 9068.9150\n", - "Epoch 53/53\n", - "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 610ms/step - categorical_accuracy: 0.2291 - loss: 3.3674 - perplexity_phase_i_b: 29.2831 - val_categorical_accuracy: 0.1311 - val_loss: 9.1200 - val_perplexity_phase_i_b: 9136.3135\n", - "Restoring model weights from the end of the best epoch: 53.\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'Result of Stage 1-b training 29.637819290161133'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 25 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Stage I-b: Model Evaluation and Serialization\n", - "\n", - "After extended training, we evaluate the final model performance and save the model and tokenizer for future use.\n" - ], - "metadata": { - "id": "y8Ej2P7D0T8R" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Final Generation Tests on the Stage I-b model checkpoint\n", - "\n", - "Confirm the model works after Stage I-b training." - ], - "metadata": { - "id": "dWlYvYBq0dio" - } - }, - { - "cell_type": "code", - "source": [ - "print(\"########### Phase I-b Model Checkpoint Generation Samples: ###########\")\n", - "\n", - "counter = 0\n", - "for sample in prompt_samples:\n", - " test_text(\n", - " test_prompt=sample,\n", - " max_new_tokens=MAX_NEW_TOKENS,\n", - " result_cutoff=60, #\n", - " trial_id=trial_number,\n", - " test_sample_number=counter,\n", - " result_0=result_phase_i_b\n", - " )\n", - " counter += 1\n" - ], - "metadata": { - "id": "YhGaTbGF0X_d", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8071bc5a-8520-4d13-82e1-cbd941297b4b" - }, - "execution_count": 26, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "########### Phase I-b Model Checkpoint Generation Samples: ###########\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ',,, and fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 52 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 57 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 57 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 63 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 39 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 42 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 38 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 43 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 43 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 39 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 41 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 5 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' for morning, over tree with, fruit lights bring fruit great livestock.''.\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 55 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 60 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 61 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 60 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 42 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', serve'to lights produce according each kind'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 48 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 60 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 59 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 60 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 59 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 59 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 59 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 59 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', greater and that creeping waters for fifth'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 43 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 46 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 56 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 55 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 47 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 43 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 35 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' for, lights bird produce fourth to its with'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 52 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 57 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 63 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 63 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 64 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 63 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 63 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 62 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 46 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 36 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: 'Be and, image said birds creeping day. God'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 48 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 50 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 59 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 62 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 56 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 56 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 53 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 36 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 4 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' night, image to over fish creature earth.''\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 40 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 57 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 59 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 59 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 55 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 47 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 48 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 38 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 34 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' for, to birds bird, according forth every.' man animals'\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 19 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 31 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 33 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 33 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 31 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 31 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 30 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 30 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 21 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', for plant domin fruition day with'\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 39 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 38 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 38 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 36 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 12 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 15 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 10 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' for'lights, great waters eachBe.' fruit also'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 47 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 61 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 62 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 62 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 60 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 63 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 61 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 62 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 61 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 61 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 57 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 53 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 52 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 15 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', saying produce livestock for every lights-bearing day fifth give to.''\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 47 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 60 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 61 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 62 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 64 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 63 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 63 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 63 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 62 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 62 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 60 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 34 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', fifth give to livestock light fruitful its that day every so.'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 63 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 65 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 68 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 68 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 67 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 67 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 66 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 67 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 62 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 64 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 59 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 31 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 14 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 17 non-zero probs\n", - "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' waters,, for to bring its, fruit.' kind.' and its wild'\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'And God said, Let there be light: and there ' RESPONSE: 'And God said, Let there be light: and there,, and fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 56 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 57 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 51 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 52 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 52 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 50 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 47 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 47 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 25 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 33 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' to each the kind fruit that in great birds day. its'.\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 43 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 47 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 40 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 39 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 41 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 40 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 29 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 27 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 24 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' man was forth fruit great with lesser thing animals'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 51 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 55 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 55 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 50 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 52 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 53 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 48 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 33 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 39 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 34 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 40 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' man to each thing multiply the so fruit in that as saw'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 43 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 38 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 25 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 25 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 25 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 17 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' fly created the so.' livestock fruit according'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 57 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 58 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 42 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' each fruitful-bearing in animals as man was'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 51 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 46 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 46 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 39 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 39 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 39 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 26 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 21 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 19 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 20 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 1 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' that themBeh fruit man in great according forth signs fruit.''\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 43 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 46 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 13 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 22 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' them. fruit'\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 26 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 27 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 27 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 27 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 25 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' each created so animals'\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 35 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 35 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 35 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 35 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 34 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 34 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 30 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 15 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 14 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' man-bearing lesser so animals each.' wild'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 51 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 47 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 47 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 19 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 21 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 17 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' to man each bring kind fruit forth. its animals'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 50 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 51 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 51 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 33 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 25 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 18 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' so man each. fruit in as'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 65 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 67 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 66 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 66 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 64 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 61 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 56 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 52 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 48 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 48 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 48 non-zero probs\n", - "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' man-bearing said forth so in them according signs fruit'\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'In the beginning God created the heavens' RESPONSE: ',,,,, and day day day lesser'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 41 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 53 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 41 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 39 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 40 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 42 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 40 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 40 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 39 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 36 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 15 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'In the beginning God created the heavens' RESPONSE: ',Let and was living he lesser so multiply seed fruitful livestock.'.\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 28 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 27 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 40 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 46 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 38 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 40 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 39 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 29 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' set, and said them over was man each it'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 53 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 56 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 53 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 49 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 43 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and fruitful, to was forth them'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 46 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 48 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 48 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 47 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 50 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 27 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', earth trees seed was and day good rule forth.'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 41 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 54 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 46 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 49 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 49 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 50 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 48 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 48 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 49 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 49 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 21 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 21 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', and trees them said he day good upon,' thing. fruit'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 51 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 42 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 46 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 40 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 36 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 33 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 33 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 41 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 39 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', and trees was day to seed lesser he living earth each'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 28 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 42 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 33 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 38 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 30 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 28 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 33 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 33 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 34 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 33 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 29 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 27 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 8 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', and trees was he said day. each living he fruit so'\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 15 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 25 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 25 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 25 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 22 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 20 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 20 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 19 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 20 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 24 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 27 non-zero probs\n", - ">>> After top_k: [128260] shape, 40 non-zero probs\n", - ">>> After top_p: [128260] shape, 24 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', it earth creatures day living man lesser and he each'\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 28 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 34 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 29 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 28 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 29 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 29 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 28 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 30 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 35 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 35 non-zero probs\n", - ">>> After top_k: [128260] shape, 45 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and was living day he rule trees., according'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 35 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 51 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 42 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 38 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 43 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 43 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 40 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 42 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 42 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 36 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 37 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', and trees it said upon man was forth day fruit each tree wing multiply'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 36 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 41 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 52 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 55 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 45 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 43 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 44 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 42 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 43 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' waters, and was so according each lesser multiply'\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 55 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 63 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 64 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 62 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 61 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 61 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 60 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 56 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 57 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 56 non-zero probs\n", - ">>> After top_k: [128260] shape, 75 non-zero probs\n", - ">>> After top_p: [128260] shape, 53 non-zero probs\n", - "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'In the beginning God created the heavens' RESPONSE: ',ed and to be it, multiply fruitful each'\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Save Model and Tokenizer\n", - "\n", - "- Finally, we save the tokenizer and the trained model weights to disk." - ], - "metadata": { - "id": "-oCAeR4n0mPW" - } - }, - { - "cell_type": "code", - "source": [ - "trial_number = 1 # Make sure to set this to a unique number:\n", - "# Serialize tokenizer\n", - "TOKENIZER_SAVE_PATH = f\"tokenizer-tr-{trial_number}-stage-i-b\"\n", - "tokenizer.save_pretrained(TOKENIZER_SAVE_PATH)\n", - "print(f\"Tokenizer saved to {TOKENIZER_SAVE_PATH}\")\n", - "\n", - "# Serialize model\n", - "MODEL_SAVE_PATH = f\"final_phase_ib_model_tr_{trial_number}-stage-i-b.keras\"\n", - "generator.save(MODEL_SAVE_PATH)\n", - "print(f\"Final model saved to {MODEL_SAVE_PATH}\")\n" - ], - "metadata": { - "id": "ziYdmmII0qfu", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "37a1153f-09a0-4274-9ca2-e280112e65e6" - }, - "execution_count": 27, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Tokenizer saved to tokenizer-tr-1-stage-i-b\n", - "Final model saved to final_phase_ib_model_tr_1-stage-i-b.keras\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Serialization Test\n", - "\n", - "- We run an external script (test_llm_serialization.py) to validate that the saved model and tokenizer can be loaded and used correctly." - ], - "metadata": { - "id": "y9Pvhcvl0uGt" - } - }, - { - "cell_type": "code", - "source": [ - "print(f\"๐Ÿงช Running serialization test for Stage I-b trial {trial_number}...\")\n", - "result = subprocess.run(\n", - " f\"python3 test_llm_serialization.py {TOKENIZER_SAVE_PATH} {MODEL_SAVE_PATH}\",\n", - " capture_output=True,\n", - " shell=True,\n", - " text=True # Use text=True for string output\n", - ")\n", - "\n", - "if result.returncode == 0:\n", - " print(\"โœ… Serialization test passed.\")\n", - " print(\"STDOUT:\", result.stdout)\n", - "else:\n", - " print(\"โŒ Serialization test failed.\")\n", - " print(\"STDERR:\", result.stderr)\n", - " if result.stdout:\n", - " print(\"STDOUT:\", result.stdout)\n" - ], - "metadata": { - "id": "qA5Cord40yID", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "389fe0bf-c935-4f49-dd4f-8eea8672c634" - }, - "execution_count": 28, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "๐Ÿงช Running serialization test for Stage I-b trial 1...\n", - "โœ… Serialization test passed.\n", - "STDOUT: โœ… Tokenizer loaded successfully.\n", - "โœ… CerebrosNotGPT model loaded successfully.\n", - ">>> After top_k: [128260] shape, 50 non-zero probs\n", - ">>> After top_p: [128260] shape, 19 non-zero probs\n", - ">>> After top_k: [128260] shape, 50 non-zero probs\n", - ">>> After top_p: [128260] shape, 31 non-zero probs\n", - ">>> After top_k: [128260] shape, 50 non-zero probs\n", - ">>> After top_p: [128260] shape, 28 non-zero probs\n", - ">>> After top_k: [128260] shape, 50 non-zero probs\n", - ">>> After top_p: [128260] shape, 29 non-zero probs\n", - ">>> After top_k: [128260] shape, 50 non-zero probs\n", - ">>> After top_p: [128260] shape, 26 non-zero probs\n", - ">>> After top_k: [128260] shape, 50 non-zero probs\n", - ">>> After top_p: [128260] shape, 31 non-zero probs\n", - ">>> After top_k: [128260] shape, 50 non-zero probs\n", - ">>> After top_p: [128260] shape, 30 non-zero probs\n", - ">>> After top_k: [128260] shape, 50 non-zero probs\n", - ">>> After top_p: [128260] shape, 28 non-zero probs\n", - ">>> After top_k: [128260] shape, 50 non-zero probs\n", - ">>> After top_p: [128260] shape, 32 non-zero probs\n", - ">>> After top_k: [128260] shape, 50 non-zero probs\n", - ">>> After top_p: [128260] shape, 33 non-zero probs\n", - "๐Ÿง  (serialized) Prompt: In the beginning God created the Generated Text from Serialized Model: 'In the beginning God created the, waters each trees and to living man according them'\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# And there you have it: What it takes to build an LLM from scratch using our novel architecture.\n" - ], - "metadata": { - "id": "z1lSMQ6i03XC" - } - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "W6lcAxij-Z5r" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file From eaf660ef27614ca6c3a08fb286ae6e474ad12513 Mon Sep 17 00:00:00 2001 From: David Thrower Date: Mon, 24 Nov 2025 19:14:56 -0500 Subject: [PATCH 2/4] Add files via upload --- ...1_23_demo_train_an_llm_with_cerebros.ipynb | 6561 +++++++++++++++++ 1 file changed, 6561 insertions(+) create mode 100644 2025_11_23_demo_train_an_llm_with_cerebros.ipynb diff --git a/2025_11_23_demo_train_an_llm_with_cerebros.ipynb b/2025_11_23_demo_train_an_llm_with_cerebros.ipynb new file mode 100644 index 0000000..d1f0f28 --- /dev/null +++ b/2025_11_23_demo_train_an_llm_with_cerebros.ipynb @@ -0,0 +1,6561 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Build our LLM From Scratch -\n", + "\n", + "## How Cerebros NotGPT works under the hood:\n", + "\n", + "\n", + "### This notebook demonstrates the end-to-end training pipeline that builds a small scale generative LLM from scratch, a small scale proof of concept for our own Cerebros NotGPT model, then fine tunes it on additional data.\n", + "\n", + "The process is divided into two main phases:\n", + "\n", + "- Phase I-a: Neural Architecture Search (NAS) - We use SimpleCerebrosRandomSearch to automatically discover an effective neural network architecture from a small dataset.\n", + "- Phase I-b: Extended Training - The best architecture found in Phase I-a is then trained on a larger dataset to improve its performance.\n", + "\n", + "Finally, the trained model is evaluated and serialized for future use.\n", + "\n", + "\n", + "## Setup and Configuration\n", + "\n", + "Note: This script is configured as a vanilla-scale demo environment (4 CPU / 16 GB RAM Linux with Python 3.12). No GPU is needed, and this will run in the free version of Google Colab. \n", + "\n", + "## Vanilla Demo\n", + "\n", + "- For production use, you would significantly increase the sample sizes and adjust other parameters accordingly.\n", + "- The quality of the text generated by this minimal demo (trained on 30 text samples at a sequence length of 40) does not represent the quality of a full-scale model generated from the same code.\n", + "- A script that can be modified to do such as availible at: https://github.com/david-thrower/cerebros-core-algorithm-alpha/blob/main/train_a_generative_llm.py" + ], + "metadata": { + "id": "nnsAHoJyWLed" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NzJF6_JuWElV", + "outputId": "a0f3246f-0ccd-48ea-da55-86479bc0f93c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Python 3.12.12\n" + ] + } + ], + "source": [ + "! python --version" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Getting started: Download the repo and go to the main directory of the repo" + ], + "metadata": { + "id": "f6TD2XsKPJIY" + } + }, + { + "cell_type": "code", + "source": [ + "# Download the repo\n", + "! git clone https://github.com/david-thrower/cerebros-core-algorithm-alpha.git" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AcECFSs7WVsi", + "outputId": "9fd59935-35d4-4a08-9c8a-fb01fd3e4f03" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'cerebros-core-algorithm-alpha'...\n", + "remote: Enumerating objects: 8036, done.\u001b[K\n", + "remote: Counting objects: 100% (1737/1737), done.\u001b[K\n", + "remote: Compressing objects: 100% (321/321), done.\u001b[K\n", + "remote: Total 8036 (delta 1612), reused 1449 (delta 1411), pack-reused 6299 (from 2)\u001b[K\n", + "Receiving objects: 100% (8036/8036), 65.90 MiB | 21.67 MiB/s, done.\n", + "Resolving deltas: 100% (3116/3116), done.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# set the working directory\n", + "%cd cerebros-core-algorithm-alpha" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mCpJGfD2WfLj", + "outputId": "e0fe8c05-6154-41cd-f489-08cfd2ad0fa8" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/cerebros-core-algorithm-alpha/cerebros-core-algorithm-alpha\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Next install all dependencies.\n", + "\n", + "There are 2 requirement files:\n", + " - requirements.txt: The core requirements of the neural architecture search\n", + " - cicd-requirements.txt: Requirements for NLP and text generation" + ], + "metadata": { + "id": "yT4hPXOKPU_8" + } + }, + { + "cell_type": "code", + "source": [ + "# Install the requirements for the core algorithm\n", + "! pip install -r requirements.txt; pip install -r cicd-requirements.txt" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "nwElyEdpW90P", + "outputId": "170e2158-b7a9-49f0-ce63-22c4c7410f33" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: jax==0.5.3 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 1)) (0.5.3)\n", + "Requirement already satisfied: jaxlib==0.5.3 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 2)) (0.5.3)\n", + "Requirement already satisfied: pendulum==3.0.0 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 3)) (3.0.0)\n", + "Collecting tensorflow==2.20.0 (from -r requirements.txt (line 4))\n", + " Using cached tensorflow-2.20.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)\n", + 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requirements.txt (line 9)) (1.3.3)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib==3.10.7->-r requirements.txt (line 9)) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib==3.10.7->-r requirements.txt (line 9)) (4.60.1)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib==3.10.7->-r requirements.txt (line 9)) (1.4.9)\n", + "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib==3.10.7->-r requirements.txt (line 9)) (11.3.0)\n", + "Requirement already satisfied: pyparsing>=3 in /usr/local/lib/python3.12/dist-packages (from matplotlib==3.10.7->-r requirements.txt (line 9)) (3.2.5)\n", + "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from astunparse>=1.6.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (0.45.1)\n", + "Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.19.2)\n", + "Requirement already satisfied: decorator in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (4.4.2)\n", + "Requirement already satisfied: pickleshare in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.7.5)\n", + "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (5.7.1)\n", + "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (3.0.52)\n", + "Requirement already satisfied: pygments in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (2.19.2)\n", + "Requirement already satisfied: backcall in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.2.0)\n", + "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.2.1)\n", + "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (4.9.0)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2>=2.9.6->pyvis==0.3.2->-r requirements.txt (line 7)) (3.0.3)\n", + "Requirement already satisfied: rich in /usr/local/lib/python3.12/dist-packages (from keras>=3.10.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (13.9.4)\n", + "Requirement already satisfied: namex in /usr/local/lib/python3.12/dist-packages (from keras>=3.10.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (0.1.0)\n", + "Requirement already satisfied: optree in /usr/local/lib/python3.12/dist-packages (from keras>=3.10.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (0.18.0)\n", + "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (3.4.4)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (3.11)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (2.5.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (2025.11.12)\n", + "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.20.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (3.10)\n", + "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.20.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (0.7.2)\n", + "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.20.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (3.1.3)\n", + "Requirement already satisfied: parso<0.9.0,>=0.8.4 in /usr/local/lib/python3.12/dist-packages (from jedi>=0.16->ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.8.5)\n", + "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.12/dist-packages (from pexpect>4.3->ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.7.0)\n", + "Requirement already satisfied: wcwidth in /usr/local/lib/python3.12/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.2.14)\n", + "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->keras>=3.10.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (4.0.0)\n", + "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.10.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (0.1.2)\n", + "Using cached tensorflow-2.20.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (620.7 MB)\n", + "Using cached numpy-2.3.5-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (16.6 MB)\n", + "Using cached tensorboard-2.20.0-py3-none-any.whl (5.5 MB)\n", + "Installing collected packages: numpy, tensorboard, tensorflow\n", + " Attempting uninstall: numpy\n", + " Found existing installation: numpy 1.26.4\n", + " Uninstalling numpy-1.26.4:\n", + " Successfully uninstalled numpy-1.26.4\n", + " Attempting uninstall: tensorboard\n", + " Found existing installation: tensorboard 2.19.0\n", + " Uninstalling tensorboard-2.19.0:\n", + " Successfully uninstalled tensorboard-2.19.0\n", + " Attempting uninstall: tensorflow\n", + " Found existing installation: tensorflow 2.19.1\n", + " Uninstalling tensorflow-2.19.1:\n", + " Successfully uninstalled tensorflow-2.19.1\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "scikit-learn 1.4.1.post1 requires numpy<2.0,>=1.19.5, but you have numpy 2.3.5 which is incompatible.\n", + "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.3 which is incompatible.\n", + "tensorflow-text 2.19.0 requires tensorflow<2.20,>=2.19.0, but you have tensorflow 2.20.0 which is incompatible.\n", + "opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.3.5 which is incompatible.\n", + "numba 0.60.0 requires numpy<2.1,>=1.22, but you have numpy 2.3.5 which is incompatible.\n", + "opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.3.5 which is incompatible.\n", + "umap-learn 0.5.9.post2 requires scikit-learn>=1.6, but you have scikit-learn 1.4.1.post1 which is incompatible.\n", + "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.3.5 which is incompatible.\n", + "orbax-checkpoint 0.11.28 requires jax>=0.6.0, but you have jax 0.5.3 which is incompatible.\n", + "tensorflow-decision-forests 1.12.0 requires tensorflow==2.19.0, but you have tensorflow 2.20.0 which is incompatible.\n", + "flax 0.10.7 requires jax>=0.6.0, but you have jax 0.5.3 which is incompatible.\n", + "tf-keras 2.19.0 requires tensorflow<2.20,>=2.19, but you have tensorflow 2.20.0 which is incompatible.\n", + "imbalanced-learn 0.14.0 requires scikit-learn<2,>=1.4.2, but you have scikit-learn 1.4.1.post1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed numpy-2.3.5 tensorboard-2.20.0 tensorflow-2.20.0\n", + "Requirement already satisfied: tensorflow-text==2.19.0 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 1)) (2.19.0)\n", + "Requirement already satisfied: keras-nlp==0.19.0 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 2)) (0.19.0)\n", + "Requirement already satisfied: scikit-learn==1.4.1.post1 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 3)) (1.4.1.post1)\n", + "Requirement already satisfied: tensorflow-hub==0.16.1 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 4)) (0.16.1)\n", + "Requirement already satisfied: transformers==4.54.0 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 5)) (4.54.0)\n", + "Collecting tensorflow<2.20,>=2.19.0 (from tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1))\n", + " Using cached tensorflow-2.19.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)\n", + "Requirement already satisfied: keras-hub==0.19.0 in /usr/local/lib/python3.12/dist-packages (from keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (0.19.0)\n", + "Collecting numpy<2.0,>=1.19.5 (from scikit-learn==1.4.1.post1->-r cicd-requirements.txt (line 3))\n", + " Using cached numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.4.1.post1->-r cicd-requirements.txt (line 3)) (1.16.3)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.4.1.post1->-r cicd-requirements.txt (line 3)) (1.5.2)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.4.1.post1->-r cicd-requirements.txt (line 3)) (3.6.0)\n", + "Requirement already satisfied: protobuf>=3.19.6 in /usr/local/lib/python3.12/dist-packages (from tensorflow-hub==0.16.1->-r cicd-requirements.txt (line 4)) (5.29.5)\n", + "Requirement already satisfied: tf-keras>=2.14.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow-hub==0.16.1->-r cicd-requirements.txt (line 4)) (2.19.0)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (3.20.0)\n", + "Requirement already satisfied: huggingface-hub<1.0,>=0.34.0 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (0.36.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (25.0)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (6.0.3)\n", + "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (2024.11.6)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (2.32.4)\n", + "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (0.21.4)\n", + "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (0.7.0)\n", + "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (4.67.1)\n", + "Requirement already satisfied: keras>=3.5 in /usr/local/lib/python3.12/dist-packages (from keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (3.10.0)\n", + "Requirement already satisfied: absl-py in /usr/local/lib/python3.12/dist-packages (from keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (1.4.0)\n", + "Requirement already satisfied: rich in /usr/local/lib/python3.12/dist-packages (from keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (13.9.4)\n", + "Requirement already satisfied: kagglehub in /usr/local/lib/python3.12/dist-packages (from keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (0.3.13)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (2025.3.0)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (4.15.0)\n", + "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (1.2.0)\n", + "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (25.9.23)\n", + "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.6.0)\n", + "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.2.0)\n", + "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (18.1.1)\n", + "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.4.0)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (75.2.0)\n", + "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (1.17.0)\n", + "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.2.0)\n", + "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (2.0.1)\n", + "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (1.76.0)\n", + "Collecting tensorboard~=2.19.0 (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1))\n", + " Using cached tensorboard-2.19.0-py3-none-any.whl.metadata (1.8 kB)\n", + "Requirement already satisfied: h5py>=3.11.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.15.1)\n", + "Requirement already satisfied: ml-dtypes<1.0.0,>=0.5.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.5.4)\n", + "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (3.4.4)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.54.0->-r cicd-requirements.txt (line 5)) 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satisfied: markdown>=2.6.8 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.10)\n", + "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.7.2)\n", + "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.1.3)\n", + "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (4.0.0)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from 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+ " Successfully uninstalled numpy-2.3.5\n", + " Attempting uninstall: tensorboard\n", + " Found existing installation: tensorboard 2.20.0\n", + " Uninstalling tensorboard-2.20.0:\n", + " Successfully uninstalled tensorboard-2.20.0\n", + " Attempting uninstall: tensorflow\n", + " Found existing installation: tensorflow 2.20.0\n", + " Uninstalling tensorflow-2.20.0:\n", + " Successfully uninstalled tensorflow-2.20.0\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.3 which is incompatible.\n", + "opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", + "opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", + "pytensor 2.35.1 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", + "umap-learn 0.5.9.post2 requires scikit-learn>=1.6, but you have scikit-learn 1.4.1.post1 which is incompatible.\n", + "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", + "orbax-checkpoint 0.11.28 requires jax>=0.6.0, but you have jax 0.5.3 which is incompatible.\n", + "tensorflow-decision-forests 1.12.0 requires tensorflow==2.19.0, but you have tensorflow 2.19.1 which is incompatible.\n", + "flax 0.10.7 requires jax>=0.6.0, but you have jax 0.5.3 which is incompatible.\n", + "shap 0.50.0 requires numpy>=2, but you have numpy 1.26.4 which is incompatible.\n", + "imbalanced-learn 0.14.0 requires scikit-learn<2,>=1.4.2, but you have scikit-learn 1.4.1.post1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed numpy-1.26.4 tensorboard-2.19.0 tensorflow-2.19.1\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "numpy", + "tensorflow" + ] + }, + "id": "d3a167bbbde043ef9a994c35060fda79" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **RESTART THE SESSION**\n", + "\n", + "Then proceed to the next cell which imports all necessary libraries and defines global constants and hyperparameters for the entire pipeline.\n" + ], + "metadata": { + "id": "v69rLBcmXyGD" + } + }, + { + "cell_type": "code", + "source": [ + "! ls" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ubtKyfBQzFEW", + "outputId": "6cbe44e6-3ce7-4227-982a-88d0d36d2205" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "cerebros-core-algorithm-alpha sample_data\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 1. # **ONLY IF** the directory cerebros-core-algorithm-alpha is not still\n", + "# there, clone the directory again.\n", + "# ! git clone https://github.com/david-thrower/cerebros-core-algorithm-alpha.git\n", + "\n", + "# 2. Set the working directory (in the new session) - DO run this.\n", + "%cd cerebros-core-algorithm-alpha" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NemXTsYgfE0s", + "outputId": "ca92342f-1f82-42ee-8562-980b1c8dd849" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/cerebros-core-algorithm-alpha\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Verify we are in the right place:\n", + "! pwd" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "D3K4dSVQhrIc", + "outputId": "5a45fa94-1bb3-46ce-c362-27f456221fd6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/cerebros-core-algorithm-alpha\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Standard library imports\n", + "import subprocess\n", + "import time\n", + "from gc import collect\n", + "\n", + "# Third-party library imports\n", + "import tensorflow as tf\n", + "import pandas as pd\n", + "import pendulum\n", + "from transformers import AutoTokenizer\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Cerebros specific imports\n", + "from cerebros.units.units import DenseUnit\n", + "from cerebros.simplecerebrosrandomsearch.simple_cerebros_random_search import SimpleCerebrosRandomSearch\n", + "from cerebros.denseautomlstructuralcomponent.dense_automl_structural_component import (\n", + " zero_7_exp_decay,\n", + " zero_95_exp_decay,\n", + " simple_sigmoid\n", + ")\n", + "from cerebrosllmutils.llm_utils import (\n", + " prepare_data,\n", + " InterleavedRoPE,\n", + " Perplexity,\n", + " CerebrosNotGPTConfig,\n", + " CerebrosNotGPT,\n", + " WarmupCosineDecayRestarts\n", + ")\n", + "\n", + "# Import the data source: Format List[str]\n", + "from vanilladatasets.web_english_bible import samples as bible\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WKCdCv96X4YX", + "outputId": "875f6626-4f4b-426c-c697-da9f186e440a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/jaxlib/plugin_support.py:71: RuntimeWarning: JAX plugin jax_cuda12_plugin version 0.7.2 is installed, but it is not compatible with the installed jaxlib version 0.5.3, so it will not be used.\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Data and Training Constants\n", + "\n", + "These parameters control the amount of data used and the behavior of the training stages.\n", + "\n", + "- **PHASE_I_A_SAMPLES_TO_CREATE**: Size of the subset of the dataset used for the NAS (Neural Architecture Search) stage (number of text samples).\n", + "- **PHASE_I_B_SAMPLES_TO_CREATE**: Number of samples to use for the main training task stage after Neural Architecture Search is completed.\n", + "- **PHASE_I_B_VAL_SPLIT**: Fraction of data for validation in Phase I-b.\n", + "- **PHASE_I_B_SAMPLE_EXPANSION_BATCH_SIZE**: Batch size for preprocessing in Phase I-b to manage RAM.\n", + "- **PROMPT_LENGTH**: Number of tokens provided to the model to predict the next token. It is recommended to keep this as 1.\n" + ], + "metadata": { + "id": "rK0LZP7KbQqm" + } + }, + { + "cell_type": "code", + "source": [ + "# Samples to use for the neural architecture search stage\n", + "PHASE_I_A_SAMPLES_TO_CREATE = 10\n", + "\n", + "# Samples to use for the main training stage\n", + "PHASE_I_B_SAMPLES_TO_CREATE = 20\n", + "PHASE_I_B_VAL_SPLIT = 0.15\n", + "\n", + "# For Stage I-b, we preprocess in batches to avoid high RAM usage.\n", + "PHASE_I_B_SAMPLE_EXPANSION_BATCH_SIZE = 10\n", + "\n", + "# How many tokens to provide before expecting the next token to be predicted.\n", + "PROMPT_LENGTH = 1\n" + ], + "metadata": { + "id": "vywbZQxAZC9R" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Model and Embedding Constants\n", + "\n", + "These constants define the size and shape of the model's text processing components.\n", + "\n", + "- **MAX_SEQ_LENGTH**: The maximum sequence length the model will handle. This has a linear relationship with RAM/CPU usage.\n", + "- **tokenizer_checkpoint**: The Hugging Face model to use for tokenization.\n", + "- **EMBEDDING_N**: A factor to determine the embedding dimensionality (EMBEDDING_DIM = EMBEDDING_N * 2). A factor to determine the embedding dimensionality (EMBEDDING_DIM = EMBEDDING_N * 2). The resulting embedding dimensionality (EMBEDDING_DIM) for InterleavedRoPE must be an even number. Using this parameter as a proxy, rather than setting EMBEDDING_DIM directly, acts as a guard rail to ensure this constraint is met.\n", + "- **PROJECTION_N**: Controls the size of a projection layer after embedding. Increasing this value can significantly increase RAM usage.\n" + ], + "metadata": { + "id": "5jK5wbA5b8se" + } + }, + { + "cell_type": "code", + "source": [ + "# Text encoding / embedding related constants\n", + "MAX_SEQ_LENGTH = 40\n", + "\n", + "# Tokenization\n", + "tokenizer_checkpoint = \"HuggingFaceTB/SmolLM3-3B\"\n", + "tokenizer = AutoTokenizer.from_pretrained(tokenizer_checkpoint)\n", + "\n", + "# Add special tokens for potential instruction-following formats\n", + "special_tokens = {\n", + " \"additional_special_tokens\": [\"\", \"\", \"\", \"\"]\n", + "}\n", + "tokenizer.add_special_tokens(special_tokens)\n", + "\n", + "VOCABULARY_SIZE = len(tokenizer)\n", + "\n", + "# For InterleavedRoPE, the embedding output dim must be an even number.\n", + "EMBEDDING_N = 6\n", + "EMBEDDING_DIM = int(EMBEDDING_N * 2)\n", + "\n", + "# Size of the projection layer. Keep low to manage RAM.\n", + "PROJECTION_N = 1\n" + ], + "metadata": { + "id": "4Kka_A4tb3aJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6c85d1ae-52f4-4ddf-d768-ea5781b1b7da" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Stage I-a (NAS) Hyperparameters\n", + "\n", + "These parameters control the Neural Architecture Search process.\n", + "\n", + "- **moities_to_try**: Number of different layer permutations to try.\n", + "- **tries_per_moity**: Number of topologies to try for each permutation.\n", + "- **epochs, batch_size, learning_rate**: Standard training parameters for the NAS stage.\n", + "- **predecessor_level_connection_affinity_factor_first**: Controls connectivity density between the Input layer and the first level of Dense layers.\n", + "- **predecessor_level_connection_affinity_factor_main**: Controls connectivity density between the Input layer and the first level of Dense layers and the subsequent level of Dense layers, as well as all subsequent vertical connectivity.\n", + "- **p_lateral_connection, num_lateral_connection_tries_per_unit**: Control the density of lateral connectivity between Dense layers on the same row.\n", + "- **minimum_levels, maximum_levels**: Number of **rows of** Dense layers in the architecture grid.\n", + "- **minimum_units_per_level, maximum_units_per_level**: Number of Dense layers per row.\n", + "- **minimum_neurons_per_unit, maximum_neurons_per_unit**: The number of neurons for each Dense layer unit.\n" + ], + "metadata": { + "id": "MeoWtePacWz_" + } + }, + { + "cell_type": "code", + "source": [ + "# Cerebros [non-HP-tunable] configurables for NAS\n", + "moities_to_try = 3\n", + "tries_per_moity = 1\n", + "\n", + "### Main tunable hyperparameters for NAS ##\n", + "\n", + "POSITIONAL_EMBEDDING_DROPOUT = 0.7651951380000674\n", + "activation = 'softplus'\n", + "\n", + "# Vertical connectivity hyperparameters\n", + "predecessor_level_connection_affinity_factor_first = 17.851026458010523\n", + "predecessor_level_connection_affinity_factor_main = 21.487301631581428\n", + "\n", + "# Lateral connectivity hyperparameters\n", + "max_consecutive_lateral_connections = 7\n", + "p_lateral_connection = 0.24927354102044022\n", + "num_lateral_connection_tries_per_unit = 32\n", + "learning_rate = 0.003025583248301791\n", + "epochs = 41\n", + "batch_size = 5\n", + "gradient_accumulation_steps = 4\n", + "\n", + "# Architecture grid constraints\n", + "minimum_levels = 2\n", + "maximum_levels = 2\n", + "minimum_units_per_level = 2\n", + "maximum_units_per_level = 2\n", + "minimum_neurons_per_unit = 2\n", + "maximum_neurons_per_unit = 2\n" + ], + "metadata": { + "id": "Wbowkxnbc4Zd" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Stage I-b (Extended Training) Hyperparameters\n", + "\n", + "These parameters are for fine-tuning the best model from Stage I-a.\n", + "\n", + "- INITIAL_LR_STAGE_I_B: Initial learning rate for this phase.\n", + "- WARMUP_EPOCHS_STAGE_I_B, WARMUP_STEPS: Parameters for the learning rate scheduler.\n", + "- phase_i_b_epochs: Number of epochs for extended training.\n", + "- phase_i_b_weight_decay: Weight decay for the optimizer.\n" + ], + "metadata": { + "id": "fcGTs9ASdXps" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "## Training Stage I-b parameters:\n", + "INITIAL_LR_STAGE_I_B = 0.0039295722955565125\n", + "WARMUP_EPOCHS_STAGE_I_B = 7\n", + "WARMUP_STEPS = 1140\n", + "FIRST_DECAY_STEPS_STAGE_I_B = 1900\n", + "phase_i_b_epochs = 53\n", + "phase_i_b_gradient_accumulation_steps = 7\n", + "phase_i_b_weight_decay = 0.01647018768215773 # For AdamW\n" + ], + "metadata": { + "id": "-znwaddIdiKU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "# Generation Constants\n", + "\n", + "Parameters used during the text generation evaluation phase." + ], + "metadata": { + "id": "vy5y6OXhdvzV" + } + }, + { + "cell_type": "code", + "source": [ + "## Generation time configurables:\n", + "GENERATION_PROMPT_LEN = 25\n", + "MAX_NEW_TOKENS = MAX_SEQ_LENGTH - GENERATION_PROMPT_LEN" + ], + "metadata": { + "id": "JHjCz9qXd5Gq" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# **Data Preparation**\n", + "\n", + "Here, we load and subset the dataset for both training Stages.\n", + "\n", + "\n", + "We first split the Bible text samples into two sets: one for Phase I-a (NAS) and a larger one for Phase I-b (extended training).\n" + ], + "metadata": { + "id": "N7fJIZ1md-0Y" + } + }, + { + "cell_type": "code", + "source": [ + "# Get training data from the bible text samples\n", + "non_instruct_samples = bible[:PHASE_I_A_SAMPLES_TO_CREATE]\n", + "phase_i_b_samples = bible[PHASE_I_A_SAMPLES_TO_CREATE:PHASE_I_B_SAMPLES_TO_CREATE + PHASE_I_A_SAMPLES_TO_CREATE]\n", + "\n", + "print(f\"Samples from KJV bible consisting of {len(non_instruct_samples)} look like this (sub-sample of 3): {non_instruct_samples[:3]}\")\n" + ], + "metadata": { + "id": "jIFxWcBzeLjN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d46f8e34-3d7d-4fb4-dddc-bf1c45bae7ee" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Samples from KJV bible consisting of 10 look like this (sub-sample of 3): ['In the beginning God created the heavens and the earth.', \"The earth was formless and empty, with darkness over the deep and God's Spirit hovering over the waters.\", \"God said, 'Let there be light,' and there was light.\"]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Preprocess Data for Phase I-a (NAS)\n", + "\n", + "The Cerebros LLM is a single head model. This means that each time the model is called, it returns only the next token. It does not regurgitate the cumulative sequence, nor does it have a separate head for each position in the sequence.\n", + "\n", + "For both training stages, each text sample is expanded into multiple input/label pairs, which we call \"sub-samples.\" There is one \"sub-sample\" for each token in the range between the first token and the first occurrence of a padding token or the end of the sequence, whichever comes first.\n", + "\n", + "For example, the sequence [t1, t2, t3] becomes:\n", + "\n", + " Input: [t1, 2, 2, 2] Label: [t2] # One hot encoded to VOCABULARY_SIZE\n", + " Input: [t1, t2, 2, 2], Label: [t3]\n", + " Input: [t1, t2, t3, 2], Label: [2]\n", + "\n", + "For training Stage 1-a, we perform the entire expansion for its training data in memory. This is because the NAS does not yet support a tf.data.Dataset object. In the future, we may retrofit the NAS algorithm to support streaming preprocessing as well, allowing us to use a larger dataset for the NAS.\n", + "\n", + "For stage I-b, the extended training stage, the same operation is done in batches. This is because this operation significantly increases the amount of memory required. The main reason for this is the one-hot encoded label, where the vocabulary size is 128,260. Since we do this in batches, this allows for a virtually unlimited number of samples to be processed.\n", + "\n", + "For reference, this is the preprocessing being applied:\n", + "\n", + "```python\n", + "def prepare_data(\n", + " data_0: List[str],\n", + " tokenizer_0: Any,\n", + " max_seq_length: int = 1024,\n", + " prompt_length: int = 1) -> Tuple[List[List[int]], List[List[int]], int]:\n", + "\n", + "\n", + " all_input_ids = []\n", + " all_labels = []\n", + "\n", + " pad_token_id = tokenizer_0.pad_token_id\n", + "\n", + " # Tokenize all data at once for efficiency\n", + " tokenized_data = tokenizer_0(\n", + " data_0,\n", + " max_length=max_seq_length,\n", + " padding='max_length',\n", + " truncation=True,\n", + " add_special_tokens=False # We'll handle special tokens manually\n", + " )\n", + " vocab_size = len(tokenizer_0)\n", + "\n", + " # Get the token ID for \n", + " end_prompt_token_id = tokenizer_0.encode(\"\", add_special_tokens=False)[0]\n", + "\n", + " # Process each sample\n", + " for sample_tokens in tokenized_data['input_ids']:\n", + " # Find the index of token\n", + " try:\n", + " end_prompt_index = sample_tokens.index(end_prompt_token_id)\n", + " except ValueError:\n", + " # If not found, treat sample as a non-instruct sample\n", + " end_prompt_index = (\n", + " prompt_length - 1) # int(np.ceil(len(sample_tokens) * (1/3))) # 0 ## 1. Give it a fair starting place to predict the next word 2. reduce the number of expanded samples\n", + "\n", + " # Find first pad token after \n", + " first_pad_index = None\n", + " for i in range(end_prompt_index + 1, len(sample_tokens)):\n", + " if sample_tokens[i] == pad_token_id:\n", + " first_pad_index = i\n", + " break\n", + "\n", + " # If no pad token found, use the end of sequence\n", + " if first_pad_index is None:\n", + " first_pad_index = len(sample_tokens)\n", + "\n", + " # Apply sliding window from after to first pad token\n", + " # Start from end_prompt_index + 1 (first token to predict)\n", + " # End at first_pad_index - 1 (last token to predict)\n", + " for i in range(end_prompt_index + 1, first_pad_index):\n", + " # Input: from start up to (but not including) token i\n", + " input_ids = sample_tokens[:i]\n", + "\n", + " # Pad or truncate to max_seq_length\n", + " if len(input_ids) > max_seq_length:\n", + " input_ids = input_ids[:max_seq_length]\n", + " else:\n", + " input_ids = input_ids + [pad_token_id] * (max_seq_length - len(input_ids))\n", + "\n", + " # Label: one-hot encoding of token at position i\n", + " next_token = sample_tokens[i]\n", + " label = [0] * vocab_size\n", + " label[next_token] = 1\n", + "\n", + " all_input_ids.append(input_ids)\n", + " all_labels.append(label)\n", + "\n", + " # Add final sample with pad token as label to indicate termination\n", + " if first_pad_index < len(sample_tokens): # Only if there's actually a pad token\n", + " input_ids = sample_tokens[:first_pad_index]\n", + "\n", + " # Pad or truncate to max_seq_length\n", + " if len(input_ids) > max_seq_length:\n", + " input_ids = input_ids[:max_seq_length]\n", + " else:\n", + " input_ids = input_ids + [pad_token_id] * (max_seq_length - len(input_ids))\n", + "\n", + " # Label: one-hot encoding of pad token\n", + " label = [0] * vocab_size\n", + " label[pad_token_id] = 1\n", + "\n", + " all_input_ids.append(input_ids)\n", + " all_labels.append(label)\n", + "\n", + " return all_input_ids, all_labels, vocab_size\n", + "```\n" + ], + "metadata": { + "id": "8Tu8X9cVeQVD" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Preprocess data for Stage I-a training\n", + "x, y, vocab_size = prepare_data(data_0=non_instruct_samples,\n", + " tokenizer_0=tokenizer,\n", + " max_seq_length=MAX_SEQ_LENGTH,\n", + " prompt_length=PROMPT_LENGTH)\n", + "\n", + "# Split the preprocessed data for NAS training and validation\n", + "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.85, shuffle=False)\n", + "\n", + "# Package data into lists for the Cerebros AutoML component\n", + "x_train_tf = tf.constant(X_train, tf.int32)\n", + "y_train_tf = tf.constant(y_train, tf.float32)\n", + "x_train_packaged = [x_train_tf]\n", + "y_train_packaged = [y_train_tf]\n", + "\n", + "# Do the same for the validation data\n", + "x_test_tf = tf.constant(X_test, tf.int32)\n", + "y_test_tf = tf.constant(y_test, tf.float32)\n", + "x_test_packaged = [x_test_tf]\n", + "y_test_packaged = [y_test_tf]\n", + "\n", + "# Define input and output shapes for the AutoML model\n", + "INPUT_SHAPES = [(MAX_SEQ_LENGTH,)]\n", + "OUTPUT_SHAPES = [(VOCABULARY_SIZE)]\n" + ], + "metadata": { + "id": "EDyuTMLufYvs" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Train, Test Split of the Data for Stage I-b training\n", + "\n", + "We split the larger Phase I-b dataset into training and validation sets. Again, this dataset will be processed by a streaming generator in batches to avoid memory saturation and make the training more scalable. We will revisit that later." + ], + "metadata": { + "id": "zX60zcpykasl" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Split the phase I-b data set for training and validation\n", + "phase_i_b_train_samples, phase_i_b_val_samples = train_test_split(\n", + " phase_i_b_samples,\n", + " test_size=PHASE_I_B_VAL_SPLIT,\n", + " shuffle=False\n", + ")\n" + ], + "metadata": { + "id": "SMSdkFRPkg7D" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "phase_i_b_train_samples[:3]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Oqw-T7bOo1GD", + "outputId": "2e8f24fc-24c2-4a06-babb-550b676b7751" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[\"God said, 'Let the earth produce vegetation, seed-bearing plants, and fruit trees, each according to its kind,' and it was so.\",\n", + " 'The earth brought forth grass, seed-bearing herbs, and fruit trees, each with its seed, and God saw that it was good.',\n", + " 'There was evening and morning, the third day.']" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "source": [ + "X_train[:2]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Hv_52izIjOQ7", + "outputId": "e2972924-0190-4f16-9317-c00100486203" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[[644,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012],\n", + " [644,\n", + " 279,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012]]" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Base Text Embedding Model Definition\n", + "\n", + "- Before we run the NAS, we define a base model that handles token embeddings and positional embeddings.\n", + "- The SimpleCerebrosRandomSearch will then attach its auto-generated lattice of dense layers on top of this base model.\n", + "- The Cerebros NAS takes an init parameter base_models: List[tf.keras.Model]\n" + ], + "metadata": { + "id": "11Ri4PtKktih" + } + }, + { + "cell_type": "code", + "source": [ + "####### Text embedding base model #####################\n", + "\n", + "inp = tf.keras.layers.Input(shape=(MAX_SEQ_LENGTH,), dtype=tf.int32)\n", + "\n", + "# Token embedding layer\n", + "embedded = tf.keras.layers.Embedding(\n", + " input_dim=VOCABULARY_SIZE,\n", + " output_dim=EMBEDDING_DIM,\n", + " input_length=MAX_SEQ_LENGTH,\n", + " mask_zero=False\n", + ")(inp)\n", + "\n", + "# Interleaved Rotary Positional Embedding (iRoPE)\n", + "position_embedding = InterleavedRoPE(\n", + " dim=EMBEDDING_DIM,\n", + " max_seq_len=MAX_SEQ_LENGTH,\n", + ")(embedded)\n", + "\n", + "# Concatenate token and positional embeddings\n", + "x = tf.keras.layers.Concatenate()([embedded, position_embedding])\n", + "x = tf.keras.layers.Dropout(POSITIONAL_EMBEDDING_DROPOUT)(x)\n", + "\n", + "# Flatten and project to the desired dimension\n", + "flattened = tf.keras.layers.Flatten()(x)\n", + "projected = tf.keras.layers.Dense(EMBEDDING_DIM * PROJECTION_N)(flattened)\n", + "\n", + "# Create the base Keras model\n", + "cerebros_base_model = tf.keras.Model(\n", + " inputs=inp,\n", + " outputs=projected\n", + ")\n" + ], + "metadata": { + "id": "tn1qrGISn_Pe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e76e091c-6e7f-4820-ef79-15143f1e6b64" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## FYI: The iRoPE Embedding:\n", + "\n", + "The RoPE embedding, and helper functions it depends on (previously imported from the local package cerebrosllmutils):\n", + "\n", + "- iRoPE: Interleaved Rotary Positional Embedding\n", + "- RoPE: Rotary Positional Embedding\n", + "- The Rotary Positional Embedding expresses positional relationships as angles, extends feasible context window.\n", + "- iRoPE: iRoPE applies the rotation in an interleaved manner and enables capturing more nuance and extending context windows feasible to around 2 million tokens.\n", + "\n", + "```python\n", + "# --- Base Rotary Positional Embedding\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='RotaryEmbedding')\n", + "class RotaryEmbedding(tf.keras.layers.Layer):\n", + " def __init__(self, dim, max_seq_len=1024, temperature=10000.0, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.dim = dim\n", + " # Ensure dim is even right at initialization\n", + " if self.dim % 2 != 0:\n", + " raise ValueError(f\"Embedding dimension `dim` ({self.dim}) must be even for RotaryEmbedding.\")\n", + " self.max_seq_len = max_seq_len\n", + " self.temperature = temperature\n", + " # *** No calculation or storage of inv_freq here or in build ***\n", + "\n", + " def build(self, input_shape):\n", + " # Build should primarily be for creating trainable weights, which we don't have.\n", + " # Call super().build() for Keras compatibility.\n", + " super().build(input_shape)\n", + "\n", + " def call(self, x): # Removed seq_len argument, calculate from x\n", + " shape = tf.shape(x)\n", + " batch_size = shape[0]\n", + " actual_seq_len = shape[1]\n", + "\n", + " # *** Calculate inv_freq inside call ***\n", + " inv_freq_base = tf.range(0, self.dim, 2, dtype=tf.float32)\n", + " inv_freq = 1.0 / (self.temperature ** (inv_freq_base / self.dim))\n", + " # Ensure inv_freq has the correct shape [dim/2]\n", + " inv_freq = tf.cast(inv_freq, dtype=x.dtype) # Match dtype early\n", + "\n", + " # Use actual_seq_len for calculations\n", + " position = tf.range(actual_seq_len, dtype=x.dtype) # Match dtype\n", + "\n", + " # Calculate sinusoid input using einsum or broadcasting\n", + " # Einsum approach: Ensure correct dimensions [seq_len, dim/2]\n", + " sinusoid_inp = tf.einsum(\"i,j->ij\", position, inv_freq)\n", + "\n", + " # Calculate sin and cos based on the actual sequence length\n", + " sin = tf.sin(sinusoid_inp)\n", + " cos = tf.cos(sinusoid_inp)\n", + "\n", + " # Repeat sin/cos for interleaving: [a, b] -> [a, a, b, b]\n", + " # Result needs shape [actual_seq_len, dim]\n", + " sin = tf.repeat(sin, 2, axis=-1)\n", + " cos = tf.repeat(cos, 2, axis=-1)\n", + "\n", + " # Expand dims for batch and tile\n", + " # Output shape needs to be [batch_size, actual_seq_len, dim]\n", + " # Add batch dimension: [1, actual_seq_len, dim]\n", + " sin = tf.expand_dims(sin, axis=0)\n", + " cos = tf.expand_dims(cos, axis=0)\n", + "\n", + " # Tile to match the batch size: [batch_size, actual_seq_len, dim]\n", + " sin = tf.tile(sin, [batch_size, 1, 1])\n", + " cos = tf.tile(cos, [batch_size, 1, 1])\n", + "\n", + " # Casting to x.dtype was already done for inv_freq, sin/cos will inherit\n", + " # sin = tf.cast(sin, x.dtype) # Already done via calculation chain\n", + " # cos = tf.cast(cos, x.dtype) # Already done via calculation chain\n", + "\n", + " # Return sin and cos needed by InterleavedRoPE\n", + " return sin, cos\n", + "\n", + " def get_config(self):\n", + " config = super().get_config()\n", + " config.update({\n", + " \"dim\": self.dim,\n", + " \"max_seq_len\": self.max_seq_len,\n", + " \"temperature\": self.temperature,\n", + " })\n", + " return config\n", + "\n", + " @classmethod\n", + " def from_config(cls, config):\n", + " return cls(**config)\n", + "\n", + "\n", + "# iRoPE helper functions\n", + "\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='split_alternate')\n", + "def split_alternate(x):\n", + " shape = tf.shape(x)\n", + " x = tf.reshape(x, [shape[0], shape[1], shape[2] // 2, 2])\n", + " x = tf.transpose(x, [0, 1, 3, 2])\n", + " x = tf.reshape(x, [shape[0], shape[1], -1])\n", + " return x\n", + "\n", + "\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='rotate_half')\n", + "def rotate_half(x):\n", + " x = split_alternate(x)\n", + " d = tf.shape(x)[-1]\n", + " rotated_x = tf.concat([-x[..., d // 2:], x[..., :d // 2]], axis=-1)\n", + " return tf.reshape(rotated_x, tf.shape(x))\n", + "\n", + "\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='apply_rotary_pos_emb')\n", + "def apply_rotary_pos_emb(x, sin, cos):\n", + " cos = tf.reshape(cos, [tf.shape(cos)[0], tf.shape(cos)[1], -1])\n", + " sin = tf.reshape(sin, [tf.shape(sin)[0], tf.shape(sin)[1], -1])\n", + " x_rotated = x * cos + rotate_half(x) * sin\n", + " return x_rotated\n", + "\n", + "\n", + "# interleaved Rotary Postional Embedding (iRoPE)\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='InterleavedRoPE')\n", + "class InterleavedRoPE(tf.keras.layers.Layer):\n", + " def __init__(self, dim, max_seq_len=1024, **kwargs):\n", + " super().__init__(**kwargs)\n", + " if dim % 2 != 0:\n", + " raise ValueError(f\"Embedding dimension `dim` ({dim}) must be even for InterleavedRoPE.\")\n", + " self.dim = dim\n", + " self.max_seq_len = max_seq_len\n", + " # Instantiate the RotaryEmbedding layer\n", + " # Ensure the name is consistent if needed for saving/loading\n", + " self.rotary_emb = RotaryEmbedding(dim, max_seq_len, name=\"rotary_embedding\")\n", + "\n", + " def call(self, x):\n", + " # Get sin and cos from the RotaryEmbedding layer's call method\n", + " # *** Pass only 'x'. RotaryEmbedding calculates seq_len internally. ***\n", + " sin, cos = self.rotary_emb(x)\n", + "\n", + " # Apply the positional embeddings\n", + " x_embedded = apply_rotary_pos_emb(x, sin, cos)\n", + " return x_embedded\n", + "\n", + " def get_config(self):\n", + " config = super().get_config()\n", + " config.update({\n", + " \"dim\": self.dim,\n", + " \"max_seq_len\": self.max_seq_len,\n", + " })\n", + " # Keras handles nested layer serialization automatically\n", + " return config\n", + "\n", + " @classmethod\n", + " def from_config(cls, config):\n", + " # Keras handles nested layer restoration automatically\n", + " return cls(**config)\n", + "```" + ], + "metadata": { + "id": "CXtYv20vpkMY" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Custom metric Perplexity (previously imported from the local package cerebrosllmutils):\n", + "\n", + "Since there is not a Perplexity metric in tensorflow.keras.metrics, we created our own, and one designed for this single - head model.\n", + "\n", + "## This is what it looks like:\n", + "\n", + "```python\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='Perplexity')\n", + "class Perplexity(tf.keras.metrics.Metric):\n", + " \"\"\"\n", + " Computes perplexity, defined as e^(categorical crossentropy).\n", + " \"\"\"\n", + "\n", + " def __init__(self, name='perplexity', **kwargs):\n", + " super().__init__(name=name, **kwargs)\n", + " self.total_crossentropy = self.add_weight(name='total_crossentropy', initializer='zeros')\n", + " self.count = self.add_weight(name='count', initializer='zeros')\n", + "\n", + " def update_state(self, y_true, y_pred, sample_weight=None):\n", + " # Calculate categorical crossentropy\n", + " crossentropy = tf.keras.losses.categorical_crossentropy(y_true, y_pred)\n", + "\n", + " # Update the running sum of crossentropy and the count of samples\n", + " self.total_crossentropy.assign_add(tf.reduce_sum(crossentropy))\n", + " self.count.assign_add(tf.cast(tf.shape(y_true)[0], dtype=tf.float32))\n", + "\n", + " def result(self):\n", + " # Compute the average crossentropy\n", + " average_crossentropy = self.total_crossentropy / self.count\n", + " # Compute perplexity as e^(average crossentropy)\n", + " return tf.exp(average_crossentropy)\n", + "\n", + " def reset_state(self):\n", + " # Reset the state variables\n", + " self.total_crossentropy.assign(0.0)\n", + " self.count.assign(0.0)\n", + "```\n" + ], + "metadata": { + "id": "uN3adqRLo61X" + } + }, + { + "cell_type": "code", + "source": [ + "# Custom metric: Perplexity\n", + "perplexity_metric = Perplexity()" + ], + "metadata": { + "id": "_8uTBW_to7iQ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "# Stage I-a training: Neural Architecture Search (NAS)\n", + "\n", + "We now run the SimpleCerebrosRandomSearch to find the best performing architecture based on the training data and the base model. The search aims to minimize the perplexity in the train set. The search aims to minimize the perplexity in the training set. Obviously, in a full - scale run, we would use the validation set's value.\n", + "\n", + "- The Cerebros NAS will parse a block composed of rows (Levels) of multiple Dense layers (Units) with an overlapping, interleaved, interwoven topology both laterally between Dense layers on the same row and vertically between layers on different levels.\n", + "- This topology emulates the neuroscience principle of modularity.\n", + "- This topology allows local clusters of densely connected neurons to learn specialized fragments of a problem, while allowing efficient communication between these clusters to coordinate among themselves to compose a solution to a complex problem.\n", + "\n", + "For the deep technical details of how Cerebros NAS works: [How Cerebros NAS Works](https://github.com/david-thrower/cerebros-core-algorithm-alpha/blob/277-attempt-to-imporve-parameters-on--dev-branch-275/documentation/cerebros-technical-details.md)\n", + "\n", + "## This is what a neural network parsed by Cerebros looks like:\n", + "\n", + "- Green triangles: Input layers\n", + "- Blue squares: Concatenate layer -> [BatchNormalization | Dropout]\n", + "- Pink ovals: Hidden Dense layers\n", + "- Red oval: Output Dense layer\n" + ], + "metadata": { + "id": "tWjbHiHRMhR4" + } + }, + { + "cell_type": "markdown", + "source": [ + "![Brain-lookalike1.png](data:image/png;base64,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)" + ], + "metadata": { + "id": "1wR8EVItNNh_" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "## For a more readable view of that this looks like\n", + "\n", + "![image.png](data:image/png;base64,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)\n" + ], + "metadata": { + "id": "_bXR1QxaLPiq" + } + }, + { + "cell_type": "code", + "source": [ + "######## Instantiate Cerebros Neural Architecture Search #######\n", + "\n", + "# Project metadata\n", + "TIME = pendulum.now(tz='America/New_York').__str__()[:16].replace('T', '_').replace(':', '_').replace('-', '_')\n", + "PROJECT_NAME = f'{TIME}_cerebros_not-gpt'\n", + "meta_trial_number = 42\n", + "\n", + "# Initialize the AutoML search\n", + "cerebros_automl = SimpleCerebrosRandomSearch(\n", + " unit_type=DenseUnit,\n", + " input_shapes=INPUT_SHAPES,\n", + " output_shapes=OUTPUT_SHAPES,\n", + " training_data=x_train_packaged,\n", + " labels=y_train_packaged,\n", + " validation_split=0.2,\n", + " direction='minimize',\n", + " metric_to_rank_by=\"perplexity\",\n", + " minimum_levels=minimum_levels,\n", + " maximum_levels=maximum_levels,\n", + " minimum_units_per_level=minimum_units_per_level,\n", + " maximum_units_per_level=maximum_units_per_level,\n", + " minimum_neurons_per_unit=minimum_neurons_per_unit,\n", + " maximum_neurons_per_unit=maximum_neurons_per_unit,\n", + " activation=activation,\n", + " final_activation='softmax',\n", + " number_of_architecture_moities_to_try=moities_to_try,\n", + " number_of_tries_per_architecture_moity=tries_per_moity,\n", + " predecessor_level_connection_affinity_factor_first=predecessor_level_connection_affinity_factor_first,\n", + " predecessor_level_connection_affinity_factor_main=predecessor_level_connection_affinity_factor_main,\n", + " predecessor_level_connection_affinity_factor_decay_main=zero_7_exp_decay,\n", + " max_consecutive_lateral_connections=max_consecutive_lateral_connections,\n", + " p_lateral_connection=p_lateral_connection,\n", + " p_lateral_connection_decay=zero_95_exp_decay,\n", + " num_lateral_connection_tries_per_unit=num_lateral_connection_tries_per_unit,\n", + " learning_rate=learning_rate,\n", + " loss=tf.keras.losses.CategoricalCrossentropy(),\n", + " metrics=[tf.keras.metrics.CategoricalAccuracy(), perplexity_metric],\n", + " epochs=epochs,\n", + " project_name=f\"{PROJECT_NAME}_meta_{meta_trial_number}\",\n", + " model_graphs='model_graphs',\n", + " batch_size=batch_size,\n", + " gradient_accumulation_steps=gradient_accumulation_steps,\n", + " meta_trial_number=meta_trial_number,\n", + " base_models=[cerebros_base_model],\n", + " train_data_dtype=tf.int32\n", + ")" + ], + "metadata": { + "id": "XV2q_5WEwBJ0" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Run the Cerebros Neural Architecture Search\n" + ], + "metadata": { + "id": "TJVLfmJ2virA" + } + }, + { + "cell_type": "code", + "source": [ + "cerebros_t0 = time.time()\n", + "phase_i_a_result_0 = cerebros_automl.run_random_search()\n", + "cerebros_t1 = time.time()\n", + "\n", + "# Report results\n", + "cerebros_time_all_models_min = (cerebros_t1 - cerebros_t0) / 60\n", + "models_tried = moities_to_try * tries_per_moity\n", + "cerebros_time_per_model = cerebros_time_all_models_min / models_tried\n", + "phase_i_a_result = float(phase_i_a_result_0)\n", + "\n", + "print(f\"Cerebros trained {models_tried} models in {cerebros_time_all_models_min:.2f} min. Average time per model: {cerebros_time_per_model:.2f} min.\")\n", + "print(f'Cerebros best perplexity achieved in Phase I-a is {phase_i_a_result}')" + ], + "metadata": { + "id": "ulL0EGnow5L7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "d56dd1ec-2f7b-4a3c-ecc6-75e595910367" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\rGlobal task progress: 0%|\u001b[38;2;22;206;235m \u001b[0m| 0/3 [00:00nnf>ceil\n", + "k is: 0 value is: [{'1': }]\n", + "0\n", + "k is: 1 value is: [{'2': }, {'2': }]\n", + "1\n", + "Trying to create level 1\n", + "We think level 1's predecessors are: [0]\n", + "k is: 2 value is: [{'128260': }]\n", + "2\n", + "Trying to create Final level 2\n", + "Trying to create level 2\n", + "We think level final level 2's predecessors are: [0, 1]\n", + "levels:\n", + "[0, 1, 2]\n", + "{'0': 'InputUnitModule'}\n", + "InputLevel.input_shapes [(40,)]\n", + "{'2': }\n", + "{'2': }\n", + "Debug: I am 2 selecting 1\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "Setting levels_unmaterialized[0] level_number 0 to have first successor: levels_unmaterialized[:1], having level_numbers of [1, 2]\n", + "Setting levels_unmaterialized[1] level_number 1 to have first successor: levels_unmaterialized[:2], having level_numbers of [2]\n", + "Debug: successor_connectivity_errors_2d []\n", + "$$$$$$>>>>> Base model: \n", + "InputUnit.input_shape: (40,)\n", + "{'2': }\n", + "{'2': }\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "Debug: successor_connectivity_errors_2d []\n", + "Debug: successor_connectivity_errors_2d []\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_0_DenseLevel_0000000000000001_tr_0_DenseUnit_0000000000000001_tr_0_0 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_0_DenseLevel_0000000000000001_tr_0_DenseUnit_0000000000000001_tr_0_1 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "{'128260': }\n", + "debug: meta_level_number\n", + "Debug: successor_connectivity_errors_2d []\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_0_FinalDenseLevel_0000000000000002_tr_0_FinalDenseUnit_0000000000000002_tr_0_0 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "inputs\n", + "\n", + "\n", + "outputs\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"NeuralNetworkFuture_0000000000000nan_tr_0_nn_materialized\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"NeuralNetworkFuture_0000000000000nan_tr_0_nn_materialized\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ - โ”‚\n", + "โ”‚ (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ functional โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m) โ”‚ \u001b[38;5;34m1,550,652\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mFunctional\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m1,584\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128260\u001b[0m) โ”‚ \u001b[38;5;34m50,919,220\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ], + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+              "โ”ƒ Layer (type)        โ”ƒ Output Shape      โ”ƒ    Param # โ”ƒ Connected to      โ”ƒ\n",
+              "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 40)        โ”‚          0 โ”‚ -                 โ”‚\n",
+              "โ”‚ (InputLayer)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ functional          โ”‚ (None, 12)        โ”‚  1,550,652 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Functional)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0]  โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0]  โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚          0 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚      1,584 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 128260)    โ”‚ 50,919,220 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+              "
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categorical_accuracy: 0.1685 - loss: 4.4140 - perplexity: 99.5634 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.1954 - val_perplexity: 537862.5000\n", + "Epoch 29/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 517ms/step - categorical_accuracy: 0.0568 - loss: 5.8209 - perplexity: 412.2969 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.3590 - val_perplexity: 633498.9375\n", + "Epoch 30/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 748ms/step - categorical_accuracy: 0.1864 - loss: 4.9144 - perplexity: 157.2443 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.5253 - val_perplexity: 748103.1875\n", + "Epoch 31/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 507ms/step - categorical_accuracy: 0.1052 - loss: 8.2503 - perplexity: 22384.6094 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.6228 - val_perplexity: 824754.3750\n", + "Epoch 32/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 612ms/step - categorical_accuracy: 0.4431 - loss: 4.0581 - perplexity: 75.1973 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.0377 - val_perplexity: 1248790.8750\n", + "Epoch 33/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 766ms/step - categorical_accuracy: 0.2086 - loss: 5.6123 - perplexity: 301.7467 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.2131 - val_perplexity: 1488169.6250\n", + "Epoch 34/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 569ms/step - categorical_accuracy: 0.2919 - loss: 4.4319 - perplexity: 154.5172 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.2928 - val_perplexity: 1611684.3750\n", + "Epoch 35/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 585ms/step - categorical_accuracy: 0.1802 - loss: 5.1381 - perplexity: 190.7273 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.4868 - val_perplexity: 1956789.0000\n", + "Epoch 36/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 639ms/step - categorical_accuracy: 0.1719 - loss: 4.6314 - perplexity: 111.0518 - val_categorical_accuracy: 0.1667 - val_loss: 14.5656 - val_perplexity: 2117109.5000\n", + "Epoch 37/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 693ms/step - categorical_accuracy: 0.0839 - loss: 6.8925 - perplexity: 1205.9113 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.6420 - val_perplexity: 2285232.2500\n", + "Epoch 38/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 594ms/step - categorical_accuracy: 0.2530 - loss: 5.8083 - perplexity: 927.8478 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.6140 - val_perplexity: 2222210.0000\n", + "Epoch 39/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 557ms/step - categorical_accuracy: 0.1913 - loss: 4.0802 - perplexity: 62.5591 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.5886 - val_perplexity: 2166540.2500\n", + "Epoch 40/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 757ms/step - categorical_accuracy: 0.2987 - loss: 4.2323 - perplexity: 90.9604 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.6538 - val_perplexity: 2312421.2500\n", + "Epoch 41/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 529ms/step - categorical_accuracy: 0.2453 - loss: 3.7488 - perplexity: 50.1163 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.6478 - val_perplexity: 2298534.5000\n", + "this is neural_network_spec_file 2025_11_23 16_55_cerebros_not-gpt_meta_42/model_architectures/tr_0000000000000000_subtrial_0000000000000000.txt\n", + "returning trial 0 oracles\n", + " categorical_accuracy loss perplexity val_categorical_accuracy \\\n", + "0 0.000000 11.769061 129192.796875 0.000000 \n", + "1 0.000000 11.635833 113077.960938 0.000000 \n", + "2 0.130435 11.652204 114944.367188 0.000000 \n", + "3 0.130435 11.464634 95285.593750 0.000000 \n", + "4 0.000000 11.768666 129141.796875 0.000000 \n", + "5 0.130435 10.994949 59572.500000 0.000000 \n", + "6 0.043478 11.276978 78982.257812 0.000000 \n", + "7 0.000000 11.120511 67542.414062 0.000000 \n", + "8 0.173913 10.726726 45557.273438 0.000000 \n", + "9 0.217391 10.059676 23380.933594 0.166667 \n", + "10 0.173913 10.355123 31417.570312 0.000000 \n", + "11 0.000000 10.472779 35340.300781 0.000000 \n", + "12 0.086957 10.171259 26140.964844 0.000000 \n", + "13 0.217391 9.254299 10449.392578 0.000000 \n", + "14 0.217391 8.896774 7308.360840 0.000000 \n", + "15 0.130435 9.018457 8254.035156 0.000000 \n", + "16 0.130435 8.039083 3099.770996 0.000000 \n", + "17 0.217391 7.848331 2561.456787 0.000000 \n", + "18 0.173913 7.948806 2832.192139 0.000000 \n", + "19 0.043478 7.698378 2204.769043 0.000000 \n", + "20 0.173913 7.669386 2141.766846 0.000000 \n", + "21 0.260870 6.773150 874.061218 0.166667 \n", + "22 0.217391 7.382279 1607.248413 0.166667 \n", + "23 0.130435 6.034015 417.387543 0.166667 \n", + "24 0.130435 6.000526 403.641022 0.166667 \n", + "25 0.043478 6.586512 725.246826 0.166667 \n", + "26 0.260870 5.741646 311.576935 0.166667 \n", + "27 0.130435 5.138083 170.388733 0.000000 \n", + "28 0.086957 5.670679 290.231415 0.000000 \n", + "29 0.217391 5.602477 271.096985 0.000000 \n", + "30 0.173913 6.986033 1081.422852 0.000000 \n", + "31 0.304348 4.127844 62.044033 0.000000 \n", + "32 0.217391 5.934126 377.709869 0.000000 \n", + "33 0.217391 5.564253 260.930054 0.000000 \n", + "34 0.173913 5.642823 282.258331 0.000000 \n", + "35 0.173913 4.475579 87.845474 0.166667 \n", + "36 0.043478 6.194771 490.179321 0.000000 \n", + "37 0.217391 5.472395 238.029572 0.000000 \n", + "38 0.173913 4.001881 54.700928 0.000000 \n", + "39 0.304348 3.707729 40.761116 0.000000 \n", + "40 0.260870 4.130568 62.213223 0.000000 \n", + "\n", + " val_loss val_perplexity trial_number subtrial_number \\\n", + "0 11.755738 1.274830e+05 0 0 \n", + "1 11.755464 1.274480e+05 0 0 \n", + "2 11.755542 1.274580e+05 0 0 \n", + "3 11.739563 1.254375e+05 0 0 \n", + "4 11.729648 1.241999e+05 0 0 \n", + "5 11.724008 1.235014e+05 0 0 \n", + "6 11.714873 1.223784e+05 0 0 \n", + "7 11.718637 1.228398e+05 0 0 \n", + "8 11.724952 1.236180e+05 0 0 \n", + "9 11.730713 1.243322e+05 0 0 \n", + "10 11.739503 1.254300e+05 0 0 \n", + "11 11.750234 1.267832e+05 0 0 \n", + "12 11.752646 1.270895e+05 0 0 \n", + "13 11.755273 1.274237e+05 0 0 \n", + "14 11.762258 1.283168e+05 0 0 \n", + "15 11.835355 1.380477e+05 0 0 \n", + "16 11.884326 1.449764e+05 0 0 \n", + "17 11.947881 1.544894e+05 0 0 \n", + "18 12.035375 1.686152e+05 0 0 \n", + "19 12.174404 1.937655e+05 0 0 \n", + "20 12.258733 2.108143e+05 0 0 \n", + "21 12.340481 2.287719e+05 0 0 \n", + "22 12.413980 2.462197e+05 0 0 \n", + "23 12.586273 2.925156e+05 0 0 \n", + "24 12.676117 3.200130e+05 0 0 \n", + "25 12.751137 3.449438e+05 0 0 \n", + "26 12.875606 3.906650e+05 0 0 \n", + "27 13.195358 5.378625e+05 0 0 \n", + "28 13.359014 6.334989e+05 0 0 \n", + "29 13.525296 7.481032e+05 0 0 \n", + "30 13.622841 8.247544e+05 0 0 \n", + "31 14.037686 1.248791e+06 0 0 \n", + "32 14.213058 1.488170e+06 0 0 \n", + "33 14.292789 1.611684e+06 0 0 \n", + "34 14.486815 1.956789e+06 0 0 \n", + "35 14.565562 2.117110e+06 0 0 \n", + "36 14.641978 2.285232e+06 0 0 \n", + "37 14.614013 2.222210e+06 0 0 \n", + "38 14.588642 2.166540e+06 0 0 \n", + "39 14.653806 2.312421e+06 0 0 \n", + "40 14.647781 2.298534e+06 0 0 \n", + "\n", + " model_name \n", + "0 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "1 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "2 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "3 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "4 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "5 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "6 2025_11_23 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16_55_cerebros_not-gpt_meta_42/mode... \n", + "23 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "24 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "25 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "26 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "27 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "28 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "29 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "30 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "31 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "32 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "33 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "34 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "35 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "36 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "37 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "38 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "39 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "40 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "Global task progress: 33%|\u001b[38;2;22;206;235mโ–ˆโ–ˆโ–ˆโ–Ž \u001b[0m| 1/3 [03:54<07:49, 234.85s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "SimpleCerebrosRandomSearch.input_shapes: [(40,)]\n", + "nan\n", + ">nnf>ceil\n", + "k is: 0 value is: [{'1': }]\n", + "0\n", + "k is: 1 value is: [{'2': }, {'2': }]\n", + "1\n", + "Trying to create level 1\n", + "We think level 1's predecessors are: [0]\n", + "k is: 2 value is: [{'128260': }]\n", + "2\n", + "Trying to create Final level 2\n", + "Trying to create level 2\n", + "We think level final level 2's predecessors are: [0, 1]\n", + "levels:\n", + "[0, 1, 2]\n", + "{'0': 'InputUnitModule'}\n", + "InputLevel.input_shapes [(40,)]\n", + "{'2': }\n", + "{'2': }\n", + "Debug: I am 2 selecting 1\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "Setting levels_unmaterialized[0] level_number 0 to have first successor: levels_unmaterialized[:1], having level_numbers of [1, 2]\n", + "Setting levels_unmaterialized[1] level_number 1 to have first successor: levels_unmaterialized[:2], having level_numbers of [2]\n", + "Debug: successor_connectivity_errors_2d []\n", + "$$$$$$>>>>> Base model: \n", + "InputUnit.input_shape: (40,)\n", + "{'2': }\n", + "{'2': }\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "Debug: successor_connectivity_errors_2d []\n", + "Debug: successor_connectivity_errors_2d []\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_1_DenseLevel_0000000000000001_tr_1_DenseUnit_0000000000000001_tr_1_0 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_1_DenseLevel_0000000000000001_tr_1_DenseUnit_0000000000000001_tr_1_1 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "{'128260': }\n", + "debug: meta_level_number\n", + "Debug: successor_connectivity_errors_2d []\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_1_FinalDenseLevel_0000000000000002_tr_1_FinalDenseUnit_0000000000000002_tr_1_0 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "inputs\n", + "\n", + "\n", + "outputs\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"NeuralNetworkFuture_0000000000000nan_tr_1_nn_materialized\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"NeuralNetworkFuture_0000000000000nan_tr_1_nn_materialized\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ - โ”‚\n", + "โ”‚ (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ functional โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m) โ”‚ \u001b[38;5;34m1,550,652\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mFunctional\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m1,584\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128260\u001b[0m) โ”‚ \u001b[38;5;34m50,919,220\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ], + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+              "โ”ƒ Layer (type)        โ”ƒ Output Shape      โ”ƒ    Param # โ”ƒ Connected to      โ”ƒ\n",
+              "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 40)        โ”‚          0 โ”‚ -                 โ”‚\n",
+              "โ”‚ (InputLayer)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ functional          โ”‚ (None, 12)        โ”‚  1,550,652 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Functional)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0]  โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0]  โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚          0 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚      1,584 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 128260)    โ”‚ 50,919,220 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+              "
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186.2288 - val_categorical_accuracy: 0.1667 - val_loss: 13.5879 - val_perplexity: 796426.8750\n", + "Epoch 29/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 973ms/step - categorical_accuracy: 0.2314 - loss: 5.0346 - perplexity: 261.9535 - val_categorical_accuracy: 0.1667 - val_loss: 13.5785 - val_perplexity: 788957.9375\n", + "Epoch 30/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 1s/step - categorical_accuracy: 0.3508 - loss: 3.9460 - perplexity: 55.9352 - val_categorical_accuracy: 0.1667 - val_loss: 13.5878 - val_perplexity: 796315.2500\n", + "Epoch 31/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 684ms/step - categorical_accuracy: 0.2141 - loss: 3.3061 - perplexity: 29.5618 - val_categorical_accuracy: 0.1667 - val_loss: 13.5959 - val_perplexity: 802850.9375\n", + "Epoch 32/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 659ms/step - categorical_accuracy: 0.1719 - loss: 4.1759 - perplexity: 72.8835 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.7057 - val_perplexity: 896031.1250\n", + "Epoch 33/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 725ms/step - categorical_accuracy: 0.1302 - loss: 5.0193 - perplexity: 177.1105 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.7885 - val_perplexity: 973393.5000\n", + "Epoch 34/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 632ms/step - categorical_accuracy: 0.2919 - loss: 2.9201 - perplexity: 24.4465 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.9237 - val_perplexity: 1114295.3750\n", + "Epoch 35/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 639ms/step - categorical_accuracy: 0.3197 - loss: 3.6359 - perplexity: 60.7448 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.0890 - val_perplexity: 1314598.2500\n", + "Epoch 36/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 527ms/step - categorical_accuracy: 0.2364 - loss: 3.6853 - perplexity: 93.4177 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.1742 - val_perplexity: 1431418.1250\n", + "Epoch 37/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 605ms/step - categorical_accuracy: 0.2731 - loss: 3.3295 - perplexity: 31.0892 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.2398 - val_perplexity: 1528469.6250\n", + "Epoch 38/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 755ms/step - categorical_accuracy: 0.5054 - loss: 4.2462 - perplexity: 96.5757 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.3218 - val_perplexity: 1659098.5000\n", + "Epoch 39/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 606ms/step - categorical_accuracy: 0.3638 - loss: 3.2328 - perplexity: 26.5526 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.3728 - val_perplexity: 1745870.1250\n", + "Epoch 40/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 745ms/step - categorical_accuracy: 0.5727 - loss: 1.9158 - perplexity: 9.5471 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.5209 - val_perplexity: 2024707.8750\n", + "Epoch 41/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 671ms/step - categorical_accuracy: 0.1068 - loss: 5.0107 - perplexity: 172.7614 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.5204 - val_perplexity: 2023570.8750\n", + "this is neural_network_spec_file 2025_11_23 16_55_cerebros_not-gpt_meta_42/model_architectures/tr_0000000000000001_subtrial_0000000000000000.txt\n", + "returning trial 1 oracles\n", + " categorical_accuracy loss perplexity val_categorical_accuracy \\\n", + "0 0.000000 11.719700 225372.531250 0.166667 \n", + "1 0.173913 11.155995 69982.140625 0.166667 \n", + "2 0.173913 10.995764 59621.039062 0.166667 \n", + "3 0.217391 10.042144 22974.583984 0.000000 \n", + "4 0.173913 9.805058 18125.181641 0.000000 \n", + "5 0.130435 9.198784 9885.100586 0.000000 \n", + "6 0.173913 8.641828 5663.671387 0.000000 \n", + "7 0.043478 8.808529 6691.075195 0.000000 \n", + "8 0.217391 7.256882 1417.828491 0.000000 \n", + "9 0.173913 6.904544 996.794250 0.000000 \n", + "10 0.217391 6.873430 966.256958 0.000000 \n", + "11 0.173913 5.982946 396.607025 0.000000 \n", + "12 0.043478 6.824471 920.089539 0.166667 \n", + "13 0.130435 6.259269 522.836731 0.166667 \n", + "14 0.217391 5.205779 182.322769 0.166667 \n", + "15 0.130435 5.462027 235.574463 0.000000 \n", + "16 0.217391 6.074162 434.485474 0.000000 \n", + "17 0.217391 5.354462 211.550262 0.000000 \n", + "18 0.304348 4.318021 75.040001 0.000000 \n", + "19 0.217391 5.875260 356.117035 0.000000 \n", + "20 0.217391 5.246053 189.815536 0.000000 \n", + "21 0.391304 4.035575 56.575462 0.000000 \n", + "22 0.173913 3.672752 39.360092 0.000000 \n", + "23 0.347826 4.800797 121.607239 0.000000 \n", + "24 0.260870 6.058529 427.745911 0.166667 \n", + "25 0.260870 6.874752 967.535400 0.166667 \n", + "26 0.304348 3.871903 48.033691 0.166667 \n", + "27 0.217391 5.597022 269.622284 0.166667 \n", + "28 0.260870 4.006342 54.945507 0.166667 \n", + "29 0.260870 4.286894 72.740173 0.166667 \n", + "30 0.217391 3.180355 24.055300 0.166667 \n", + "31 0.173913 4.073040 58.735218 0.000000 \n", + "32 0.173913 5.302594 200.857193 0.000000 \n", + "33 0.217391 3.763384 43.094006 0.000000 \n", + "34 0.217391 4.363249 78.511826 0.000000 \n", + "35 0.217391 5.450110 232.783875 0.000000 \n", + "36 0.260870 3.634080 37.866989 0.000000 \n", + "37 0.391304 5.082735 161.214310 0.000000 \n", + "38 0.391304 3.312840 27.463017 0.000000 \n", + "39 0.434783 2.846823 17.232950 0.000000 \n", + "40 0.086957 5.169964 175.908478 0.000000 \n", + "\n", + " val_loss val_perplexity trial_number subtrial_number \\\n", + "0 11.768844 1.291648e+05 1 0 \n", + "1 11.750198 1.267787e+05 1 0 \n", + "2 11.742490 1.258052e+05 1 0 \n", + "3 11.745057 1.261286e+05 1 0 \n", + "4 11.736234 1.250206e+05 1 0 \n", + "5 11.717111 1.226525e+05 1 0 \n", + "6 11.690784 1.194656e+05 1 0 \n", + "7 11.644112 1.140180e+05 1 0 \n", + "8 11.626712 1.120513e+05 1 0 \n", + "9 11.637473 1.132634e+05 1 0 \n", + "10 11.659701 1.158094e+05 1 0 \n", + "11 11.750339 1.267965e+05 1 0 \n", + "12 11.802508 1.335870e+05 1 0 \n", + "13 11.897525 1.469026e+05 1 0 \n", + "14 11.997716 1.623835e+05 1 0 \n", + "15 12.270196 2.132448e+05 1 0 \n", + "16 12.433421 2.510535e+05 1 0 \n", + "17 12.588585 2.931926e+05 1 0 \n", + "18 12.766930 3.504347e+05 1 0 \n", + "19 13.121078 4.993575e+05 1 0 \n", + "20 13.272231 5.808403e+05 1 0 \n", + "21 13.392925 6.553504e+05 1 0 \n", + "22 13.513103 7.390372e+05 1 0 \n", + "23 13.607346 8.120731e+05 1 0 \n", + "24 13.620921 8.231726e+05 1 0 \n", + "25 13.592243 7.999009e+05 1 0 \n", + "26 13.596767 8.035281e+05 1 0 \n", + "27 13.587891 7.964269e+05 1 0 \n", + "28 13.578468 7.889579e+05 1 0 \n", + "29 13.587750 7.963152e+05 1 0 \n", + "30 13.595924 8.028509e+05 1 0 \n", + "31 13.705730 8.960311e+05 1 0 \n", + "32 13.788544 9.733935e+05 1 0 \n", + "33 13.923733 1.114295e+06 1 0 \n", + "34 14.089040 1.314598e+06 1 0 \n", + "35 14.174176 1.431418e+06 1 0 \n", + "36 14.239779 1.528470e+06 1 0 \n", + "37 14.321785 1.659098e+06 1 0 \n", + "38 14.372764 1.745870e+06 1 0 \n", + "39 14.520935 2.024708e+06 1 0 \n", + "40 14.520374 2.023571e+06 1 0 \n", + "\n", + " model_name \n", + "0 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "1 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "2 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "3 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "4 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "5 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "6 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "7 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "8 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "9 2025_11_23 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16_55_cerebros_not-gpt_meta_42/mode... \n", + "26 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "27 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "28 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "29 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "30 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "31 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "32 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "33 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "34 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "35 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "36 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "37 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "38 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "39 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "40 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "Global task progress: 67%|\u001b[38;2;22;206;235mโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹ \u001b[0m| 2/3 [07:42<03:50, 230.58s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "SimpleCerebrosRandomSearch.input_shapes: [(40,)]\n", + "nan\n", + ">nnf>ceil\n", + "k is: 0 value is: [{'1': }]\n", + "0\n", + "k is: 1 value is: [{'2': }, {'2': }]\n", + "1\n", + "Trying to create level 1\n", + "We think level 1's predecessors are: [0]\n", + "k is: 2 value is: [{'128260': }]\n", + "2\n", + "Trying to create Final level 2\n", + "Trying to create level 2\n", + "We think level final level 2's predecessors are: [0, 1]\n", + "levels:\n", + "[0, 1, 2]\n", + "{'0': 'InputUnitModule'}\n", + "InputLevel.input_shapes [(40,)]\n", + "{'2': }\n", + "{'2': }\n", + "Debug: I am 2 selecting 1\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "Setting levels_unmaterialized[0] level_number 0 to have first successor: levels_unmaterialized[:1], having level_numbers of [1, 2]\n", + "Setting levels_unmaterialized[1] level_number 1 to have first successor: levels_unmaterialized[:2], having level_numbers of [2]\n", + "Debug: successor_connectivity_errors_2d []\n", + "$$$$$$>>>>> Base model: \n", + "InputUnit.input_shape: (40,)\n", + "{'2': }\n", + "{'2': }\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "Debug: successor_connectivity_errors_2d []\n", + "Debug: successor_connectivity_errors_2d []\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_2_DenseLevel_0000000000000001_tr_2_DenseUnit_0000000000000001_tr_2_0 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_2_DenseLevel_0000000000000001_tr_2_DenseUnit_0000000000000001_tr_2_1 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "{'128260': }\n", + "debug: meta_level_number\n", + "Debug: successor_connectivity_errors_2d []\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_2_FinalDenseLevel_0000000000000002_tr_2_FinalDenseUnit_0000000000000002_tr_2_0 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "inputs\n", + "\n", + "\n", + "outputs\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"NeuralNetworkFuture_0000000000000nan_tr_2_nn_materialized\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"NeuralNetworkFuture_0000000000000nan_tr_2_nn_materialized\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ - โ”‚\n", + "โ”‚ (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ functional โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m) โ”‚ \u001b[38;5;34m1,550,652\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mFunctional\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m1,584\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128260\u001b[0m) โ”‚ \u001b[38;5;34m50,919,220\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ], + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+              "โ”ƒ Layer (type)        โ”ƒ Output Shape      โ”ƒ    Param # โ”ƒ Connected to      โ”ƒ\n",
+              "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 40)        โ”‚          0 โ”‚ -                 โ”‚\n",
+              "โ”‚ (InputLayer)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ functional          โ”‚ (None, 12)        โ”‚  1,550,652 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Functional)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0]  โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0]  โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚          0 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚      1,584 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 128260)    โ”‚ 50,919,220 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+              "
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0.1667 - val_loss: 14.6578 - val_perplexity: 2321627.0000\n", + "Epoch 29/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 794ms/step - categorical_accuracy: 0.3320 - loss: 3.2803 - perplexity: 27.8907 - val_categorical_accuracy: 0.1667 - val_loss: 14.7196 - val_perplexity: 2469625.0000\n", + "Epoch 30/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 660ms/step - categorical_accuracy: 0.2752 - loss: 5.4753 - perplexity: 249.7908 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.8572 - val_perplexity: 2834024.2500\n", + "Epoch 31/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 748ms/step - categorical_accuracy: 0.2925 - loss: 5.2035 - perplexity: 302.8727 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.9761 - val_perplexity: 3191841.5000\n", + "Epoch 32/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 585ms/step - categorical_accuracy: 0.2715 - loss: 3.0830 - perplexity: 22.1130 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.0934 - val_perplexity: 3589043.2500\n", + "Epoch 33/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 837ms/step - categorical_accuracy: 0.3638 - loss: 2.0138 - perplexity: 7.6831 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.1927 - val_perplexity: 3963894.5000\n", + "Epoch 34/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 622ms/step - categorical_accuracy: 0.4165 - loss: 2.3430 - perplexity: 12.4422 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.2933 - val_perplexity: 4383348.5000\n", + "Epoch 35/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 730ms/step - categorical_accuracy: 0.4832 - loss: 3.8156 - perplexity: 57.3130 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4055 - val_perplexity: 4903895.5000\n", + "Epoch 36/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 831ms/step - categorical_accuracy: 0.1641 - loss: 4.5182 - perplexity: 317.0210 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4245 - val_perplexity: 4998003.5000\n", + "Epoch 37/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 621ms/step - categorical_accuracy: 0.2752 - loss: 2.9753 - perplexity: 25.5228 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4590 - val_perplexity: 5173094.0000\n", + "Epoch 38/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 666ms/step - categorical_accuracy: 0.2280 - loss: 2.4058 - perplexity: 11.6680 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4232 - val_perplexity: 4991435.0000\n", + "Epoch 39/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 565ms/step - categorical_accuracy: 0.3592 - loss: 3.6356 - perplexity: 40.6227 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4089 - val_perplexity: 4920268.0000\n", + "Epoch 40/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 665ms/step - categorical_accuracy: 0.2691 - loss: 3.4659 - perplexity: 47.4784 - val_categorical_accuracy: 0.1667 - val_loss: 15.3797 - val_perplexity: 4778703.0000\n", + "Epoch 41/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 693ms/step - categorical_accuracy: 0.4310 - loss: 2.7924 - perplexity: 20.8595 - val_categorical_accuracy: 0.1667 - val_loss: 15.4324 - val_perplexity: 5037467.5000\n", + "this is neural_network_spec_file 2025_11_23 16_55_cerebros_not-gpt_meta_42/model_architectures/tr_0000000000000002_subtrial_0000000000000000.txt\n", + "returning trial 2 oracles\n", + " categorical_accuracy loss perplexity val_categorical_accuracy \\\n", + "0 0.000000 11.761698 226943.984375 0.000000 \n", + "1 0.260870 11.090140 65521.941406 0.166667 \n", + "2 0.086957 10.702163 44451.890625 0.166667 \n", + "3 0.173913 9.990288 21813.576172 0.166667 \n", + "4 0.347826 9.246581 10369.053711 0.166667 \n", + "5 0.260870 8.266317 3890.594971 0.166667 \n", + "6 0.347826 7.704062 2217.337646 0.166667 \n", + "7 0.304348 7.122604 1239.675415 0.166667 \n", + "8 0.347826 6.225540 505.495789 0.166667 \n", + "9 0.086957 6.562615 708.120972 0.166667 \n", + "10 0.260870 5.511858 247.610764 0.166667 \n", + "11 0.217391 5.295715 199.480270 0.166667 \n", + "12 0.130435 6.463620 641.378784 0.000000 \n", + "13 0.260870 5.026217 152.355484 0.000000 \n", + "14 0.217391 5.910099 368.742676 0.000000 \n", + "15 0.217391 4.887461 132.616455 0.000000 \n", + "16 0.434783 3.347833 28.441027 0.000000 \n", + "17 0.347826 4.313054 74.668182 0.000000 \n", + "18 0.304348 4.665129 106.179253 0.000000 \n", + "19 0.217391 4.334057 76.253006 0.000000 \n", + "20 0.391304 2.807739 16.572411 0.000000 \n", + "21 0.391304 4.215992 67.761353 0.000000 \n", + "22 0.391304 3.522572 33.871445 0.000000 \n", + "23 0.478261 2.265072 9.631822 0.000000 \n", + "24 0.347826 5.091538 162.639801 0.000000 \n", + "25 0.347826 2.982907 19.745134 0.000000 \n", + "26 0.130435 3.861120 47.518566 0.000000 \n", + "27 0.434783 4.315707 74.866554 0.166667 \n", + "28 0.304348 3.004366 20.173416 0.166667 \n", + "29 0.217391 5.262289 192.922501 0.000000 \n", + "30 0.260870 5.697386 298.087250 0.000000 \n", + "31 0.347826 3.149921 23.334219 0.000000 \n", + "32 0.391304 2.063896 7.876601 0.000000 \n", + "33 0.391304 3.141111 23.129541 0.000000 \n", + "34 0.391304 3.663168 38.984657 0.000000 \n", + "35 0.217391 3.455597 31.677193 0.000000 \n", + "36 0.217391 3.796592 44.549091 0.000000 \n", + "37 0.217391 2.545129 12.744876 0.000000 \n", + "38 0.260870 4.018140 55.597588 0.000000 \n", + "39 0.173913 3.072442 21.594568 0.166667 \n", + "40 0.434783 2.709372 15.019834 0.166667 \n", + "\n", + " val_loss val_perplexity trial_number subtrial_number \\\n", + "0 11.722857 1.233594e+05 2 0 \n", + "1 11.644334 1.140433e+05 2 0 \n", + "2 11.609864 1.101793e+05 2 0 \n", + "3 11.582150 1.071679e+05 2 0 \n", + "4 11.588885 1.078919e+05 2 0 \n", + "5 11.611365 1.103448e+05 2 0 \n", + "6 11.635376 1.130263e+05 2 0 \n", + "7 11.736249 1.250225e+05 2 0 \n", + "8 11.836572 1.382158e+05 2 0 \n", + "9 11.940872 1.534104e+05 2 0 \n", + "10 12.069603 1.744865e+05 2 0 \n", + "11 12.378307 2.375913e+05 2 0 \n", + "12 12.568721 2.874260e+05 2 0 \n", + "13 12.738580 3.406394e+05 2 0 \n", + "14 12.861950 3.853664e+05 2 0 \n", + "15 13.139782 5.087856e+05 2 0 \n", + "16 13.314763 6.060774e+05 2 0 \n", + "17 13.506835 7.344190e+05 2 0 \n", + "18 13.664001 8.594092e+05 2 0 \n", + "19 13.938638 1.131028e+06 2 0 \n", + "20 14.083958 1.307933e+06 2 0 \n", + "21 14.162007 1.414105e+06 2 0 \n", + "22 14.258838 1.557883e+06 2 0 \n", + "23 14.377925 1.754904e+06 2 0 \n", + "24 14.447185 1.880756e+06 2 0 \n", + "25 14.536368 2.056196e+06 2 0 \n", + "26 14.590198 2.169913e+06 2 0 \n", + "27 14.657779 2.321627e+06 2 0 \n", + "28 14.719577 2.469625e+06 2 0 \n", + "29 14.857208 2.834024e+06 2 0 \n", + "30 14.976109 3.191842e+06 2 0 \n", + "31 15.093396 3.589043e+06 2 0 \n", + "32 15.192738 3.963894e+06 2 0 \n", + "33 15.293323 4.383348e+06 2 0 \n", + "34 15.405540 4.903896e+06 2 0 \n", + "35 15.424549 4.998004e+06 2 0 \n", + "36 15.458982 5.173094e+06 2 0 \n", + "37 15.423234 4.991435e+06 2 0 \n", + "38 15.408874 4.920268e+06 2 0 \n", + "39 15.379680 4.778703e+06 2 0 \n", + "40 15.432412 5.037468e+06 2 0 \n", + "\n", + " model_name \n", + "0 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "1 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "2 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "3 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "4 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "5 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "6 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "7 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "8 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "9 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "10 2025_11_23 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16_55_cerebros_not-gpt_meta_42/mode... \n", + "27 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "28 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "29 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "30 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "31 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "32 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "33 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "34 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "35 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "36 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "37 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "38 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "39 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "40 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "Global task progress: 100%|\u001b[38;2;22;206;235mโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ\u001b[0m| 3/3 [12:11<00:00, 243.86s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Index(['categorical_accuracy', 'loss', 'perplexity',\n", + " 'val_categorical_accuracy', 'val_loss', 'val_perplexity',\n", + " 'trial_number', 'subtrial_number', 'model_name'],\n", + " dtype='object')\n", + "metric_to_rank_by is: 'perplexity'\n", + "Type of metric_to_rank_by is: \n", + "metric_to_rank_by is: 'perplexity'\n", + "Type of metric_to_rank_by is: \n", + "Best result this trial was: 7.876600742340088\n", + "Type of best result: \n", + "Best model name: 2025_11_23 16_55_cerebros_not-gpt_meta_42/models/tr_0000000000000002_subtrial_0000000000000000.keras\n", + "Cerebros trained 3 models in 12.19 min. Average time per model: 4.06 min.\n", + "Cerebros best perplexity achieved in Phase I-a is 7.876600742340088\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Training Stage I-a - Model Evaluation (Subjective):\n", + "\n", + "- We retrieve the best model found during the NAS phase and test its text generation capabilities from a subjective standpoint.\n", + "- Keep in mind, this is trained on 10 text samples. It is impressive that it can generate anything, especially subjects and verbs that are on-topic and agree, and is otherwise sensible, despite being grammatically gibberish.\n", + "\n", + "FYI: The generative components we imported from cerebrosllmutils:\n", + "\n", + "## Model config\n", + "```python\n", + "\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='CerebrosNotGPTConfig')\n", + "class CerebrosNotGPTConfig:\n", + " def __init__(self, max_sequence_length=1536, padding_token=None):\n", + " self.max_sequence_length = max_sequence_length\n", + " self.padding_token = padding_token\n", + "\n", + " def get_config(self):\n", + " return {\n", + " 'max_sequence_length': self.max_sequence_length,\n", + " 'padding_token': self.padding_token\n", + " }\n", + "\n", + " @classmethod\n", + " def from_config(cls, config):\n", + " return cls(**config)\n", + "```\n", + "\n", + "## Model class we imported from cerebrosllmutil, having:\n", + "\n", + "- Greedy sampling\n", + "- Temperature scaling\n", + "- Top p sampling\n", + "- Top k sampling\n", + "- Presence penlaty\n", + "- Frequency penalty\n", + "- Repetition penalty\n", + "\n", + "```python\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='CerebrosNotGPT')\n", + "class CerebrosNotGPT(tf.keras.Model):\n", + " def __init__(self, config: Any, model: Any = None, **kwargs):\n", + " # 1. Store the nested model argument.\n", + " self.config = config\n", + " self.model = model\n", + " \n", + " # 2. Extract and remove custom kwargs (like 'model') before calling super.\n", + " # This is important to prevent 'unrecognized keyword argument' errors.\n", + " # The nested model is already extracted and stored, so it can be safely removed.\n", + " kwargs.pop('model', None)\n", + " \n", + " # 3. Call the parent constructor with the cleaned kwargs.\n", + " super().__init__(**kwargs)\n", + "\n", + " self.max_sequence_length = config.max_sequence_length\n", + " self.padding_token = config.padding_token\n", + "\n", + " def get_config(self):\n", + " base_config = super().get_config()\n", + " config_dict = {\n", + " 'config': self.config.get_config(),\n", + " }\n", + " \n", + " # Explicitly handle nested model serialization.\n", + " # This is required if Keras's automatic tracking fails.\n", + " if self.model is not None:\n", + " # Note: This approach might still suffer from weight loss.\n", + " # The recommended way is to let Keras handle it automatically.\n", + " config_dict['model'] = tf.keras.utils.serialize_keras_object(self.model)\n", + "\n", + " base_config.update(config_dict)\n", + " return base_config\n", + "\n", + " @classmethod\n", + " def from_config(cls, config):\n", + " # Separate the custom config.\n", + " config_obj_dict = config.pop('config')\n", + " config_obj = CerebrosNotGPTConfig.from_config(config_obj_dict)\n", + " \n", + " # Manually extract and load the nested model.\n", + " nested_model_config = config.pop('model', None)\n", + " if nested_model_config:\n", + " nested_model = tf.keras.utils.deserialize_keras_object(nested_model_config)\n", + " else:\n", + " nested_model = None\n", + " \n", + " # Reconstruct the outer model by passing the restored parts.\n", + " return cls(config=config_obj, model=nested_model, **config)\n", + "\n", + " def call(self, inputs, training=False):\n", + " if self.model is None:\n", + " raise ValueError(\"Inner model not initialized properly\")\n", + " return self.model(inputs, training=training)\n", + "\n", + " @staticmethod\n", + " def apply_top_k_probs(probs, k):\n", + " if k is None or k <= 0:\n", + " return probs\n", + " # Flatten and argsort for indices\n", + " sorted_indices = tf.argsort(probs, direction='DESCENDING')\n", + " keep_indices = sorted_indices[:k]\n", + " mask = tf.zeros_like(probs, dtype=tf.bool)\n", + " mask = tf.tensor_scatter_nd_update(mask, tf.reshape(keep_indices, (-1, 1)),\n", + " tf.ones((k,), dtype=tf.bool))\n", + " filtered_probs = tf.where(mask, probs, tf.zeros_like(probs))\n", + " # Renormalize\n", + " filtered_probs = filtered_probs / tf.reduce_sum(filtered_probs)\n", + " return filtered_probs\n", + "\n", + " @staticmethod\n", + " def apply_top_p_probs(probs, p):\n", + " if p is None or p >= 1.0:\n", + " return probs\n", + " sorted_indices = tf.argsort(probs, direction='DESCENDING')\n", + " sorted_probs = tf.gather(probs, sorted_indices)\n", + " cumulative_probs = tf.cumsum(sorted_probs)\n", + " mask = cumulative_probs <= p\n", + " # Always keep at least 1 token\n", + " mask = tf.concat([tf.constant([True]), mask[1:]], axis=0)\n", + " keep_indices = tf.boolean_mask(sorted_indices, mask)\n", + " filtered_probs = tf.where(\n", + " tf.reduce_any(tf.equal(tf.range(tf.shape(probs)[0])[:, None], keep_indices), axis=1), probs,\n", + " tf.zeros_like(probs))\n", + " # Renormalize\n", + " filtered_probs = filtered_probs / tf.reduce_sum(filtered_probs)\n", + " return filtered_probs\n", + "\n", + " def generate(self,\n", + " token_ids,\n", + " do_sample=False,\n", + " max_new_tokens=None,\n", + " temperature=1.0,\n", + " top_k=None,\n", + " top_p=None,\n", + " frequency_penalty=None,\n", + " presence_penalty=None,\n", + " repetition_penalty=None):\n", + " \"\"\"\n", + " Generate text autoregressively from token IDs.\n", + " Applies filtering in sequence: penalties -> temperature -> top-k -> top-p\n", + " \"\"\"\n", + " # Convert token_ids to list if it's not already\n", + " if not isinstance(token_ids, list):\n", + " token_ids = list(token_ids)\n", + "\n", + " # Determine the actual maximum number of new tokens\n", + " if max_new_tokens is None:\n", + " max_new_tokens = self.max_sequence_length - len(token_ids)\n", + " else:\n", + " max_new_tokens = min(max_new_tokens, self.max_sequence_length - len(token_ids))\n", + "\n", + " # Initialize the generated tokens list\n", + " generated_tokens = []\n", + " current_tokens = token_ids.copy()\n", + "\n", + " # Autoregressive generation loop\n", + " for _ in range(max_new_tokens):\n", + " # Pad or truncate to max_sequence_length\n", + " if len(current_tokens) > self.max_sequence_length:\n", + " input_tokens = current_tokens[-self.max_sequence_length:]\n", + " else:\n", + " padding_needed = self.max_sequence_length - len(current_tokens)\n", + " input_tokens = current_tokens + [self.padding_token] * padding_needed\n", + "\n", + " # Convert to tensor and get model prediction\n", + " input_tensor = tf.constant([input_tokens], dtype=tf.int32)\n", + " probs_nested = self.model(input_tensor)\n", + " probs = probs_nested[0] # Already softmax probabilities (NOT logits as comment says)\n", + " logits = tf.math.log(probs + 10 ** -20) # Convert to logits for penalty application\n", + "\n", + " if do_sample:\n", + " # Apply repetition/frequency/presence penalties to logits\n", + " if frequency_penalty is not None or presence_penalty is not None:\n", + " # Collect token counts from current_tokens\n", + " token_counts = {}\n", + " for t in current_tokens:\n", + " token_counts[t] = token_counts.get(t, 0) + 1\n", + "\n", + " # Prepare penalty tensor\n", + " vocab_size = tf.shape(logits)[0]\n", + " penalties = tf.zeros_like(logits)\n", + "\n", + " for token_id, count in token_counts.items():\n", + " if token_id >= vocab_size:\n", + " continue\n", + " penalty = 0.0\n", + " if presence_penalty is not None:\n", + " penalty += presence_penalty\n", + " if frequency_penalty is not None:\n", + " penalty += frequency_penalty * count\n", + "\n", + " penalties = tf.tensor_scatter_nd_add(\n", + " penalties,\n", + " [[token_id]],\n", + " [penalty]\n", + " )\n", + "\n", + " # Subtract penalties from logits\n", + " logits = logits - penalties\n", + "\n", + " # Apply repetition penalty (standard approach)\n", + " if repetition_penalty is not None and repetition_penalty != 1.0:\n", + " # Collect unique tokens that have appeared\n", + " unique_tokens = list(set(current_tokens))\n", + " vocab_size = tf.shape(logits)[0]\n", + "\n", + " for token_id in unique_tokens:\n", + " if token_id < vocab_size:\n", + " # Divide logits of repeated tokens by penalty\n", + " logits = tf.tensor_scatter_nd_update(\n", + " logits,\n", + " [[token_id]],\n", + " [logits[token_id] / repetition_penalty]\n", + " )\n", + "\n", + " # Apply temperature\n", + " if temperature != 1.0:\n", + " logits = logits / temperature\n", + "\n", + " # Convert to probabilities\n", + " probs = tf.nn.softmax(logits)\n", + "\n", + " # Apply top-k filtering (if specified)\n", + " if top_k is not None and top_k > 0:\n", + " k = min(top_k, tf.shape(probs)[0])\n", + " # Get top-k values and indices\n", + " top_k_values, top_k_indices = tf.nn.top_k(probs, k=k, sorted=False)\n", + " # Create mask for top-k positions\n", + " top_k_mask = tf.scatter_nd(\n", + " tf.expand_dims(top_k_indices, 1),\n", + " tf.ones_like(top_k_values, dtype=tf.bool),\n", + " tf.shape(probs)\n", + " )\n", + " # Zero out non-top-k probabilities\n", + " probs = tf.where(top_k_mask, probs, tf.zeros_like(probs))\n", + " # Renormalize\n", + " probs = probs / tf.reduce_sum(probs)\n", + " print(\n", + " f\">>> After top_k: {tf.shape(probs)} shape, {tf.reduce_sum(tf.cast(probs > 1e-8, tf.int32))} non-zero probs\")\n", + "\n", + " # Apply top-p filtering (if specified)\n", + " if top_p is not None and top_p < 1.0:\n", + " # Sort probabilities in descending order\n", + " sorted_indices = tf.argsort(probs, direction='DESCENDING')\n", + " sorted_probs = tf.gather(probs, sorted_indices)\n", + " cumulative_probs = tf.cumsum(sorted_probs)\n", + " # Create mask for top-p\n", + " mask = cumulative_probs <= top_p\n", + " # Always keep at least one token\n", + " mask = tf.concat([tf.constant([True]), mask[1:]], axis=0)\n", + " # Get indices to keep\n", + " keep_indices = tf.boolean_mask(sorted_indices, mask)\n", + " # Create mask for original indices\n", + " filter_mask = tf.scatter_nd(\n", + " tf.expand_dims(keep_indices, 1),\n", + " tf.ones_like(keep_indices, dtype=tf.bool),\n", + " tf.shape(probs)\n", + " )\n", + " # Apply mask and renormalize\n", + " probs = tf.where(filter_mask, probs, tf.zeros_like(probs))\n", + " probs = probs / tf.reduce_sum(probs)\n", + " print(\n", + " f\">>> After top_p: {tf.shape(probs)} shape, {tf.reduce_sum(tf.cast(probs > 1e-8, tf.int32))} non-zero probs\")\n", + "\n", + " # Sample from the final filtered distribution\n", + " # Get non-zero indices and their probabilities\n", + " non_zero_mask = probs > 1e-8\n", + " if tf.reduce_any(non_zero_mask):\n", + " filtered_indices = tf.where(non_zero_mask)[:, 0] # Get indices\n", + " filtered_probs = tf.boolean_mask(probs, non_zero_mask) # Get probabilities\n", + " # Sample\n", + " sampled_local_index = tf.random.categorical(tf.math.log(filtered_probs)[None, :], 1)[0, 0]\n", + " # Map back to vocabulary index\n", + " next_token_id = int(filtered_indices[sampled_local_index].numpy())\n", + " else:\n", + " # Fallback if all probabilities are zero\n", + " warn(\n", + " \"Token sampling had to revert to greedy sampling, because no probs had a value > 0, unexpected\")\n", + " next_token_id = int(tf.argmax(probs, axis=-1).numpy())\n", + "\n", + " else:\n", + " # Greedy sampling (argmax) - apply repetition penalty if needed\n", + " if repetition_penalty is not None and repetition_penalty != 1.0:\n", + " unique_tokens = list(set(current_tokens))\n", + " vocab_size = tf.shape(logits)[0]\n", + " for token_id in unique_tokens:\n", + " if token_id < vocab_size:\n", + " logits = tf.tensor_scatter_nd_update(\n", + " logits,\n", + " [[token_id]],\n", + " [logits[token_id] / repetition_penalty]\n", + " )\n", + "\n", + " next_token_id = int(tf.argmax(logits, axis=-1).numpy())\n", + "\n", + " # Check for termination condition\n", + " if next_token_id == self.padding_token:\n", + " break\n", + "\n", + " # Add to generated tokens and update current tokens\n", + " generated_tokens.append(int(next_token_id))\n", + " current_tokens.append(int(next_token_id))\n", + "\n", + " # Check if we've reached max sequence length\n", + " if len(current_tokens) >= self.max_sequence_length:\n", + " break\n", + "\n", + " return token_ids + generated_tokens\n", + "\n", + "```" + ], + "metadata": { + "id": "96KSf1hKoe0H" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "## How this LLM wrapper works under the hood: A Simple Overview\n", + "\n", + "- Think of a Large Language Model like the \"autocomplete\" on your cell phone's keyboard that suggests the next word.\n", + "- Now, imagine you continuously click the suggested next word.\n", + "- The model picks the mathematically most likely next word, and you just go with it, and pick the next, then the next ...\n", + "\n", + "### Here is the step-by-step flow of how it generates text.\n", + "\n", + "1. INPUT: The Prompt\n", + "\n", + "The process always starts with a piece of text from you, the user.\n", + "\n", + "\"Write a story\"\n", + "\n", + "2. STEP 1: Tokenization โ€” From Words to Numbers\n", + "\n", + "A computer doesn't understand letters or words; it understands numbers. The first step is to convert the prompt into a sequence of numbers the model can process. The tokenizer is a specialized dictionary for this job.\n", + "\n", + " What comes in: A string of text (\"Write a story\").\n", + " What goes out: A list of numerical IDs ([92, 21, 54, 21, 63, ...]).\n", + "\n", + "To make processing consistent, the input is always padded to a fixed length (e.g., 40 tokens). Any empty slots are filled with a special ID that is assigned by the tokenizer.\n", + "\n", + "\"Write a story\" -> tokenizer -> [92, 21, 54, 21, 63, 1234, 1234, ... (length 40)]\n", + "\n", + "For example it may look like:\n", + "```\n", + "92 = \"Write\"\n", + "21 = \" \"\n", + "54 = \"a\"\n", + "63 = \"story\"\n", + "1234 = \"\" (Repeated until there are 40 numbers)\n", + "```\n", + "\n", + "3. The Model's Core: Going From Token IDs to the Predicted Next Token:\n", + "\n", + "This is the \"black box\" part. Inside the model, 4 basic things happen:\n", + "\n", + " 1. Embedding (Converts the discrete, high-dimensional sequence of tokens into a continuous distribution of a smaller dimensionality).\n", + " 2. Positional embedding: Positional embedding: Takes the output of the embedding layer and represents their relative sequential order as a continuous distribution with a clear mathematical relationship.\n", + " 3. Prediction: Prediction: A lattice of Dense layers, arranged as columns and rows, each having randomized lateral connectivity with other Dense layers on the same row, and randomized vertical connectivity with Dense layers on other rows. This takes the positional embedding's output and returns a numerical answer from its head layer. This element, produced by the Cerebros NAS, serves as a more computationally efficient alternative to the attention block used in other LLMs. The output is of shape (BATCH_SIZE, VOCABULARY_SIZE) as logits.\n", + " 4. Output activation (Scales the output to a valid range). In this case, the raw output is a tensor of shape (BATCH_SIZE, VOCABULARY_SIZE). The numbers need to be cast as probabilities, so the valid range is:\n", + " - Each element in the list must be in the range between 0 and 1 (inclusive).\n", + " - The entire list of numbers must add up to 1.\n", + " - Softmax is used to accomplish this.\n", + "\n", + "As mentioned before, this is a \"Single Head\" model, unlike most LLMs (like GPT-3/4). Each call returns **only** the next token expected in the sequence, expressed as a list of probabilities (probs) of shape (BATCH_SIZE, VOCABULARY_SIZE).\n", + "\n", + "\n", + "4. Predicting the Next Word From the Output of The Final Layer:\n", + "\n", + "After the model returns a list of probabilities, we must **pick the next word** from this. There are VOCABULARY_SIZE words in the vocabulary, each assigned an index position on this list.\n", + "\n", + "5. Sampling\n", + "\n", + "- **Greedy Sampling** The naive strategy is to just pick the highest probability in this distribution (we call this greedy sampling) and assume it is the correct next token. You then decode that token ID and use it as the next word. Then de-code that toekn id and use that as the next word. Naively assuming the highest probability is correct makes for a few problems, including:\n", + " - The output will be identical every time you write the same prompt.\n", + " - Common words like \"the\", \"and\", ... will be used too often and used out of place.\n", + " - The text will seem \"dry\" and lack creative appeal.\n", + "- **Beam Sampling**: The better approach, is scaling then sampling from a few of the top choices. We apply scaling to the logits and recalculate the probabilities. Then, we eliminate unlikely possibilities. This leaves a smaller set of plausible tokens, from which we randomly select the next word. The methods we use are:\n", + " - **Presence penalty:** Steeply penalizes the logit for a token that has already been used recently or as the last word in the sequence, making it very unlikely to survive sampling and be selected. **Its purpose:** Mainly prevents the same word from being used twice **in immediate succession** \"This is **the the the** problem which **this this** scaling technique should fix.\"\n", + " - **Frequency penalty:** Mildly penalizes the logit for a token that has been **overused** in the text, but **not necessarily** the last or recent word, making it less likely to be chosen repeatedly but still possible. **For an example:** \"This technique **like** fixes **like** this from **like** happening. It's **like** really really annoying.\"\n", + " - **Repetition penalty scaling**: A penalty that balances the effects of both presence and frequency penalties, attempting to fix both problems at the same time.\n", + " - **Temperature scaling:** Temperature scaling divides logits by a number you set for 'tempterature' to control output \"creativity\" vs \"precision\". Low temperatures less than 1 make the model's top choices more likely, creating predictable text. High temperatures greater than 1, give less likely words a better chance, leading to more diverse and random text. Basically the higher you set it, the more creative and less factual the LLM's writing will be, the lower, the more precice and factual.\n", + " - After applying all scaling, we convert the logits back to probabilities using softmax. We then proceed to sampling:\n", + " - **Top k sampling**: Set a number 'k'. Eliminate all but the highest k numbers on this list of scaled probabilities.\n", + " - **Top p sampling:** Set a number 'p'. Starting from the most likely token, add up the probabilities until the sum reaches or exceeds 'p'. Keep only this cumulative set of tokens.\n", + " \n", + "Now that we have scaled and filtered the list of tokens, we randomly pick one from the remaining options.\n", + "\n", + "\n", + "6. ## The Generation Loop: We just do this on repeat.\n", + "\n", + "The model only predicts one word at a time. To complete text, we repeat this with the result of the original prompt + the result of predicting the next. We call this an **autoregressive** loop.\n", + "\n", + "\n", + " Start with a prompt \"\"Write, a, story\"\n", + " \n", + " Input: [Write, a, story]\n", + " Model predicts the token that decodes to: \"about\"\n", + "\n", + " Repeat 1: New Input: The appended sequence is fed back into the model.\n", + " New Input: [Write, a, story, about]\n", + " Model predicts: \"a\"\n", + "\n", + " Repeat 2:New Input:\n", + " New Input: [Write, a, story, about, a]\n", + " Model predicts: \"fox\"\n", + "\n", + "This loop continues until the model generates a special \"end-of-sequence\" token / pad token or it reaches its maximum length limit (40 tokens in our example).\n", + "\n", + "\n", + "\n", + "## Revisiting the analogy of the auto complete on repeat, this is what this looks like:\n" + ], + "metadata": { + "id": "kMW_6Vrq_Yi9" + } + }, + { + "cell_type": "markdown", + "source": [ + 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)" + ], + "metadata": { + "id": "NaMi9QniKqdO" + } + }, + { + "cell_type": "code", + "source": [ + "# Get the best model from the search\n", + "best_model_found = cerebros_automl.get_best_model(purge_model_storage_files='slate')\n", + "\n", + "# Create config and generative model wrapper\n", + "config = CerebrosNotGPTConfig(\n", + " max_sequence_length=MAX_SEQ_LENGTH,\n", + " padding_token=tokenizer.pad_token_id\n", + ")\n", + "generator = CerebrosNotGPT(config, model=best_model_found)\n", + "\n", + "# Test if the model can be built successfully\n", + "text = \"This is a test ...\"\n", + "input_ids = tokenizer(text, add_special_tokens=False)['input_ids']\n", + "current_tokens = input_ids.copy()\n", + "PADDING_TOKEN = tokenizer.pad_token_id\n", + "\n", + "if len(current_tokens) > MAX_SEQ_LENGTH:\n", + " input_tokens = current_tokens[-MAX_SEQ_LENGTH:]\n", + "else:\n", + " padding_needed = MAX_SEQ_LENGTH - len(current_tokens)\n", + " input_tokens = current_tokens + [PADDING_TOKEN] * padding_needed\n", + "\n", + "# A dummy pass to force the model to build\n", + "\n", + "input_tensor = tf.constant([input_tokens], dtype=tf.int32)\n", + "\n", + "try:\n", + " _ = generator(input_tensor)\n", + " print(\"โœ… Building LLM Model Successful!\")\n", + "except Exception as exc:\n", + " error_message = f\"โŒ Building model returned the error: {exc}\"\n", + " print(error_message)\n" + ], + "metadata": { + "id": "AEk-TtPCxleV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d253eeeb-831e-48ce-f256-c8f10540064a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/layer.py:421: UserWarning: `build()` was called on layer 'interleaved_ro_pe', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "โœ… Building LLM Model Successful!\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Text Generation Utilities\n", + "\n", + "We define two helper functions for text generation:\n", + "\n", + "- One for greedy sampling\n", + "- One for beam sampling with various parameters." + ], + "metadata": { + "id": "u6-wAM0XyUZC" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Required parameter\n", + "\n", + "trial_number =1\n", + "\n", + "\n", + "# Utility function for greedy sampling\n", + "def complete_text_greedy(text: str, max_new_tokens: int = 10) -> str:\n", + " input_ids = tokenizer(text, add_special_tokens=False)['input_ids']\n", + " generated_tokens = generator.generate(\n", + " token_ids=input_ids,\n", + " do_sample=False,\n", + " max_new_tokens=max_new_tokens\n", + " )\n", + " generated_text = tokenizer.decode(generated_tokens).replace(text, \"\")\n", + " return generated_text\n", + "\n", + "# Utility function for beam sampling\n", + "def complete_text_beam(text: str,\n", + " max_new_tokens: int = 10,\n", + " temperature: float = 0.75,\n", + " top_k: int = 75,\n", + " top_p: float = 0.98,\n", + " repetition_penalty: float = None,\n", + " presence_penalty: float = 1.3,\n", + " frequency_penalty: float = 1.4) -> str:\n", + " input_ids = tokenizer(text, add_special_tokens=False)['input_ids']\n", + " generated_tokens = generator.generate(\n", + " token_ids=input_ids,\n", + " do_sample=True,\n", + " max_new_tokens=max_new_tokens,\n", + " temperature=temperature,\n", + " top_k=top_k,\n", + " top_p=top_p,\n", + " presence_penalty=presence_penalty,\n", + " frequency_penalty=frequency_penalty\n", + " )\n", + " generated_text = tokenizer.decode(generated_tokens).replace(text, \"\")\n", + " return generated_text\n" + ], + "metadata": { + "id": "f8XigcJcykLn" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Running Generation Tests\n", + "\n", + "We run a series of tests with different prompts and sampling parameters to evaluate the quality of the model from Stage I-a." + ], + "metadata": { + "id": "HG0IjcWEyrXn" + } + }, + { + "cell_type": "code", + "source": [ + "def test_text(test_prompt: str, max_new_tokens: int, result_cutoff: float, trial_id: int,\n", + " test_sample_number: int, result_0: float) -> None:\n", + " \"\"\"\n", + " If the result_0 < result_cutoff, this will run a matrix of different sampling values and print out the resulting text for human subjective evaluation.\n", + "\n", + " Parameters:\n", + " - test_prompt: a string to prompt generation\n", + " - max_new_tokens: int, number of tokens to generate unless we generate a stop token.\n", + " - sample_number: Metadata for sample...\n", + " - result_0: Perplexity score from this run\n", + " - result_cutoff: Perplexity score that would be expected to indicate a trial worth running this pn\n", + "\n", + " \"\"\"\n", + " if result_0 < result_cutoff:\n", + " generation_param_permutations = [\n", + " # #3\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.6,\n", + " 'top_k': 75,\n", + " 'top_p': 0.98,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.3,\n", + " 'frequency_penalty': 1.4\n", + " },\n", + " # #4\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.7,\n", + " 'top_k': 75,\n", + " 'top_p': 0.98,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.3,\n", + " 'frequency_penalty': 1.4\n", + " },\n", + " # #5\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.7,\n", + " 'top_k': 75,\n", + " 'top_p': 0.97,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.3,\n", + " 'frequency_penalty': 1.4},\n", + " # #6\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.75,\n", + " 'top_k': 75,\n", + " 'top_p': 0.98,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.4},\n", + " # #7\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.7,\n", + " 'top_k': 75,\n", + " 'top_p': 0.98,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.4},\n", + " # #8\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.6,\n", + " 'top_k': 75,\n", + " 'top_p': 0.98,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.4\n", + " },\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.6,\n", + " 'top_k': 40,\n", + " 'top_p': 0.96,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.4\n", + " },\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.7,\n", + " 'top_k': 45,\n", + " 'top_p': 0.97,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.3\n", + " }, #\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.6,\n", + " 'top_k': 75,\n", + " 'top_p': 0.99,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.4\n", + " },\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.65,\n", + " 'top_k': 75,\n", + " 'top_p': 0.985,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.4\n", + " },\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.8,\n", + " 'top_k': 75,\n", + " 'top_p': 0.99,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 0.7,\n", + " 'frequency_penalty': 0.7\n", + " }\n", + " ]\n", + " # Default cases, no params\n", + " response_1 = complete_text_greedy(text=test_prompt, max_new_tokens=max_new_tokens)\n", + " print(\n", + " f\"Trial #: {trial_id} Text Sample #: {test_sample_number} Perplexity: {result_0} GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: '{test_prompt}' RESPONSE: '{response_1}'\")\n", + " # print(f\"Sample {sample_number}: I ask the generator (greedy): {test_prompt}... It responds: '{response_1}'.\")\n", + " response_2 = complete_text_beam(text=test_prompt, max_new_tokens=max_new_tokens)\n", + " print(\n", + " f\"Trial #: {trial_id} Text Sample #: {test_sample_number} Perplexity: {result_0} GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: '{test_prompt}' RESPONSE: '{response_2}'.\")\n", + " # print(f\"Sample {sample_number}: I ask the generator (Beam defaults - max_new_tokens: 10, temperature: 0.75, top_k: 75, top_p: 0.98, repetition_penalty: None, presence_penalty: 1.3, frequency_penalty: 1.4): {test_prompt}... It responds: '{response_2}'.\")\n", + "\n", + " for perm_0 in generation_param_permutations:\n", + " response_0 = complete_text_beam(text=test_prompt,\n", + " max_new_tokens=max_new_tokens,\n", + " temperature=perm_0['temperature'],\n", + " top_k=perm_0['top_k'],\n", + " top_p=perm_0['top_p'],\n", + " repetition_penalty=perm_0['repetition_penalty'],\n", + " presence_penalty=perm_0['presence_penalty'],\n", + " frequency_penalty=perm_0['frequency_penalty'])\n", + " print(\n", + " f\"Trial #: {trial_id} Text Sample #: {test_sample_number} Perplexity: {result_0} GENERATE PARAMS: max_new_tokens={perm_0['max_new_tokens']} temperature={perm_0['temperature']}, top_k={perm_0['top_k']}, top_p={perm_0['top_p']}, repetition_penalty={perm_0['repetition_penalty']} presence_penalty={perm_0['presence_penalty']} frequency_penalty{perm_0['frequency_penalty']} PROMPT: '{test_prompt}' RESPONSE: '{response_0}'\")\n", + "\n", + "\n", + "prompt_samples = [\n", + " \"I saw the sun and it was as shining on the\",\n", + " \"And God said, Let there be light: and there \",\n", + " \"In the beginning God created the heavens\"\n", + "]\n", + "\n", + "\n", + "counter = 0\n", + "for sample in prompt_samples:\n", + " test_text(\n", + " test_prompt=sample,\n", + " max_new_tokens=MAX_NEW_TOKENS,\n", + " result_cutoff=15,\n", + " trial_id=trial_number,\n", + " test_sample_number=counter,\n", + " result_0=phase_i_a_result)\n", + " counter += 1\n", + "\n", + "\n", + "collect()\n" + ], + "metadata": { + "id": "hut-HAJjyvn-", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e05a9fb1-706e-4f26-e668-825f7df940c2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth the the the the the the the the the the the the the the'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth God beginning'.\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth. beginning created God'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 13 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 9 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 14 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' created. beginning God earthless earth beginning'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth God.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 10 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 16 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' God earth beginning created heavens. earth created'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' beginning created earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 9 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' created beginning earth heavens. God earth'\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' created earth beginning God heavens. earth'\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 10 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 9 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' God earth beginning created beginning God. earth heavens created beginning heavens earth. earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 9 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 10 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 18 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' created earth beginning God heavens. created earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 9 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 12 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' beginning earth God created earth heavens'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 26 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 16 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 26 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth created heavens earth\\Order.cpt. the beginning'\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the the.. the the the the the the the the the the.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' earth. the'.\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' earth. the.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: 'And God said, Let there be light: and there. earth the'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the. the earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: 'And God said, Let there be light: and there. the earth'\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the. earth'\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the. earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: 'And God said, Let there be light: and there. the earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the earth.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ''\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'In the beginning God created the heavens' RESPONSE: ' heavens heavens heavens heavens and heavens heavens heavens heavens heavens heavens heavens heavens and and'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth'.\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and. earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' was earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and. earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and. earth'\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and. earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and created earth.'\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "5885" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Stage I-b: Extended Training\n", + "\n", + "- Now, we take the best model from Stage I-a and continue training it on a larger dataset.\n", + "- This uses a streaming `tf.data.Dataset` generator to allow handling of much larger data sets without using more RAM.\n", + "- This would allow us to select far more samples, but for now, we select a small subset for this small scale environment.\n", + "\n", + "## Streaming Data Generator for Large Datasets\n", + "\n", + "\n", + "The **SampleExpansionGenerator** class, which we create below:\n", + "\n", + " - Applies and streams the same preprocessing logic to the raw text samples as we did in Stage I-a.\n", + " - However, it preprocesses one **sample expansion batch** at a time and stores the resulting expanded samples in memory.\n", + " - It then feeds the resulting expanded samples to the model in batches matching the **model's BATCH_SIZE** as requested by the training loop.\n", + " - **sample expansion batch** is not the same as **the model's BATCH_SIZE**.\n", + "\n", + "For example, we could train on a dataset of 10 \\** 6 samples, while setting the **sample expansion batch size** to 100 while the **model's batch size** is 10.\n", + " - 100 raw text samples will be expoanded at a time.\n", + " - This results in thousands of expanded sub-samples being queued and ready for the model to take.\n", + " - The model will take 10 of these at a time until it does not have 10 left to provide.\n", + " - Then, the generator will then preprocess another 100 text samples and garbage collect.\n", + "\n", + "This allows training on datasets that would be much larger than available memory after expansion, making the training scalable.\n", + "\n", + "\n", + "### The sample expansion batch size should be optimized to balance two opposing forces:\n", + "\n", + " - Memory pressure increases with the number of expanded samples held in memory.\n", + " - Delays are caused by switching back and forth between tensor operations and preprocessing when batches are too small.\n", + "\n" + ], + "metadata": { + "id": "tuhQx2kjy4nn" + } + }, + { + "cell_type": "code", + "source": [ + "# Replace your existing class and function with these:\n", + "class SampleExpansionGenerator:\n", + " def __init__(self,\n", + " raw_text_samples,\n", + " tokenizer,\n", + " sample_expansion_batch_size=50,\n", + " model_batch_size=10,\n", + " prompt_length_0=PROMPT_LENGTH,\n", + " max_seq_length=MAX_SEQ_LENGTH,\n", + " vocabulary_size=VOCABULARY_SIZE):\n", + "\n", + " self.raw_text_samples = raw_text_samples\n", + " self.tokenizer = tokenizer\n", + " self.sample_expansion_batch_size = sample_expansion_batch_size\n", + " self.model_batch_size = model_batch_size\n", + " self.prompt_length_0 = prompt_length_0\n", + " self.max_seq_length = max_seq_length\n", + " self.vocabulary_size = vocabulary_size\n", + " self.data = []\n", + " self.labels = []\n", + " self.current_index = 0\n", + "\n", + " def _expand_next_batch(self):\n", + " # If we've already processed all raw samples for this epoch, do nothing.\n", + " if self.current_index >= len(self.raw_text_samples):\n", + " return\n", + "\n", + " # Determine the next meta-batch\n", + " start_idx = self.current_index\n", + " end_idx = min(start_idx + self.sample_expansion_batch_size, len(self.raw_text_samples))\n", + "\n", + " batch_samples = self.raw_text_samples[start_idx:end_idx]\n", + " self.current_index = end_idx\n", + "\n", + " # Run prepare_data on this batch\n", + " input_ids_list, labels_list, _ = prepare_data(\n", + " data_0=batch_samples,\n", + " tokenizer_0=self.tokenizer,\n", + " max_seq_length=self.max_seq_length,\n", + " prompt_length=self.prompt_length_0)\n", + "\n", + " # Add the new data to our internal queues\n", + " self.data.extend(input_ids_list)\n", + " self.labels.extend(labels_list)\n", + "\n", + " def __iter__(self):\n", + " # Reset to initial state for new epoch\n", + " self.current_index = 0\n", + " self.data = []\n", + " self.labels = []\n", + " return self\n", + "\n", + " def __next__(self):\n", + " # If queues are empty, try to expand them from raw samples\n", + " if not self.data:\n", + " self._expand_next_batch()\n", + "\n", + " # If they are STILL empty after trying to expand, the epoch is over.\n", + " if not self.data:\n", + " raise StopIteration\n", + "\n", + " # Pop and return one sample\n", + " input_sample = self.data.pop(0)\n", + " label_sample = self.labels.pop(0)\n", + "\n", + " return ((input_sample,), label_sample)\n", + "\n", + "\n", + "# Create the tf.data.Dataset\n", + "def create_dataset(raw_text_samples, tokenizer, sample_expansion_batch_size=50, model_batch_size=10) -> tf.data.Dataset:\n", + " generator_0 = SampleExpansionGenerator(\n", + " raw_text_samples=raw_text_samples,\n", + " tokenizer=tokenizer,\n", + " sample_expansion_batch_size=sample_expansion_batch_size,\n", + " model_batch_size=model_batch_size # Pass this parameter\n", + " )\n", + "\n", + " dataset = tf.data.Dataset.from_generator(\n", + " lambda: generator_0,\n", + " # output_signature=(\n", + " # (tf.TensorSpec(shape=(generator_0.max_seq_length,), dtype=tf.int32),),\n", + " # # tf.TensorSpec(shape=(generator_0.max_seq_length,), dtype=tf.int32), # Use generator's parameter\n", + " # tf.TensorSpec(shape=(generator_0.vocabulary_size,), dtype=tf.float32) # Use generator's parameter\n", + " # )\n", + " output_signature=(\n", + " (tf.TensorSpec(shape=(generator_0.max_seq_length,), dtype=tf.int32),), # A tuple containing ONE TensorSpec\n", + " tf.TensorSpec(shape=(generator_0.vocabulary_size,), dtype=tf.float32) # A single TensorSpec\n", + " )\n", + " )\n", + "\n", + " # Batch it\n", + " dataset = dataset.batch(model_batch_size)\n", + " dataset = dataset.prefetch(tf.data.AUTOTUNE) # Prefetch for performance\n", + " return dataset\n", + "\n", + "# Create training and validation datasets\n", + "phase_i_b_train_dataset = create_dataset(\n", + " raw_text_samples=phase_i_b_train_samples,\n", + " tokenizer=tokenizer,\n", + " sample_expansion_batch_size=PHASE_I_B_SAMPLE_EXPANSION_BATCH_SIZE,\n", + " model_batch_size=batch_size\n", + ")\n", + "\n", + "phase_i_b_val_dataset = create_dataset(\n", + " raw_text_samples=phase_i_b_val_samples,\n", + " tokenizer=tokenizer,\n", + " sample_expansion_batch_size=PHASE_I_B_SAMPLE_EXPANSION_BATCH_SIZE,\n", + " model_batch_size=batch_size\n", + ")\n" + ], + "metadata": { + "id": "MHWWE0xIzLRD" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "type(phase_i_b_train_dataset)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 121 + }, + "id": "HxwyQzSppQwp", + "outputId": "89a48aa5-c364-4057-98c4-fc4a291f448e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "tensorflow.python.data.ops.prefetch_op._PrefetchDataset" + ], + "text/html": [ + "
\n", + " 
tensorflow.python.data.ops.prefetch_op._PrefetchDataset
def __init__(input_dataset, buffer_size, slack_period=None, name=None)
/usr/local/lib/python3.12/dist-packages/tensorflow/python/data/ops/prefetch_op.pyA `Dataset` that asynchronously prefetches its input.
\n", + " \n", + "
" + ] + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "## Model Compilation for Stage I-b\n", + "\n", + "- We recompile the model with the same base optimizer (AdamW), however this time with a custom learning rate scheduler (WarmupCosineDecayRestarts), and for disambiguation, relevant metrics for this training phase. We also add an EarlyStopping callback which is mainly being used to restore the weights from the best epoch, if that turns out to not be the last epoch.\n", + "\n", + "\n", + "## For those wanting to scale this up, a word to point out:\n", + "\n", + "The parameters for the learning rate scheduler may need to be optimized. They will be different for your data. Alternatively, you can remove the learning rate scheduler if this is too much trail and error.\n", + "\n", + "- We set the starting learning rate at: 0.0039295722955565125\n", + "- We set warmup steps to 1140, which for the data selected is 15 epochs.\n", + "- We set first decay steps to 1900, which for this data set is about 25 epochs.\n", + "\n", + "Also:\n", + "\n", + "Additionally, the early stopping callback will likely need to be adjusted. When training at scale, you may use a lower learning rate and a larger number of epochs, as well as a larger value for the start_from_epoch parameter (which specifies when to begin tracking the metric for early stopping).\n", + "\n", + "FYI, this is the custom scheduler we imported from cerebrosllmutils (CosineDecayRestarts augmented with warmup steps):\n", + "\n", + "\n", + "```python\n", + "# A custom schedule: Cosine decay with some warm - up steps\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='WarmupCosineDecayRestarts')\n", + "class WarmupCosineDecayRestarts(tf.keras.optimizers.schedules.LearningRateSchedule):\n", + " \"\"\"\n", + " A learning rate schedule that combines a linear warmup with cosine decay restarts.\n", + " \"\"\"\n", + "\n", + " def __init__(self, initial_learning_rate, warmup_steps, first_decay_steps, t_mul=2.0, m_mul=1.0, alpha=0.0):\n", + " super().__init__()\n", + "\n", + " # Store all parameters as public attributes for get_config serialization\n", + " self.initial_learning_rate = initial_learning_rate\n", + " self.warmup_steps = warmup_steps\n", + " self.first_decay_steps = first_decay_steps\n", + " self.t_mul = t_mul\n", + " self.m_mul = m_mul\n", + " self.alpha = alpha\n", + "\n", + " # Create the CosineDecayRestarts schedule for internal logic.\n", + " # The parameters passed here are the same ones we just stored.\n", + " self.cosine_restarts_schedule = tf.keras.optimizers.schedules.CosineDecayRestarts(\n", + " initial_learning_rate=initial_learning_rate,\n", + " first_decay_steps=first_decay_steps,\n", + " t_mul=t_mul,\n", + " m_mul=m_mul,\n", + " alpha=alpha\n", + " )\n", + "\n", + "\n", + " def __call__(self, step):\n", + " step = tf.cast(step, dtype=tf.float32)\n", + "\n", + " # Calculate the learning rate for both phases unconditionally\n", + " warmup_lr = self.initial_learning_rate * step / self.warmup_steps\n", + "\n", + " # The cosine schedule is designed to start from step 0, so we give it\n", + " # the \"post-warmup\" step count.\n", + " decay_lr = self.cosine_restarts_schedule(step - self.warmup_steps)\n", + "\n", + " # Create a multiplier that is 1.0 during warmup and 0.0 after.\n", + " # tf.cast(condition, tf.float32) converts a boolean tensor to 1.0 or 0.0.\n", + " warmup_multiplier = tf.cast(step < self.warmup_steps, tf.float32)\n", + "\n", + " # The decay multiplier is the opposite.\n", + " decay_multiplier = 1.0 - warmup_multiplier\n", + "\n", + " # Combine the two learning rates. Only one will be active at a time.\n", + " return (warmup_multiplier * warmup_lr) + (decay_multiplier * decay_lr)\n", + "\n", + " def get_config(self):\n", + " # Use the stored public attributes for the config.\n", + " # This bypasses the issue of accessing private attributes (_t_mul) from\n", + " # the nested Keras object, which can be brittle.\n", + " config = {\n", + " \"initial_learning_rate\": self.initial_learning_rate,\n", + " \"warmup_steps\": self.warmup_steps,\n", + " \"first_decay_steps\": self.first_decay_steps,\n", + " \"t_mul\": self.t_mul,\n", + " \"m_mul\": self.m_mul,\n", + " \"alpha\": self.alpha,\n", + " }\n", + "\n", + " # Use from_config to properly allow deserialization\n", + " return config\n", + "```\n", + "\n" + ], + "metadata": { + "id": "DPaeJKEzzlPw" + } + }, + { + "cell_type": "code", + "source": [ + "# Define loss and metrics for Phase I-b\n", + "phase_i_b_loss = tf.keras.losses.CategoricalCrossentropy()\n", + "phase_i_b_categorical_accuracy = tf.keras.metrics.CategoricalAccuracy()\n", + "phase_i_b_perplexity = Perplexity(name=\"perplexity_phase_i_b\")\n", + "\n", + "# Create the learning rate schedule instance\n", + "lr_scheduler = WarmupCosineDecayRestarts(\n", + " initial_learning_rate=INITIAL_LR_STAGE_I_B,\n", + " warmup_steps=WARMUP_STEPS,\n", + " first_decay_steps=FIRST_DECAY_STEPS_STAGE_I_B,\n", + " t_mul=1.0,\n", + " m_mul=0.9,\n", + " alpha=0.01\n", + ")\n", + "\n", + "# Recompile the existing model\n", + "generator.model.compile(\n", + " loss=phase_i_b_loss,\n", + " metrics=[phase_i_b_categorical_accuracy, phase_i_b_perplexity],\n", + " optimizer=tf.keras.optimizers.AdamW(\n", + " learning_rate=lr_scheduler,\n", + " weight_decay=phase_i_b_weight_decay,\n", + " gradient_accumulation_steps=phase_i_b_gradient_accumulation_steps\n", + " ),\n", + " jit_compile=True\n", + ")\n", + "\n", + "# Define the Early Stopping callback\n", + "early_stopping = tf.keras.callbacks.EarlyStopping(\n", + " monitor='perplexity_phase_i_b', # Monitor validation perplexity\n", + " patience=10, # Number of epochs with no improvement after which training will be stopped.\n", + " verbose=1,\n", + " restore_best_weights=True, # Restores model weights from the epoch with the best value of the monitored metric.\n", + " mode='min',\n", + " start_from_epoch=40\n", + ")\n", + "\n", + "\n", + "callbacks_list = [early_stopping]\n" + ], + "metadata": { + "id": "GGkEVa2dzOtf" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Run Stage I-b Training\n", + "\n", + "- We start the training process using the model.fit method with the new datasets and callbacks to continue training the same model on another dataset. In our at scale runs, both the previous stage and this stage are dene on far more data." + ], + "metadata": { + "id": "y_K5nLzVz_-b" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "\n", + "\n", + "\n", + "# print(\"Calculating steps per epoch...\")\n", + "# train_steps = sum(1 for _ in phase_i_b_train_dataset)\n", + "# val_steps = sum(1 for _ in phase_i_b_val_dataset)\n", + "# print(f\"Calculated training steps per epoch: {train_steps}\")\n", + "# print(f\"Calculated validation steps: {val_steps}\")\n", + "\n", + "# Train the model\n", + "phase_i_b_history = generator.model.fit(\n", + " x=phase_i_b_train_dataset,\n", + " validation_data=phase_i_b_val_dataset,\n", + " epochs=phase_i_b_epochs,\n", + " callbacks=callbacks_list\n", + ")\n", + "\n", + "# Store history and get the best validation perplexity\n", + "phase_i_b_history = pd.DataFrame(phase_i_b_history.history)\n", + "result_phase_i_b = float(phase_i_b_history['perplexity_phase_i_b'].min())\n", + "f\"Result of Stage 1-b training {result_phase_i_b}\"\n" + ], + "metadata": { + "id": "3GGqvlIl0FvV", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "0daf05b2-7072-4818-8b47-a05558b33470" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/53\n", + " 76/Unknown \u001b[1m69s\u001b[0m 636ms/step - categorical_accuracy: 0.0389 - loss: 13.6508 - perplexity_phase_i_b: 966782.2500" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/trainers/epoch_iterator.py:160: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n", + " self._interrupted_warning()\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 690ms/step - categorical_accuracy: 0.0388 - loss: 13.6471 - perplexity_phase_i_b: 962529.6250 - val_categorical_accuracy: 0.0492 - val_loss: 11.5516 - val_perplexity_phase_i_b: 103939.8906\n", + "Epoch 2/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 609ms/step - categorical_accuracy: 0.0164 - loss: 13.8992 - perplexity_phase_i_b: 2969392.7500 - val_categorical_accuracy: 0.0492 - val_loss: 12.0771 - val_perplexity_phase_i_b: 175791.3594\n", + "Epoch 3/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 609ms/step - categorical_accuracy: 0.0250 - loss: 12.8039 - perplexity_phase_i_b: 402124.0625 - val_categorical_accuracy: 0.0656 - val_loss: 12.3528 - val_perplexity_phase_i_b: 231597.3438\n", + "Epoch 4/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 591ms/step - categorical_accuracy: 0.0415 - loss: 11.6595 - perplexity_phase_i_b: 140648.8125 - val_categorical_accuracy: 0.0492 - val_loss: 12.4123 - val_perplexity_phase_i_b: 245801.6250\n", + "Epoch 5/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 611ms/step - categorical_accuracy: 0.0439 - loss: 11.1954 - perplexity_phase_i_b: 73950.6797 - val_categorical_accuracy: 0.0492 - val_loss: 12.3395 - val_perplexity_phase_i_b: 228538.3750\n", + "Epoch 6/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 589ms/step - categorical_accuracy: 0.0816 - loss: 10.2579 - perplexity_phase_i_b: 29194.4102 - val_categorical_accuracy: 0.0656 - val_loss: 12.1179 - val_perplexity_phase_i_b: 183113.2031\n", + "Epoch 7/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 590ms/step - categorical_accuracy: 0.0590 - loss: 9.9608 - perplexity_phase_i_b: 22667.8711 - val_categorical_accuracy: 0.0492 - val_loss: 11.8740 - val_perplexity_phase_i_b: 143489.0312\n", + "Epoch 8/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 599ms/step - categorical_accuracy: 0.0593 - loss: 8.9806 - perplexity_phase_i_b: 8207.2861 - val_categorical_accuracy: 0.0328 - val_loss: 12.3863 - val_perplexity_phase_i_b: 239495.6562\n", + "Epoch 9/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 558ms/step - categorical_accuracy: 0.0661 - loss: 7.8740 - perplexity_phase_i_b: 2828.0859 - val_categorical_accuracy: 0.0164 - val_loss: 11.9790 - val_perplexity_phase_i_b: 159370.9219\n", + "Epoch 10/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 630ms/step - categorical_accuracy: 0.1062 - loss: 6.8127 - perplexity_phase_i_b: 987.0147 - val_categorical_accuracy: 0.0328 - val_loss: 11.2031 - val_perplexity_phase_i_b: 73360.1719\n", + "Epoch 11/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 604ms/step - categorical_accuracy: 0.0687 - loss: 5.7574 - perplexity_phase_i_b: 324.8636 - val_categorical_accuracy: 0.0164 - val_loss: 9.6458 - val_perplexity_phase_i_b: 15456.3154\n", + "Epoch 12/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 686ms/step - categorical_accuracy: 0.0943 - loss: 4.8160 - perplexity_phase_i_b: 124.1660 - val_categorical_accuracy: 0.0492 - val_loss: 8.6260 - val_perplexity_phase_i_b: 5574.9229\n", + "Epoch 13/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 608ms/step - categorical_accuracy: 0.1206 - loss: 4.4321 - perplexity_phase_i_b: 84.3652 - val_categorical_accuracy: 0.0328 - val_loss: 8.1588 - val_perplexity_phase_i_b: 3493.8950\n", + "Epoch 14/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 597ms/step - categorical_accuracy: 0.1237 - loss: 4.4953 - perplexity_phase_i_b: 91.3969 - val_categorical_accuracy: 0.0328 - val_loss: 8.3403 - val_perplexity_phase_i_b: 4189.2686\n", + "Epoch 15/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 579ms/step - categorical_accuracy: 0.0997 - loss: 4.2491 - perplexity_phase_i_b: 70.9299 - val_categorical_accuracy: 0.0656 - val_loss: 8.6163 - val_perplexity_phase_i_b: 5520.8823\n", + "Epoch 16/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 585ms/step - categorical_accuracy: 0.1204 - loss: 4.2542 - perplexity_phase_i_b: 70.9240 - val_categorical_accuracy: 0.0656 - val_loss: 8.7940 - val_perplexity_phase_i_b: 6594.3228\n", + "Epoch 17/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 577ms/step - categorical_accuracy: 0.1386 - loss: 4.2547 - perplexity_phase_i_b: 70.8944 - val_categorical_accuracy: 0.0984 - val_loss: 8.7318 - val_perplexity_phase_i_b: 6196.8022\n", + "Epoch 18/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 597ms/step - categorical_accuracy: 0.1209 - loss: 4.2489 - perplexity_phase_i_b: 70.4136 - val_categorical_accuracy: 0.0984 - val_loss: 8.9164 - val_perplexity_phase_i_b: 7453.2446\n", + "Epoch 19/53\n", + "\u001b[1m76/76\u001b[0m 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5970.1401\n", + "Epoch 22/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 589ms/step - categorical_accuracy: 0.1520 - loss: 4.1843 - perplexity_phase_i_b: 66.0555 - val_categorical_accuracy: 0.0656 - val_loss: 8.3286 - val_perplexity_phase_i_b: 4140.4941\n", + "Epoch 23/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 557ms/step - categorical_accuracy: 0.1807 - loss: 3.8604 - perplexity_phase_i_b: 48.0663 - val_categorical_accuracy: 0.0656 - val_loss: 8.6137 - val_perplexity_phase_i_b: 5506.4224\n", + "Epoch 24/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 608ms/step - categorical_accuracy: 0.1533 - loss: 3.9858 - perplexity_phase_i_b: 54.5812 - val_categorical_accuracy: 0.1148 - val_loss: 8.5935 - val_perplexity_phase_i_b: 5396.4331\n", + "Epoch 25/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 573ms/step - categorical_accuracy: 0.1230 - loss: 4.0118 - perplexity_phase_i_b: 55.6288 - val_categorical_accuracy: 0.1475 - val_loss: 8.6210 - val_perplexity_phase_i_b: 5547.1172\n", + "Epoch 26/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 564ms/step - categorical_accuracy: 0.1588 - loss: 3.8591 - perplexity_phase_i_b: 47.8675 - val_categorical_accuracy: 0.1148 - val_loss: 8.4999 - val_perplexity_phase_i_b: 4914.4688\n", + "Epoch 27/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 582ms/step - categorical_accuracy: 0.1900 - loss: 3.8535 - perplexity_phase_i_b: 47.2824 - val_categorical_accuracy: 0.0820 - val_loss: 8.7680 - val_perplexity_phase_i_b: 6425.2207\n", + "Epoch 28/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 593ms/step - categorical_accuracy: 0.1927 - loss: 3.6720 - perplexity_phase_i_b: 39.7386 - val_categorical_accuracy: 0.0656 - val_loss: 8.7999 - val_perplexity_phase_i_b: 6633.3721\n", + "Epoch 29/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 594ms/step - categorical_accuracy: 0.1848 - loss: 3.8259 - perplexity_phase_i_b: 46.2804 - val_categorical_accuracy: 0.0656 - val_loss: 8.6051 - val_perplexity_phase_i_b: 5459.4458\n", + "Epoch 30/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 597ms/step - categorical_accuracy: 0.1691 - loss: 3.6890 - perplexity_phase_i_b: 40.3801 - val_categorical_accuracy: 0.0984 - val_loss: 8.5689 - val_perplexity_phase_i_b: 5265.4810\n", + "Epoch 31/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 587ms/step - categorical_accuracy: 0.1774 - loss: 3.6971 - perplexity_phase_i_b: 40.6956 - val_categorical_accuracy: 0.0984 - val_loss: 8.7037 - val_perplexity_phase_i_b: 6025.3599\n", + "Epoch 32/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 573ms/step - categorical_accuracy: 0.1597 - loss: 3.6218 - perplexity_phase_i_b: 37.8592 - val_categorical_accuracy: 0.0984 - val_loss: 8.7827 - val_perplexity_phase_i_b: 6520.5991\n", + "Epoch 33/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 573ms/step - categorical_accuracy: 0.2066 - loss: 3.6265 - perplexity_phase_i_b: 38.0441 - val_categorical_accuracy: 0.0984 - val_loss: 8.7695 - val_perplexity_phase_i_b: 6434.8853\n", + "Epoch 34/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 550ms/step - categorical_accuracy: 0.1622 - loss: 3.7388 - perplexity_phase_i_b: 42.4272 - val_categorical_accuracy: 0.1148 - val_loss: 8.6601 - val_perplexity_phase_i_b: 5768.0454\n", + "Epoch 35/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 537ms/step - categorical_accuracy: 0.1974 - loss: 3.4737 - perplexity_phase_i_b: 32.6702 - val_categorical_accuracy: 0.1148 - val_loss: 8.6486 - val_perplexity_phase_i_b: 5702.0361\n", + "Epoch 36/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 603ms/step - categorical_accuracy: 0.1640 - loss: 3.5527 - perplexity_phase_i_b: 35.4395 - val_categorical_accuracy: 0.1148 - val_loss: 8.7015 - val_perplexity_phase_i_b: 6011.7910\n", + "Epoch 37/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 590ms/step - categorical_accuracy: 0.1779 - loss: 3.5903 - perplexity_phase_i_b: 36.4963 - val_categorical_accuracy: 0.1148 - val_loss: 8.7223 - val_perplexity_phase_i_b: 6138.1729\n", + "Epoch 38/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 598ms/step - categorical_accuracy: 0.1935 - loss: 3.5401 - perplexity_phase_i_b: 34.7298 - val_categorical_accuracy: 0.1148 - val_loss: 8.6995 - val_perplexity_phase_i_b: 5999.7402\n", + "Epoch 39/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 622ms/step - categorical_accuracy: 0.2109 - loss: 3.5383 - perplexity_phase_i_b: 34.5639 - val_categorical_accuracy: 0.1148 - val_loss: 8.6650 - val_perplexity_phase_i_b: 5796.6436\n", + "Epoch 40/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 555ms/step - categorical_accuracy: 0.2047 - loss: 3.5124 - perplexity_phase_i_b: 33.9720 - val_categorical_accuracy: 0.1148 - val_loss: 8.7431 - val_perplexity_phase_i_b: 6267.4624\n", + "Epoch 41/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 576ms/step - categorical_accuracy: 0.1514 - loss: 3.5711 - perplexity_phase_i_b: 35.7887 - val_categorical_accuracy: 0.0656 - val_loss: 8.9814 - val_perplexity_phase_i_b: 7953.5283\n", + "Epoch 42/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 590ms/step - categorical_accuracy: 0.1761 - loss: 3.6074 - perplexity_phase_i_b: 37.1983 - val_categorical_accuracy: 0.0984 - val_loss: 9.0303 - val_perplexity_phase_i_b: 8352.2227\n", + "Epoch 43/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 571ms/step - categorical_accuracy: 0.1727 - loss: 3.6003 - perplexity_phase_i_b: 36.7872 - val_categorical_accuracy: 0.0328 - val_loss: 8.9927 - val_perplexity_phase_i_b: 8044.2207\n", + "Epoch 44/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 619ms/step - categorical_accuracy: 0.1786 - loss: 3.7416 - perplexity_phase_i_b: 42.6958 - val_categorical_accuracy: 0.1148 - val_loss: 9.1039 - val_perplexity_phase_i_b: 8990.1494\n", + "Epoch 45/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 594ms/step - categorical_accuracy: 0.2062 - loss: 3.6020 - perplexity_phase_i_b: 37.0046 - val_categorical_accuracy: 0.0984 - val_loss: 9.3867 - val_perplexity_phase_i_b: 11928.1768\n", + "Epoch 46/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 583ms/step - categorical_accuracy: 0.2035 - loss: 3.6276 - perplexity_phase_i_b: 37.9026 - val_categorical_accuracy: 0.0820 - val_loss: 9.5581 - val_perplexity_phase_i_b: 14159.1719\n", + "Epoch 47/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 590ms/step - categorical_accuracy: 0.1784 - loss: 3.4276 - perplexity_phase_i_b: 31.0932 - val_categorical_accuracy: 0.1148 - val_loss: 9.1575 - val_perplexity_phase_i_b: 9485.0088\n", + "Epoch 48/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 583ms/step - categorical_accuracy: 0.1864 - loss: 3.4227 - perplexity_phase_i_b: 31.1301 - val_categorical_accuracy: 0.1148 - val_loss: 9.1156 - val_perplexity_phase_i_b: 9095.7666\n", + "Epoch 49/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 622ms/step - categorical_accuracy: 0.2266 - loss: 3.4226 - perplexity_phase_i_b: 30.7439 - val_categorical_accuracy: 0.0820 - val_loss: 9.4648 - val_perplexity_phase_i_b: 12897.0039\n", + "Epoch 50/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 589ms/step - categorical_accuracy: 0.2455 - loss: 3.4171 - perplexity_phase_i_b: 30.9408 - val_categorical_accuracy: 0.0820 - val_loss: 9.4194 - val_perplexity_phase_i_b: 12325.3525\n", + "Epoch 51/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 596ms/step - categorical_accuracy: 0.2168 - loss: 3.2941 - perplexity_phase_i_b: 27.1144 - val_categorical_accuracy: 0.0984 - val_loss: 9.3049 - val_perplexity_phase_i_b: 10991.2559\n", + "Epoch 52/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 642ms/step - categorical_accuracy: 0.1940 - loss: 3.3548 - perplexity_phase_i_b: 28.8572 - val_categorical_accuracy: 0.0984 - val_loss: 9.1126 - val_perplexity_phase_i_b: 9068.9150\n", + "Epoch 53/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 610ms/step - categorical_accuracy: 0.2291 - loss: 3.3674 - perplexity_phase_i_b: 29.2831 - val_categorical_accuracy: 0.1311 - val_loss: 9.1200 - val_perplexity_phase_i_b: 9136.3135\n", + "Restoring model weights from the end of the best epoch: 53.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Result of Stage 1-b training 29.637819290161133'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 25 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Stage I-b: Model Evaluation and Serialization\n", + "\n", + "After extended training, we evaluate the final model performance and save the model and tokenizer for future use.\n" + ], + "metadata": { + "id": "y8Ej2P7D0T8R" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Final Generation Tests on the Stage I-b model checkpoint\n", + "\n", + "Confirm the model works after Stage I-b training." + ], + "metadata": { + "id": "dWlYvYBq0dio" + } + }, + { + "cell_type": "code", + "source": [ + "print(\"########### Phase I-b Model Checkpoint Generation Samples: ###########\")\n", + "\n", + "counter = 0\n", + "for sample in prompt_samples:\n", + " test_text(\n", + " test_prompt=sample,\n", + " max_new_tokens=MAX_NEW_TOKENS,\n", + " result_cutoff=60, #\n", + " trial_id=trial_number,\n", + " test_sample_number=counter,\n", + " result_0=result_phase_i_b\n", + " )\n", + " counter += 1\n" + ], + "metadata": { + "id": "YhGaTbGF0X_d", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8071bc5a-8520-4d13-82e1-cbd941297b4b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "########### Phase I-b Model Checkpoint Generation Samples: ###########\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ',,, and fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' for morning, over tree with, fruit lights bring fruit great livestock.''.\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', serve'to lights produce according each kind'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', greater and that creeping waters for fifth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' for, lights bird produce fourth to its with'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 64 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: 'Be and, image said birds creeping day. God'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 50 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' night, image to over fish creature earth.''\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' for, to birds bird, according forth every.' man animals'\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 19 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 30 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 30 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 21 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', for plant domin fruition day with'\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 12 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 15 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 10 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' for'lights, great waters eachBe.' fruit also'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 15 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', saying produce livestock for every lights-bearing day fifth give to.''\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 64 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', fifth give to livestock light fruitful its that day every so.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 65 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 68 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 68 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 67 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 67 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 66 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 67 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 64 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 14 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 17 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' waters,, for to bring its, fruit.' kind.' and its wild'\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'And God said, Let there be light: and there ' RESPONSE: 'And God said, Let there be light: and there,, and fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 50 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' to each the kind fruit that in great birds day. its'.\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 24 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' man was forth fruit great with lesser thing animals'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 50 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' man to each thing multiply the so fruit in that as saw'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 17 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' fly created the so.' livestock fruit according'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' each fruitful-bearing in animals as man was'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 26 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 21 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 19 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 20 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' that themBeh fruit man in great according forth signs fruit.''\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 13 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 22 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' them. fruit'\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 26 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' each created so animals'\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 30 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 15 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 14 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' man-bearing lesser so animals each.' wild'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 19 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 21 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 17 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' to man each bring kind fruit forth. its animals'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 50 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 18 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' so man each. fruit in as'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 65 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 67 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 66 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 66 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 64 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' man-bearing said forth so in them according signs fruit'\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'In the beginning God created the heavens' RESPONSE: ',,,,, and day day day lesser'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 15 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'In the beginning God created the heavens' RESPONSE: ',Let and was living he lesser so multiply seed fruitful livestock.'.\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' set, and said them over was man each it'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 49 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and fruitful, to was forth them'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 50 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', earth trees seed was and day good rule forth.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 49 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 49 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 50 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 49 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 49 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 21 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 21 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', and trees them said he day good upon,' thing. fruit'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', and trees was day to seed lesser he living earth each'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 30 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', and trees was he said day. each living he fruit so'\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 15 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 22 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 20 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 20 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 19 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 20 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 24 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 24 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', it earth creatures day living man lesser and he each'\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 30 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and was living day he rule trees., according'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', and trees it said upon man was forth day fruit each tree wing multiply'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' waters, and was so according each lesser multiply'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 64 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'In the beginning God created the heavens' RESPONSE: ',ed and to be it, multiply fruitful each'\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Save Model and Tokenizer\n", + "\n", + "- Finally, we save the tokenizer and the trained model weights to disk." + ], + "metadata": { + "id": "-oCAeR4n0mPW" + } + }, + { + "cell_type": "code", + "source": [ + "trial_number = 1 # Make sure to set this to a unique number:\n", + "# Serialize tokenizer\n", + "TOKENIZER_SAVE_PATH = f\"tokenizer-tr-{trial_number}-stage-i-b\"\n", + "tokenizer.save_pretrained(TOKENIZER_SAVE_PATH)\n", + "print(f\"Tokenizer saved to {TOKENIZER_SAVE_PATH}\")\n", + "\n", + "# Serialize model\n", + "MODEL_SAVE_PATH = f\"final_phase_ib_model_tr_{trial_number}-stage-i-b.keras\"\n", + "generator.save(MODEL_SAVE_PATH)\n", + "print(f\"Final model saved to {MODEL_SAVE_PATH}\")\n" + ], + "metadata": { + "id": "ziYdmmII0qfu", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "37a1153f-09a0-4274-9ca2-e280112e65e6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Tokenizer saved to tokenizer-tr-1-stage-i-b\n", + "Final model saved to final_phase_ib_model_tr_1-stage-i-b.keras\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Serialization Test\n", + "\n", + "- We run an external script (test_llm_serialization.py) to validate that the saved model and tokenizer can be loaded and used correctly." + ], + "metadata": { + "id": "y9Pvhcvl0uGt" + } + }, + { + "cell_type": "code", + "source": [ + "print(f\"๐Ÿงช Running serialization test for Stage I-b trial {trial_number}...\")\n", + "result = subprocess.run(\n", + " f\"python3 test_llm_serialization.py {TOKENIZER_SAVE_PATH} {MODEL_SAVE_PATH}\",\n", + " capture_output=True,\n", + " shell=True,\n", + " text=True # Use text=True for string output\n", + ")\n", + "\n", + "if result.returncode == 0:\n", + " print(\"โœ… Serialization test passed.\")\n", + " print(\"STDOUT:\", result.stdout)\n", + "else:\n", + " print(\"โŒ Serialization test failed.\")\n", + " print(\"STDERR:\", result.stderr)\n", + " if result.stdout:\n", + " print(\"STDOUT:\", result.stdout)\n" + ], + "metadata": { + "id": "qA5Cord40yID", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "389fe0bf-c935-4f49-dd4f-8eea8672c634" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "๐Ÿงช Running serialization test for Stage I-b trial 1...\n", + "โœ… Serialization test passed.\n", + "STDOUT: โœ… Tokenizer loaded successfully.\n", + "โœ… CerebrosNotGPT model loaded successfully.\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 19 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 26 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 30 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + "๐Ÿง  (serialized) Prompt: In the beginning God created the Generated Text from Serialized Model: 'In the beginning God created the, waters each trees and to living man according them'\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# And there you have it: What it takes to build an LLM from scratch using our novel architecture.\n" + ], + "metadata": { + "id": "z1lSMQ6i03XC" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "W6lcAxij-Z5r" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 0aca0307ebbfb4c4b0440e8aeaef5aa162c015fd Mon Sep 17 00:00:00 2001 From: David Thrower Date: Mon, 24 Nov 2025 19:18:45 -0500 Subject: [PATCH 3/4] Add copy of original Jupyter w/ execution metadata --- ...1_23_demo_train_an_llm_with_cerebros.ipynb | 6561 +++++++++++++++++ 1 file changed, 6561 insertions(+) create mode 100644 old/2025_11_23_demo_train_an_llm_with_cerebros.ipynb diff --git a/old/2025_11_23_demo_train_an_llm_with_cerebros.ipynb b/old/2025_11_23_demo_train_an_llm_with_cerebros.ipynb new file mode 100644 index 0000000..9004212 --- /dev/null +++ b/old/2025_11_23_demo_train_an_llm_with_cerebros.ipynb @@ -0,0 +1,6561 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Build our LLM From Scratch -\n", + "\n", + "## How Cerebros NotGPT works under the hood:\n", + "\n", + "\n", + "### This notebook demonstrates the end-to-end training pipeline that builds a small scale generative LLM from scratch, a small scale proof of concept for our own Cerebros NotGPT model, then fine tunes it on additional data.\n", + "\n", + "The process is divided into two main phases:\n", + "\n", + "- Phase I-a: Neural Architecture Search (NAS) - We use SimpleCerebrosRandomSearch to automatically discover an effective neural network architecture from a small dataset.\n", + "- Phase I-b: Extended Training - The best architecture found in Phase I-a is then trained on a larger dataset to improve its performance.\n", + "\n", + "Finally, the trained model is evaluated and serialized for future use.\n", + "\n", + "\n", + "## Setup and Configuration\n", + "\n", + "Note: This script is configured as a vanilla-scale demo environment (4 CPU / 16 GB RAM Linux with Python 3.12). No GPU is needed, and this will run in the free version of Google Colab. \n", + "\n", + "## Vanilla Demo\n", + "\n", + "- For production use, you would significantly increase the sample sizes and adjust other parameters accordingly.\n", + "- The quality of the text generated by this minimal demo (trained on 30 text samples at a sequence length of 40) does not represent the quality of a full-scale model generated from the same code.\n", + "- A script that can be modified to do such as availible at: https://github.com/david-thrower/cerebros-core-algorithm-alpha/blob/main/train_a_generative_llm.py" + ], + "metadata": { + "id": "nnsAHoJyWLed" + } + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NzJF6_JuWElV", + "outputId": "a0f3246f-0ccd-48ea-da55-86479bc0f93c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Python 3.12.12\n" + ] + } + ], + "source": [ + "! python --version" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Getting started: Download the repo and go to the main directory of the repo" + ], + "metadata": { + "id": "f6TD2XsKPJIY" + } + }, + { + "cell_type": "code", + "source": [ + "# Download the repo\n", + "! git clone https://github.com/david-thrower/cerebros-core-algorithm-alpha.git" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AcECFSs7WVsi", + "outputId": "9fd59935-35d4-4a08-9c8a-fb01fd3e4f03" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'cerebros-core-algorithm-alpha'...\n", + "remote: Enumerating objects: 8036, done.\u001b[K\n", + "remote: Counting objects: 100% (1737/1737), done.\u001b[K\n", + "remote: Compressing objects: 100% (321/321), done.\u001b[K\n", + "remote: Total 8036 (delta 1612), reused 1449 (delta 1411), pack-reused 6299 (from 2)\u001b[K\n", + "Receiving objects: 100% (8036/8036), 65.90 MiB | 21.67 MiB/s, done.\n", + "Resolving deltas: 100% (3116/3116), done.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# set the working directory\n", + "%cd cerebros-core-algorithm-alpha" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mCpJGfD2WfLj", + "outputId": "e0fe8c05-6154-41cd-f489-08cfd2ad0fa8" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/cerebros-core-algorithm-alpha/cerebros-core-algorithm-alpha\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Next install all dependencies.\n", + "\n", + "There are 2 requirement files:\n", + " - requirements.txt: The core requirements of the neural architecture search\n", + " - cicd-requirements.txt: Requirements for NLP and text generation" + ], + "metadata": { + "id": "yT4hPXOKPU_8" + } + }, + { + "cell_type": "code", + "source": [ + "# Install the requirements for the core algorithm\n", + "! pip install -r requirements.txt; pip install -r cicd-requirements.txt" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "nwElyEdpW90P", + "outputId": "170e2158-b7a9-49f0-ce63-22c4c7410f33" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: jax==0.5.3 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 1)) (0.5.3)\n", + "Requirement already satisfied: jaxlib==0.5.3 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 2)) (0.5.3)\n", + "Requirement already satisfied: pendulum==3.0.0 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 3)) (3.0.0)\n", + "Collecting tensorflow==2.20.0 (from -r requirements.txt (line 4))\n", + " Using cached tensorflow-2.20.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)\n", + "Collecting 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requirements.txt (line 9)) (1.3.3)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib==3.10.7->-r requirements.txt (line 9)) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib==3.10.7->-r requirements.txt (line 9)) (4.60.1)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib==3.10.7->-r requirements.txt (line 9)) (1.4.9)\n", + "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib==3.10.7->-r requirements.txt (line 9)) (11.3.0)\n", + "Requirement already satisfied: pyparsing>=3 in /usr/local/lib/python3.12/dist-packages (from matplotlib==3.10.7->-r requirements.txt (line 9)) (3.2.5)\n", + "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from astunparse>=1.6.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (0.45.1)\n", + "Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.19.2)\n", + "Requirement already satisfied: decorator in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (4.4.2)\n", + "Requirement already satisfied: pickleshare in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.7.5)\n", + "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (5.7.1)\n", + "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (3.0.52)\n", + "Requirement already satisfied: pygments in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (2.19.2)\n", + "Requirement already satisfied: backcall in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.2.0)\n", + "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.2.1)\n", + "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.12/dist-packages (from ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (4.9.0)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2>=2.9.6->pyvis==0.3.2->-r requirements.txt (line 7)) (3.0.3)\n", + "Requirement already satisfied: rich in /usr/local/lib/python3.12/dist-packages (from keras>=3.10.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (13.9.4)\n", + "Requirement already satisfied: namex in /usr/local/lib/python3.12/dist-packages (from keras>=3.10.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (0.1.0)\n", + "Requirement already satisfied: optree in /usr/local/lib/python3.12/dist-packages (from keras>=3.10.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (0.18.0)\n", + "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (3.4.4)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (3.11)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (2.5.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (2025.11.12)\n", + "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.20.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (3.10)\n", + "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.20.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (0.7.2)\n", + "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.20.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (3.1.3)\n", + "Requirement already satisfied: parso<0.9.0,>=0.8.4 in /usr/local/lib/python3.12/dist-packages (from jedi>=0.16->ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.8.5)\n", + "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.12/dist-packages (from pexpect>4.3->ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.7.0)\n", + "Requirement already satisfied: wcwidth in /usr/local/lib/python3.12/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=5.3.0->pyvis==0.3.2->-r requirements.txt (line 7)) (0.2.14)\n", + "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->keras>=3.10.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (4.0.0)\n", + "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.10.0->tensorflow==2.20.0->-r requirements.txt (line 4)) (0.1.2)\n", + "Using cached tensorflow-2.20.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (620.7 MB)\n", + "Using cached numpy-2.3.5-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (16.6 MB)\n", + "Using cached tensorboard-2.20.0-py3-none-any.whl (5.5 MB)\n", + "Installing collected packages: numpy, tensorboard, tensorflow\n", + " Attempting uninstall: numpy\n", + " Found existing installation: numpy 1.26.4\n", + " Uninstalling numpy-1.26.4:\n", + " Successfully uninstalled numpy-1.26.4\n", + " Attempting uninstall: tensorboard\n", + " Found existing installation: tensorboard 2.19.0\n", + " Uninstalling tensorboard-2.19.0:\n", + " Successfully uninstalled tensorboard-2.19.0\n", + " Attempting uninstall: tensorflow\n", + " Found existing installation: tensorflow 2.19.1\n", + " Uninstalling tensorflow-2.19.1:\n", + " Successfully uninstalled tensorflow-2.19.1\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "scikit-learn 1.4.1.post1 requires numpy<2.0,>=1.19.5, but you have numpy 2.3.5 which is incompatible.\n", + "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.3 which is incompatible.\n", + "tensorflow-text 2.19.0 requires tensorflow<2.20,>=2.19.0, but you have tensorflow 2.20.0 which is incompatible.\n", + "opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.3.5 which is incompatible.\n", + "numba 0.60.0 requires numpy<2.1,>=1.22, but you have numpy 2.3.5 which is incompatible.\n", + "opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.3.5 which is incompatible.\n", + "umap-learn 0.5.9.post2 requires scikit-learn>=1.6, but you have scikit-learn 1.4.1.post1 which is incompatible.\n", + "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.3.5 which is incompatible.\n", + "orbax-checkpoint 0.11.28 requires jax>=0.6.0, but you have jax 0.5.3 which is incompatible.\n", + "tensorflow-decision-forests 1.12.0 requires tensorflow==2.19.0, but you have tensorflow 2.20.0 which is incompatible.\n", + "flax 0.10.7 requires jax>=0.6.0, but you have jax 0.5.3 which is incompatible.\n", + "tf-keras 2.19.0 requires tensorflow<2.20,>=2.19, but you have tensorflow 2.20.0 which is incompatible.\n", + "imbalanced-learn 0.14.0 requires scikit-learn<2,>=1.4.2, but you have scikit-learn 1.4.1.post1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed numpy-2.3.5 tensorboard-2.20.0 tensorflow-2.20.0\n", + "Requirement already satisfied: tensorflow-text==2.19.0 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 1)) (2.19.0)\n", + "Requirement already satisfied: keras-nlp==0.19.0 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 2)) (0.19.0)\n", + "Requirement already satisfied: scikit-learn==1.4.1.post1 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 3)) (1.4.1.post1)\n", + "Requirement already satisfied: tensorflow-hub==0.16.1 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 4)) (0.16.1)\n", + "Requirement already satisfied: transformers==4.54.0 in /usr/local/lib/python3.12/dist-packages (from -r cicd-requirements.txt (line 5)) (4.54.0)\n", + "Collecting tensorflow<2.20,>=2.19.0 (from tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1))\n", + " Using cached tensorflow-2.19.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)\n", + "Requirement already satisfied: keras-hub==0.19.0 in /usr/local/lib/python3.12/dist-packages (from keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (0.19.0)\n", + "Collecting numpy<2.0,>=1.19.5 (from scikit-learn==1.4.1.post1->-r cicd-requirements.txt (line 3))\n", + " Using cached numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.4.1.post1->-r cicd-requirements.txt (line 3)) (1.16.3)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.4.1.post1->-r cicd-requirements.txt (line 3)) (1.5.2)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.4.1.post1->-r cicd-requirements.txt (line 3)) (3.6.0)\n", + "Requirement already satisfied: protobuf>=3.19.6 in /usr/local/lib/python3.12/dist-packages (from tensorflow-hub==0.16.1->-r cicd-requirements.txt (line 4)) (5.29.5)\n", + "Requirement already satisfied: tf-keras>=2.14.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow-hub==0.16.1->-r cicd-requirements.txt (line 4)) (2.19.0)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (3.20.0)\n", + "Requirement already satisfied: huggingface-hub<1.0,>=0.34.0 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (0.36.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (25.0)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (6.0.3)\n", + "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (2024.11.6)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (2.32.4)\n", + "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (0.21.4)\n", + "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (0.7.0)\n", + "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers==4.54.0->-r cicd-requirements.txt (line 5)) (4.67.1)\n", + "Requirement already satisfied: keras>=3.5 in /usr/local/lib/python3.12/dist-packages (from keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (3.10.0)\n", + "Requirement already satisfied: absl-py in /usr/local/lib/python3.12/dist-packages (from keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (1.4.0)\n", + "Requirement already satisfied: rich in /usr/local/lib/python3.12/dist-packages (from keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (13.9.4)\n", + "Requirement already satisfied: kagglehub in /usr/local/lib/python3.12/dist-packages (from keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (0.3.13)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (2025.3.0)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (4.15.0)\n", + "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (1.2.0)\n", + "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (25.9.23)\n", + "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.6.0)\n", + "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.2.0)\n", + "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (18.1.1)\n", + "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.4.0)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (75.2.0)\n", + "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (1.17.0)\n", + "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.2.0)\n", + "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (2.0.1)\n", + "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (1.76.0)\n", + "Collecting tensorboard~=2.19.0 (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1))\n", + " Using cached tensorboard-2.19.0-py3-none-any.whl.metadata (1.8 kB)\n", + "Requirement already satisfied: h5py>=3.11.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.15.1)\n", + "Requirement already satisfied: ml-dtypes<1.0.0,>=0.5.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.5.4)\n", + "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.54.0->-r cicd-requirements.txt (line 5)) (3.4.4)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.54.0->-r cicd-requirements.txt (line 5)) 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satisfied: markdown>=2.6.8 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.10)\n", + "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (0.7.2)\n", + "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow<2.20,>=2.19.0->tensorflow-text==2.19.0->-r cicd-requirements.txt (line 1)) (3.1.3)\n", + "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->keras-hub==0.19.0->keras-nlp==0.19.0->-r cicd-requirements.txt (line 2)) (4.0.0)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from 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+ " Successfully uninstalled numpy-2.3.5\n", + " Attempting uninstall: tensorboard\n", + " Found existing installation: tensorboard 2.20.0\n", + " Uninstalling tensorboard-2.20.0:\n", + " Successfully uninstalled tensorboard-2.20.0\n", + " Attempting uninstall: tensorflow\n", + " Found existing installation: tensorflow 2.20.0\n", + " Uninstalling tensorflow-2.20.0:\n", + " Successfully uninstalled tensorflow-2.20.0\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.3 which is incompatible.\n", + "opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", + "opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", + "pytensor 2.35.1 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", + "umap-learn 0.5.9.post2 requires scikit-learn>=1.6, but you have scikit-learn 1.4.1.post1 which is incompatible.\n", + "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", + "orbax-checkpoint 0.11.28 requires jax>=0.6.0, but you have jax 0.5.3 which is incompatible.\n", + "tensorflow-decision-forests 1.12.0 requires tensorflow==2.19.0, but you have tensorflow 2.19.1 which is incompatible.\n", + "flax 0.10.7 requires jax>=0.6.0, but you have jax 0.5.3 which is incompatible.\n", + "shap 0.50.0 requires numpy>=2, but you have numpy 1.26.4 which is incompatible.\n", + "imbalanced-learn 0.14.0 requires scikit-learn<2,>=1.4.2, but you have scikit-learn 1.4.1.post1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed numpy-1.26.4 tensorboard-2.19.0 tensorflow-2.19.1\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "numpy", + "tensorflow" + ] + }, + "id": "d3a167bbbde043ef9a994c35060fda79" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **RESTART THE SESSION**\n", + "\n", + "Then proceed to the next cell which imports all necessary libraries and defines global constants and hyperparameters for the entire pipeline.\n" + ], + "metadata": { + "id": "v69rLBcmXyGD" + } + }, + { + "cell_type": "code", + "source": [ + "! ls" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ubtKyfBQzFEW", + "outputId": "6cbe44e6-3ce7-4227-982a-88d0d36d2205" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "cerebros-core-algorithm-alpha sample_data\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 1. # **ONLY IF** the directory cerebros-core-algorithm-alpha is not still\n", + "# there, clone the directory again.\n", + "# ! git clone https://github.com/david-thrower/cerebros-core-algorithm-alpha.git\n", + "\n", + "# 2. Set the working directory (in the new session) - DO run this.\n", + "%cd cerebros-core-algorithm-alpha" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NemXTsYgfE0s", + "outputId": "ca92342f-1f82-42ee-8562-980b1c8dd849" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/cerebros-core-algorithm-alpha\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Verify we are in the right place:\n", + "! pwd" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "D3K4dSVQhrIc", + "outputId": "5a45fa94-1bb3-46ce-c362-27f456221fd6" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/cerebros-core-algorithm-alpha\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Standard library imports\n", + "import subprocess\n", + "import time\n", + "from gc import collect\n", + "\n", + "# Third-party library imports\n", + "import tensorflow as tf\n", + "import pandas as pd\n", + "import pendulum\n", + "from transformers import AutoTokenizer\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Cerebros specific imports\n", + "from cerebros.units.units import DenseUnit\n", + "from cerebros.simplecerebrosrandomsearch.simple_cerebros_random_search import SimpleCerebrosRandomSearch\n", + "from cerebros.denseautomlstructuralcomponent.dense_automl_structural_component import (\n", + " zero_7_exp_decay,\n", + " zero_95_exp_decay,\n", + " simple_sigmoid\n", + ")\n", + "from cerebrosllmutils.llm_utils import (\n", + " prepare_data,\n", + " InterleavedRoPE,\n", + " Perplexity,\n", + " CerebrosNotGPTConfig,\n", + " CerebrosNotGPT,\n", + " WarmupCosineDecayRestarts\n", + ")\n", + "\n", + "# Import the data source: Format List[str]\n", + "from vanilladatasets.web_english_bible import samples as bible\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WKCdCv96X4YX", + "outputId": "875f6626-4f4b-426c-c697-da9f186e440a" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/jaxlib/plugin_support.py:71: RuntimeWarning: JAX plugin jax_cuda12_plugin version 0.7.2 is installed, but it is not compatible with the installed jaxlib version 0.5.3, so it will not be used.\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Data and Training Constants\n", + "\n", + "These parameters control the amount of data used and the behavior of the training stages.\n", + "\n", + "- **PHASE_I_A_SAMPLES_TO_CREATE**: Size of the subset of the dataset used for the NAS (Neural Architecture Search) stage (number of text samples).\n", + "- **PHASE_I_B_SAMPLES_TO_CREATE**: Number of samples to use for the main training task stage after Neural Architecture Search is completed.\n", + "- **PHASE_I_B_VAL_SPLIT**: Fraction of data for validation in Phase I-b.\n", + "- **PHASE_I_B_SAMPLE_EXPANSION_BATCH_SIZE**: Batch size for preprocessing in Phase I-b to manage RAM.\n", + "- **PROMPT_LENGTH**: Number of tokens provided to the model to predict the next token. It is recommended to keep this as 1.\n" + ], + "metadata": { + "id": "rK0LZP7KbQqm" + } + }, + { + "cell_type": "code", + "source": [ + "# Samples to use for the neural architecture search stage\n", + "PHASE_I_A_SAMPLES_TO_CREATE = 10\n", + "\n", + "# Samples to use for the main training stage\n", + "PHASE_I_B_SAMPLES_TO_CREATE = 20\n", + "PHASE_I_B_VAL_SPLIT = 0.15\n", + "\n", + "# For Stage I-b, we preprocess in batches to avoid high RAM usage.\n", + "PHASE_I_B_SAMPLE_EXPANSION_BATCH_SIZE = 10\n", + "\n", + "# How many tokens to provide before expecting the next token to be predicted.\n", + "PROMPT_LENGTH = 1\n" + ], + "metadata": { + "id": "vywbZQxAZC9R" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Model and Embedding Constants\n", + "\n", + "These constants define the size and shape of the model's text processing components.\n", + "\n", + "- **MAX_SEQ_LENGTH**: The maximum sequence length the model will handle. This has a linear relationship with RAM/CPU usage.\n", + "- **tokenizer_checkpoint**: The Hugging Face model to use for tokenization.\n", + "- **EMBEDDING_N**: A factor to determine the embedding dimensionality (EMBEDDING_DIM = EMBEDDING_N * 2). A factor to determine the embedding dimensionality (EMBEDDING_DIM = EMBEDDING_N * 2). The resulting embedding dimensionality (EMBEDDING_DIM) for InterleavedRoPE must be an even number. Using this parameter as a proxy, rather than setting EMBEDDING_DIM directly, acts as a guard rail to ensure this constraint is met.\n", + "- **PROJECTION_N**: Controls the size of a projection layer after embedding. Increasing this value can significantly increase RAM usage.\n" + ], + "metadata": { + "id": "5jK5wbA5b8se" + } + }, + { + "cell_type": "code", + "source": [ + "# Text encoding / embedding related constants\n", + "MAX_SEQ_LENGTH = 40\n", + "\n", + "# Tokenization\n", + "tokenizer_checkpoint = \"HuggingFaceTB/SmolLM3-3B\"\n", + "tokenizer = AutoTokenizer.from_pretrained(tokenizer_checkpoint)\n", + "\n", + "# Add special tokens for potential instruction-following formats\n", + "special_tokens = {\n", + " \"additional_special_tokens\": [\"\", \"\", \"\", \"\"]\n", + "}\n", + "tokenizer.add_special_tokens(special_tokens)\n", + "\n", + "VOCABULARY_SIZE = len(tokenizer)\n", + "\n", + "# For InterleavedRoPE, the embedding output dim must be an even number.\n", + "EMBEDDING_N = 6\n", + "EMBEDDING_DIM = int(EMBEDDING_N * 2)\n", + "\n", + "# Size of the projection layer. Keep low to manage RAM.\n", + "PROJECTION_N = 1\n" + ], + "metadata": { + "id": "4Kka_A4tb3aJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6c85d1ae-52f4-4ddf-d768-ea5781b1b7da" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Stage I-a (NAS) Hyperparameters\n", + "\n", + "These parameters control the Neural Architecture Search process.\n", + "\n", + "- **moities_to_try**: Number of different layer permutations to try.\n", + "- **tries_per_moity**: Number of topologies to try for each permutation.\n", + "- **epochs, batch_size, learning_rate**: Standard training parameters for the NAS stage.\n", + "- **predecessor_level_connection_affinity_factor_first**: Controls connectivity density between the Input layer and the first level of Dense layers.\n", + "- **predecessor_level_connection_affinity_factor_main**: Controls connectivity density between the Input layer and the first level of Dense layers and the subsequent level of Dense layers, as well as all subsequent vertical connectivity.\n", + "- **p_lateral_connection, num_lateral_connection_tries_per_unit**: Control the density of lateral connectivity between Dense layers on the same row.\n", + "- **minimum_levels, maximum_levels**: Number of **rows of** Dense layers in the architecture grid.\n", + "- **minimum_units_per_level, maximum_units_per_level**: Number of Dense layers per row.\n", + "- **minimum_neurons_per_unit, maximum_neurons_per_unit**: The number of neurons for each Dense layer unit.\n" + ], + "metadata": { + "id": "MeoWtePacWz_" + } + }, + { + "cell_type": "code", + "source": [ + "# Cerebros [non-HP-tunable] configurables for NAS\n", + "moities_to_try = 3\n", + "tries_per_moity = 1\n", + "\n", + "### Main tunable hyperparameters for NAS ##\n", + "\n", + "POSITIONAL_EMBEDDING_DROPOUT = 0.7651951380000674\n", + "activation = 'softplus'\n", + "\n", + "# Vertical connectivity hyperparameters\n", + "predecessor_level_connection_affinity_factor_first = 17.851026458010523\n", + "predecessor_level_connection_affinity_factor_main = 21.487301631581428\n", + "\n", + "# Lateral connectivity hyperparameters\n", + "max_consecutive_lateral_connections = 7\n", + "p_lateral_connection = 0.24927354102044022\n", + "num_lateral_connection_tries_per_unit = 32\n", + "learning_rate = 0.003025583248301791\n", + "epochs = 41\n", + "batch_size = 5\n", + "gradient_accumulation_steps = 4\n", + "\n", + "# Architecture grid constraints\n", + "minimum_levels = 2\n", + "maximum_levels = 2\n", + "minimum_units_per_level = 2\n", + "maximum_units_per_level = 2\n", + "minimum_neurons_per_unit = 2\n", + "maximum_neurons_per_unit = 2\n" + ], + "metadata": { + "id": "Wbowkxnbc4Zd" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Phase I-b (Extended Training) Hyperparameters\n", + "\n", + "These parameters are for fine-tuning the best model from Stage I-a.\n", + "\n", + "- INITIAL_LR_STAGE_I_B: Initial learning rate for this phase.\n", + "- WARMUP_EPOCHS_STAGE_I_B, WARMUP_STEPS: Parameters for the learning rate scheduler.\n", + "- phase_i_b_epochs: Number of epochs for extended training.\n", + "- phase_i_b_weight_decay: Weight decay for the optimizer.\n" + ], + "metadata": { + "id": "fcGTs9ASdXps" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "## Training Stage I-b parameters:\n", + "INITIAL_LR_STAGE_I_B = 0.0039295722955565125\n", + "WARMUP_EPOCHS_STAGE_I_B = 7\n", + "WARMUP_STEPS = 1140\n", + "FIRST_DECAY_STEPS_STAGE_I_B = 1900\n", + "phase_i_b_epochs = 53\n", + "phase_i_b_gradient_accumulation_steps = 7\n", + "phase_i_b_weight_decay = 0.01647018768215773 # For AdamW\n" + ], + "metadata": { + "id": "-znwaddIdiKU" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "# Generation Constants\n", + "\n", + "Parameters used during the text generation evaluation phase." + ], + "metadata": { + "id": "vy5y6OXhdvzV" + } + }, + { + "cell_type": "code", + "source": [ + "## Generation time configurables:\n", + "GENERATION_PROMPT_LEN = 25\n", + "MAX_NEW_TOKENS = MAX_SEQ_LENGTH - GENERATION_PROMPT_LEN" + ], + "metadata": { + "id": "JHjCz9qXd5Gq" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# **Data Preparation**\n", + "\n", + "Here, we load and subset the dataset for both training Stages.\n", + "\n", + "\n", + "We first split the Bible text samples into two sets: one for Phase I-a (NAS) and a larger one for Phase I-b (extended training).\n" + ], + "metadata": { + "id": "N7fJIZ1md-0Y" + } + }, + { + "cell_type": "code", + "source": [ + "# Get training data from the bible text samples\n", + "non_instruct_samples = bible[:PHASE_I_A_SAMPLES_TO_CREATE]\n", + "phase_i_b_samples = bible[PHASE_I_A_SAMPLES_TO_CREATE:PHASE_I_B_SAMPLES_TO_CREATE + PHASE_I_A_SAMPLES_TO_CREATE]\n", + "\n", + "print(f\"Samples from KJV bible consisting of {len(non_instruct_samples)} look like this (sub-sample of 3): {non_instruct_samples[:3]}\")\n" + ], + "metadata": { + "id": "jIFxWcBzeLjN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d46f8e34-3d7d-4fb4-dddc-bf1c45bae7ee" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Samples from KJV bible consisting of 10 look like this (sub-sample of 3): ['In the beginning God created the heavens and the earth.', \"The earth was formless and empty, with darkness over the deep and God's Spirit hovering over the waters.\", \"God said, 'Let there be light,' and there was light.\"]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Preprocess Data for Phase I-a (NAS)\n", + "\n", + "The Cerebros LLM is a single head model. This means that each time the model is called, it returns only the next token. It does not regurgitate the cumulative sequence, nor does it have a separate head for each position in the sequence.\n", + "\n", + "For both training stages, each text sample is expanded into multiple input/label pairs, which we call \"sub-samples.\" There is one \"sub-sample\" for each token in the range between the first token and the first occurrence of a padding token or the end of the sequence, whichever comes first.\n", + "\n", + "For example, the sequence [t1, t2, t3] becomes:\n", + "\n", + " Input: [t1, 2, 2, 2] Label: [t2] # One hot encoded to VOCABULARY_SIZE\n", + " Input: [t1, t2, 2, 2], Label: [t3]\n", + " Input: [t1, t2, t3, 2], Label: [2]\n", + "\n", + "For training Stage 1-a, we perform the entire expansion for its training data in memory. This is because the NAS does not yet support a tf.data.Dataset object. In the future, we may retrofit the NAS algorithm to support streaming preprocessing as well, allowing us to use a larger dataset for the NAS.\n", + "\n", + "For stage I-b, the extended training stage, the same operation is done in batches. This is because this operation significantly increases the amount of memory required. The main reason for this is the one-hot encoded label, where the vocabulary size is 128,260. Since we do this in batches, this allows for a virtually unlimited number of samples to be processed.\n", + "\n", + "For reference, this is the preprocessing being applied:\n", + "\n", + "```python\n", + "def prepare_data(\n", + " data_0: List[str],\n", + " tokenizer_0: Any,\n", + " max_seq_length: int = 1024,\n", + " prompt_length: int = 1) -> Tuple[List[List[int]], List[List[int]], int]:\n", + "\n", + "\n", + " all_input_ids = []\n", + " all_labels = []\n", + "\n", + " pad_token_id = tokenizer_0.pad_token_id\n", + "\n", + " # Tokenize all data at once for efficiency\n", + " tokenized_data = tokenizer_0(\n", + " data_0,\n", + " max_length=max_seq_length,\n", + " padding='max_length',\n", + " truncation=True,\n", + " add_special_tokens=False # We'll handle special tokens manually\n", + " )\n", + " vocab_size = len(tokenizer_0)\n", + "\n", + " # Get the token ID for \n", + " end_prompt_token_id = tokenizer_0.encode(\"\", add_special_tokens=False)[0]\n", + "\n", + " # Process each sample\n", + " for sample_tokens in tokenized_data['input_ids']:\n", + " # Find the index of token\n", + " try:\n", + " end_prompt_index = sample_tokens.index(end_prompt_token_id)\n", + " except ValueError:\n", + " # If not found, treat sample as a non-instruct sample\n", + " end_prompt_index = (\n", + " prompt_length - 1) # int(np.ceil(len(sample_tokens) * (1/3))) # 0 ## 1. Give it a fair starting place to predict the next word 2. reduce the number of expanded samples\n", + "\n", + " # Find first pad token after \n", + " first_pad_index = None\n", + " for i in range(end_prompt_index + 1, len(sample_tokens)):\n", + " if sample_tokens[i] == pad_token_id:\n", + " first_pad_index = i\n", + " break\n", + "\n", + " # If no pad token found, use the end of sequence\n", + " if first_pad_index is None:\n", + " first_pad_index = len(sample_tokens)\n", + "\n", + " # Apply sliding window from after to first pad token\n", + " # Start from end_prompt_index + 1 (first token to predict)\n", + " # End at first_pad_index - 1 (last token to predict)\n", + " for i in range(end_prompt_index + 1, first_pad_index):\n", + " # Input: from start up to (but not including) token i\n", + " input_ids = sample_tokens[:i]\n", + "\n", + " # Pad or truncate to max_seq_length\n", + " if len(input_ids) > max_seq_length:\n", + " input_ids = input_ids[:max_seq_length]\n", + " else:\n", + " input_ids = input_ids + [pad_token_id] * (max_seq_length - len(input_ids))\n", + "\n", + " # Label: one-hot encoding of token at position i\n", + " next_token = sample_tokens[i]\n", + " label = [0] * vocab_size\n", + " label[next_token] = 1\n", + "\n", + " all_input_ids.append(input_ids)\n", + " all_labels.append(label)\n", + "\n", + " # Add final sample with pad token as label to indicate termination\n", + " if first_pad_index < len(sample_tokens): # Only if there's actually a pad token\n", + " input_ids = sample_tokens[:first_pad_index]\n", + "\n", + " # Pad or truncate to max_seq_length\n", + " if len(input_ids) > max_seq_length:\n", + " input_ids = input_ids[:max_seq_length]\n", + " else:\n", + " input_ids = input_ids + [pad_token_id] * (max_seq_length - len(input_ids))\n", + "\n", + " # Label: one-hot encoding of pad token\n", + " label = [0] * vocab_size\n", + " label[pad_token_id] = 1\n", + "\n", + " all_input_ids.append(input_ids)\n", + " all_labels.append(label)\n", + "\n", + " return all_input_ids, all_labels, vocab_size\n", + "```\n" + ], + "metadata": { + "id": "8Tu8X9cVeQVD" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Preprocess data for Stage I-a training\n", + "x, y, vocab_size = prepare_data(data_0=non_instruct_samples,\n", + " tokenizer_0=tokenizer,\n", + " max_seq_length=MAX_SEQ_LENGTH,\n", + " prompt_length=PROMPT_LENGTH)\n", + "\n", + "# Split the preprocessed data for NAS training and validation\n", + "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.85, shuffle=False)\n", + "\n", + "# Package data into lists for the Cerebros AutoML component\n", + "x_train_tf = tf.constant(X_train, tf.int32)\n", + "y_train_tf = tf.constant(y_train, tf.float32)\n", + "x_train_packaged = [x_train_tf]\n", + "y_train_packaged = [y_train_tf]\n", + "\n", + "# Do the same for the validation data\n", + "x_test_tf = tf.constant(X_test, tf.int32)\n", + "y_test_tf = tf.constant(y_test, tf.float32)\n", + "x_test_packaged = [x_test_tf]\n", + "y_test_packaged = [y_test_tf]\n", + "\n", + "# Define input and output shapes for the AutoML model\n", + "INPUT_SHAPES = [(MAX_SEQ_LENGTH,)]\n", + "OUTPUT_SHAPES = [(VOCABULARY_SIZE)]\n" + ], + "metadata": { + "id": "EDyuTMLufYvs" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Train, Test Split of the Data for Stage I-b training\n", + "\n", + "We split the larger Phase I-b dataset into training and validation sets. Again, this dataset will be processed by a streaming generator in batches to avoid memory saturation and make the training more scalable. We will revisit that later." + ], + "metadata": { + "id": "zX60zcpykasl" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Split the phase I-b data set for training and validation\n", + "phase_i_b_train_samples, phase_i_b_val_samples = train_test_split(\n", + " phase_i_b_samples,\n", + " test_size=PHASE_I_B_VAL_SPLIT,\n", + " shuffle=False\n", + ")\n" + ], + "metadata": { + "id": "SMSdkFRPkg7D" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "phase_i_b_train_samples[:3]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Oqw-T7bOo1GD", + "outputId": "2e8f24fc-24c2-4a06-babb-550b676b7751" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[\"God said, 'Let the earth produce vegetation, seed-bearing plants, and fruit trees, each according to its kind,' and it was so.\",\n", + " 'The earth brought forth grass, seed-bearing herbs, and fruit trees, each with its seed, and God saw that it was good.',\n", + " 'There was evening and morning, the third day.']" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "source": [ + "X_train[:2]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Hv_52izIjOQ7", + "outputId": "e2972924-0190-4f16-9317-c00100486203" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[[644,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012],\n", + " [644,\n", + " 279,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012,\n", + " 128012]]" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Base Text Embedding Model Definition\n", + "\n", + "- Before we run the NAS, we define a base model that handles token embeddings and positional embeddings.\n", + "- The SimpleCerebrosRandomSearch will then attach its auto-generated lattice of dense layers on top of this base model.\n", + "- The Cerebros NAS takes an init parameter base_models: List[tf.keras.Model]\n" + ], + "metadata": { + "id": "11Ri4PtKktih" + } + }, + { + "cell_type": "code", + "source": [ + "####### Text embedding base model #####################\n", + "\n", + "inp = tf.keras.layers.Input(shape=(MAX_SEQ_LENGTH,), dtype=tf.int32)\n", + "\n", + "# Token embedding layer\n", + "embedded = tf.keras.layers.Embedding(\n", + " input_dim=VOCABULARY_SIZE,\n", + " output_dim=EMBEDDING_DIM,\n", + " input_length=MAX_SEQ_LENGTH,\n", + " mask_zero=False\n", + ")(inp)\n", + "\n", + "# Interleaved Rotary Positional Embedding (iRoPE)\n", + "position_embedding = InterleavedRoPE(\n", + " dim=EMBEDDING_DIM,\n", + " max_seq_len=MAX_SEQ_LENGTH,\n", + ")(embedded)\n", + "\n", + "# Concatenate token and positional embeddings\n", + "x = tf.keras.layers.Concatenate()([embedded, position_embedding])\n", + "x = tf.keras.layers.Dropout(POSITIONAL_EMBEDDING_DROPOUT)(x)\n", + "\n", + "# Flatten and project to the desired dimension\n", + "flattened = tf.keras.layers.Flatten()(x)\n", + "projected = tf.keras.layers.Dense(EMBEDDING_DIM * PROJECTION_N)(flattened)\n", + "\n", + "# Create the base Keras model\n", + "cerebros_base_model = tf.keras.Model(\n", + " inputs=inp,\n", + " outputs=projected\n", + ")\n" + ], + "metadata": { + "id": "tn1qrGISn_Pe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e76e091c-6e7f-4820-ef79-15143f1e6b64" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## FYI: The iRoPE Embedding:\n", + "\n", + "The RoPE embedding, and helper functions it depends on (previously imported from the local package cerebrosllmutils):\n", + "\n", + "- iRoPE: Interleaved Rotary Positional Embedding\n", + "- RoPE: Rotary Positional Embedding\n", + "- The Rotary Positional Embedding expresses positional relationships as angles, extends feasible context window.\n", + "- iRoPE: iRoPE applies the rotation in an interleaved manner and enables capturing more nuance and extending context windows feasible to around 2 million tokens.\n", + "\n", + "```python\n", + "# --- Base Rotary Positional Embedding\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='RotaryEmbedding')\n", + "class RotaryEmbedding(tf.keras.layers.Layer):\n", + " def __init__(self, dim, max_seq_len=1024, temperature=10000.0, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.dim = dim\n", + " # Ensure dim is even right at initialization\n", + " if self.dim % 2 != 0:\n", + " raise ValueError(f\"Embedding dimension `dim` ({self.dim}) must be even for RotaryEmbedding.\")\n", + " self.max_seq_len = max_seq_len\n", + " self.temperature = temperature\n", + " # *** No calculation or storage of inv_freq here or in build ***\n", + "\n", + " def build(self, input_shape):\n", + " # Build should primarily be for creating trainable weights, which we don't have.\n", + " # Call super().build() for Keras compatibility.\n", + " super().build(input_shape)\n", + "\n", + " def call(self, x): # Removed seq_len argument, calculate from x\n", + " shape = tf.shape(x)\n", + " batch_size = shape[0]\n", + " actual_seq_len = shape[1]\n", + "\n", + " # *** Calculate inv_freq inside call ***\n", + " inv_freq_base = tf.range(0, self.dim, 2, dtype=tf.float32)\n", + " inv_freq = 1.0 / (self.temperature ** (inv_freq_base / self.dim))\n", + " # Ensure inv_freq has the correct shape [dim/2]\n", + " inv_freq = tf.cast(inv_freq, dtype=x.dtype) # Match dtype early\n", + "\n", + " # Use actual_seq_len for calculations\n", + " position = tf.range(actual_seq_len, dtype=x.dtype) # Match dtype\n", + "\n", + " # Calculate sinusoid input using einsum or broadcasting\n", + " # Einsum approach: Ensure correct dimensions [seq_len, dim/2]\n", + " sinusoid_inp = tf.einsum(\"i,j->ij\", position, inv_freq)\n", + "\n", + " # Calculate sin and cos based on the actual sequence length\n", + " sin = tf.sin(sinusoid_inp)\n", + " cos = tf.cos(sinusoid_inp)\n", + "\n", + " # Repeat sin/cos for interleaving: [a, b] -> [a, a, b, b]\n", + " # Result needs shape [actual_seq_len, dim]\n", + " sin = tf.repeat(sin, 2, axis=-1)\n", + " cos = tf.repeat(cos, 2, axis=-1)\n", + "\n", + " # Expand dims for batch and tile\n", + " # Output shape needs to be [batch_size, actual_seq_len, dim]\n", + " # Add batch dimension: [1, actual_seq_len, dim]\n", + " sin = tf.expand_dims(sin, axis=0)\n", + " cos = tf.expand_dims(cos, axis=0)\n", + "\n", + " # Tile to match the batch size: [batch_size, actual_seq_len, dim]\n", + " sin = tf.tile(sin, [batch_size, 1, 1])\n", + " cos = tf.tile(cos, [batch_size, 1, 1])\n", + "\n", + " # Casting to x.dtype was already done for inv_freq, sin/cos will inherit\n", + " # sin = tf.cast(sin, x.dtype) # Already done via calculation chain\n", + " # cos = tf.cast(cos, x.dtype) # Already done via calculation chain\n", + "\n", + " # Return sin and cos needed by InterleavedRoPE\n", + " return sin, cos\n", + "\n", + " def get_config(self):\n", + " config = super().get_config()\n", + " config.update({\n", + " \"dim\": self.dim,\n", + " \"max_seq_len\": self.max_seq_len,\n", + " \"temperature\": self.temperature,\n", + " })\n", + " return config\n", + "\n", + " @classmethod\n", + " def from_config(cls, config):\n", + " return cls(**config)\n", + "\n", + "\n", + "# iRoPE helper functions\n", + "\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='split_alternate')\n", + "def split_alternate(x):\n", + " shape = tf.shape(x)\n", + " x = tf.reshape(x, [shape[0], shape[1], shape[2] // 2, 2])\n", + " x = tf.transpose(x, [0, 1, 3, 2])\n", + " x = tf.reshape(x, [shape[0], shape[1], -1])\n", + " return x\n", + "\n", + "\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='rotate_half')\n", + "def rotate_half(x):\n", + " x = split_alternate(x)\n", + " d = tf.shape(x)[-1]\n", + " rotated_x = tf.concat([-x[..., d // 2:], x[..., :d // 2]], axis=-1)\n", + " return tf.reshape(rotated_x, tf.shape(x))\n", + "\n", + "\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='apply_rotary_pos_emb')\n", + "def apply_rotary_pos_emb(x, sin, cos):\n", + " cos = tf.reshape(cos, [tf.shape(cos)[0], tf.shape(cos)[1], -1])\n", + " sin = tf.reshape(sin, [tf.shape(sin)[0], tf.shape(sin)[1], -1])\n", + " x_rotated = x * cos + rotate_half(x) * sin\n", + " return x_rotated\n", + "\n", + "\n", + "# interleaved Rotary Postional Embedding (iRoPE)\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='InterleavedRoPE')\n", + "class InterleavedRoPE(tf.keras.layers.Layer):\n", + " def __init__(self, dim, max_seq_len=1024, **kwargs):\n", + " super().__init__(**kwargs)\n", + " if dim % 2 != 0:\n", + " raise ValueError(f\"Embedding dimension `dim` ({dim}) must be even for InterleavedRoPE.\")\n", + " self.dim = dim\n", + " self.max_seq_len = max_seq_len\n", + " # Instantiate the RotaryEmbedding layer\n", + " # Ensure the name is consistent if needed for saving/loading\n", + " self.rotary_emb = RotaryEmbedding(dim, max_seq_len, name=\"rotary_embedding\")\n", + "\n", + " def call(self, x):\n", + " # Get sin and cos from the RotaryEmbedding layer's call method\n", + " # *** Pass only 'x'. RotaryEmbedding calculates seq_len internally. ***\n", + " sin, cos = self.rotary_emb(x)\n", + "\n", + " # Apply the positional embeddings\n", + " x_embedded = apply_rotary_pos_emb(x, sin, cos)\n", + " return x_embedded\n", + "\n", + " def get_config(self):\n", + " config = super().get_config()\n", + " config.update({\n", + " \"dim\": self.dim,\n", + " \"max_seq_len\": self.max_seq_len,\n", + " })\n", + " # Keras handles nested layer serialization automatically\n", + " return config\n", + "\n", + " @classmethod\n", + " def from_config(cls, config):\n", + " # Keras handles nested layer restoration automatically\n", + " return cls(**config)\n", + "```" + ], + "metadata": { + "id": "CXtYv20vpkMY" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Custom metric Perplexity (previously imported from the local package cerebrosllmutils):\n", + "\n", + "Since there is not a Perplexity metric in tensorflow.keras.metrics, we created our own, and one designed for this single - head model.\n", + "\n", + "## This is what it looks like:\n", + "\n", + "```python\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='Perplexity')\n", + "class Perplexity(tf.keras.metrics.Metric):\n", + " \"\"\"\n", + " Computes perplexity, defined as e^(categorical crossentropy).\n", + " \"\"\"\n", + "\n", + " def __init__(self, name='perplexity', **kwargs):\n", + " super().__init__(name=name, **kwargs)\n", + " self.total_crossentropy = self.add_weight(name='total_crossentropy', initializer='zeros')\n", + " self.count = self.add_weight(name='count', initializer='zeros')\n", + "\n", + " def update_state(self, y_true, y_pred, sample_weight=None):\n", + " # Calculate categorical crossentropy\n", + " crossentropy = tf.keras.losses.categorical_crossentropy(y_true, y_pred)\n", + "\n", + " # Update the running sum of crossentropy and the count of samples\n", + " self.total_crossentropy.assign_add(tf.reduce_sum(crossentropy))\n", + " self.count.assign_add(tf.cast(tf.shape(y_true)[0], dtype=tf.float32))\n", + "\n", + " def result(self):\n", + " # Compute the average crossentropy\n", + " average_crossentropy = self.total_crossentropy / self.count\n", + " # Compute perplexity as e^(average crossentropy)\n", + " return tf.exp(average_crossentropy)\n", + "\n", + " def reset_state(self):\n", + " # Reset the state variables\n", + " self.total_crossentropy.assign(0.0)\n", + " self.count.assign(0.0)\n", + "```\n" + ], + "metadata": { + "id": "uN3adqRLo61X" + } + }, + { + "cell_type": "code", + "source": [ + "# Custom metric: Perplexity\n", + "perplexity_metric = Perplexity()" + ], + "metadata": { + "id": "_8uTBW_to7iQ" + }, + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "# Stage I-a training: Neural Architecture Search (NAS)\n", + "\n", + "We now run the SimpleCerebrosRandomSearch to find the best performing architecture based on the training data and the base model. The search aims to minimize the perplexity in the train set. The search aims to minimize the perplexity in the training set. Obviously, in a full - scale run, we would use the validation set's value.\n", + "\n", + "- The Cerebros NAS will parse a block composed of rows (Levels) of multiple Dense layers (Units) with an overlapping, interleaved, interwoven topology both laterally between Dense layers on the same row and vertically between layers on different levels.\n", + "- This topology emulates the neuroscience principle of modularity.\n", + "- This topology allows local clusters of densely connected neurons to learn specialized fragments of a problem, while allowing efficient communication between these clusters to coordinate among themselves to compose a solution to a complex problem.\n", + "\n", + "For the deep technical details of how Cerebros NAS works: [How Cerebros NAS Works](https://github.com/david-thrower/cerebros-core-algorithm-alpha/blob/277-attempt-to-imporve-parameters-on--dev-branch-275/documentation/cerebros-technical-details.md)\n", + "\n", + "## This is what a neural network parsed by Cerebros looks like:\n", + "\n", + "- Green triangles: Input layers\n", + "- Blue squares: Concatenate layer -> [BatchNormalization | Dropout]\n", + "- Pink ovals: Hidden Dense layers\n", + "- Red oval: Output Dense layer\n" + ], + "metadata": { + "id": "tWjbHiHRMhR4" + } + }, + { + "cell_type": "markdown", + "source": [ + "![Brain-lookalike1.png](data:image/png;base64,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)" + ], + "metadata": { + "id": "1wR8EVItNNh_" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "## For a more readable view of that this looks like\n", + "\n", + "![image.png](data:image/png;base64,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)\n" + ], + "metadata": { + "id": "_bXR1QxaLPiq" + } + }, + { + "cell_type": "code", + "source": [ + "######## Instantiate Cerebros Neural Architecture Search #######\n", + "\n", + "# Project metadata\n", + "TIME = pendulum.now(tz='America/New_York').__str__()[:16].replace('T', '_').replace(':', '_').replace('-', '_')\n", + "PROJECT_NAME = f'{TIME}_cerebros_not-gpt'\n", + "meta_trial_number = 42\n", + "\n", + "# Initialize the AutoML search\n", + "cerebros_automl = SimpleCerebrosRandomSearch(\n", + " unit_type=DenseUnit,\n", + " input_shapes=INPUT_SHAPES,\n", + " output_shapes=OUTPUT_SHAPES,\n", + " training_data=x_train_packaged,\n", + " labels=y_train_packaged,\n", + " validation_split=0.2,\n", + " direction='minimize',\n", + " metric_to_rank_by=\"perplexity\",\n", + " minimum_levels=minimum_levels,\n", + " maximum_levels=maximum_levels,\n", + " minimum_units_per_level=minimum_units_per_level,\n", + " maximum_units_per_level=maximum_units_per_level,\n", + " minimum_neurons_per_unit=minimum_neurons_per_unit,\n", + " maximum_neurons_per_unit=maximum_neurons_per_unit,\n", + " activation=activation,\n", + " final_activation='softmax',\n", + " number_of_architecture_moities_to_try=moities_to_try,\n", + " number_of_tries_per_architecture_moity=tries_per_moity,\n", + " predecessor_level_connection_affinity_factor_first=predecessor_level_connection_affinity_factor_first,\n", + " predecessor_level_connection_affinity_factor_main=predecessor_level_connection_affinity_factor_main,\n", + " predecessor_level_connection_affinity_factor_decay_main=zero_7_exp_decay,\n", + " max_consecutive_lateral_connections=max_consecutive_lateral_connections,\n", + " p_lateral_connection=p_lateral_connection,\n", + " p_lateral_connection_decay=zero_95_exp_decay,\n", + " num_lateral_connection_tries_per_unit=num_lateral_connection_tries_per_unit,\n", + " learning_rate=learning_rate,\n", + " loss=tf.keras.losses.CategoricalCrossentropy(),\n", + " metrics=[tf.keras.metrics.CategoricalAccuracy(), perplexity_metric],\n", + " epochs=epochs,\n", + " project_name=f\"{PROJECT_NAME}_meta_{meta_trial_number}\",\n", + " model_graphs='model_graphs',\n", + " batch_size=batch_size,\n", + " gradient_accumulation_steps=gradient_accumulation_steps,\n", + " meta_trial_number=meta_trial_number,\n", + " base_models=[cerebros_base_model],\n", + " train_data_dtype=tf.int32\n", + ")" + ], + "metadata": { + "id": "XV2q_5WEwBJ0" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Run the Cerebros Neural Architecture Search\n" + ], + "metadata": { + "id": "TJVLfmJ2virA" + } + }, + { + "cell_type": "code", + "source": [ + "cerebros_t0 = time.time()\n", + "phase_i_a_result_0 = cerebros_automl.run_random_search()\n", + "cerebros_t1 = time.time()\n", + "\n", + "# Report results\n", + "cerebros_time_all_models_min = (cerebros_t1 - cerebros_t0) / 60\n", + "models_tried = moities_to_try * tries_per_moity\n", + "cerebros_time_per_model = cerebros_time_all_models_min / models_tried\n", + "phase_i_a_result = float(phase_i_a_result_0)\n", + "\n", + "print(f\"Cerebros trained {models_tried} models in {cerebros_time_all_models_min:.2f} min. Average time per model: {cerebros_time_per_model:.2f} min.\")\n", + "print(f'Cerebros best perplexity achieved in Phase I-a is {phase_i_a_result}')" + ], + "metadata": { + "id": "ulL0EGnow5L7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "d56dd1ec-2f7b-4a3c-ecc6-75e595910367" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\rGlobal task progress: 0%|\u001b[38;2;22;206;235m \u001b[0m| 0/3 [00:00nnf>ceil\n", + "k is: 0 value is: [{'1': }]\n", + "0\n", + "k is: 1 value is: [{'2': }, {'2': }]\n", + "1\n", + "Trying to create level 1\n", + "We think level 1's predecessors are: [0]\n", + "k is: 2 value is: [{'128260': }]\n", + "2\n", + "Trying to create Final level 2\n", + "Trying to create level 2\n", + "We think level final level 2's predecessors are: [0, 1]\n", + "levels:\n", + "[0, 1, 2]\n", + "{'0': 'InputUnitModule'}\n", + "InputLevel.input_shapes [(40,)]\n", + "{'2': }\n", + "{'2': }\n", + "Debug: I am 2 selecting 1\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "Setting levels_unmaterialized[0] level_number 0 to have first successor: levels_unmaterialized[:1], having level_numbers of [1, 2]\n", + "Setting levels_unmaterialized[1] level_number 1 to have first successor: levels_unmaterialized[:2], having level_numbers of [2]\n", + "Debug: successor_connectivity_errors_2d []\n", + "$$$$$$>>>>> Base model: \n", + "InputUnit.input_shape: (40,)\n", + "{'2': }\n", + "{'2': }\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "Debug: successor_connectivity_errors_2d []\n", + "Debug: successor_connectivity_errors_2d []\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_0_DenseLevel_0000000000000001_tr_0_DenseUnit_0000000000000001_tr_0_0 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_0_DenseLevel_0000000000000001_tr_0_DenseUnit_0000000000000001_tr_0_1 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "{'128260': }\n", + "debug: meta_level_number\n", + "Debug: successor_connectivity_errors_2d []\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_0_FinalDenseLevel_0000000000000002_tr_0_FinalDenseUnit_0000000000000002_tr_0_0 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "inputs\n", + "\n", + "\n", + "outputs\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"NeuralNetworkFuture_0000000000000nan_tr_0_nn_materialized\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"NeuralNetworkFuture_0000000000000nan_tr_0_nn_materialized\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ - โ”‚\n", + "โ”‚ (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ functional โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m) โ”‚ \u001b[38;5;34m1,550,652\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mFunctional\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m1,584\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128260\u001b[0m) โ”‚ \u001b[38;5;34m50,919,220\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ], + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+              "โ”ƒ Layer (type)        โ”ƒ Output Shape      โ”ƒ    Param # โ”ƒ Connected to      โ”ƒ\n",
+              "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 40)        โ”‚          0 โ”‚ -                 โ”‚\n",
+              "โ”‚ (InputLayer)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ functional          โ”‚ (None, 12)        โ”‚  1,550,652 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Functional)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0]  โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0]  โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚          0 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[0][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚      1,584 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 128260)    โ”‚ 50,919,220 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+              "
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categorical_accuracy: 0.1685 - loss: 4.4140 - perplexity: 99.5634 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.1954 - val_perplexity: 537862.5000\n", + "Epoch 29/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 517ms/step - categorical_accuracy: 0.0568 - loss: 5.8209 - perplexity: 412.2969 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.3590 - val_perplexity: 633498.9375\n", + "Epoch 30/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 748ms/step - categorical_accuracy: 0.1864 - loss: 4.9144 - perplexity: 157.2443 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.5253 - val_perplexity: 748103.1875\n", + "Epoch 31/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 507ms/step - categorical_accuracy: 0.1052 - loss: 8.2503 - perplexity: 22384.6094 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.6228 - val_perplexity: 824754.3750\n", + "Epoch 32/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 612ms/step - categorical_accuracy: 0.4431 - loss: 4.0581 - perplexity: 75.1973 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.0377 - val_perplexity: 1248790.8750\n", + "Epoch 33/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 766ms/step - categorical_accuracy: 0.2086 - loss: 5.6123 - perplexity: 301.7467 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.2131 - val_perplexity: 1488169.6250\n", + "Epoch 34/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 569ms/step - categorical_accuracy: 0.2919 - loss: 4.4319 - perplexity: 154.5172 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.2928 - val_perplexity: 1611684.3750\n", + "Epoch 35/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 585ms/step - categorical_accuracy: 0.1802 - loss: 5.1381 - perplexity: 190.7273 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.4868 - val_perplexity: 1956789.0000\n", + "Epoch 36/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 639ms/step - categorical_accuracy: 0.1719 - loss: 4.6314 - perplexity: 111.0518 - val_categorical_accuracy: 0.1667 - val_loss: 14.5656 - val_perplexity: 2117109.5000\n", + "Epoch 37/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 693ms/step - categorical_accuracy: 0.0839 - loss: 6.8925 - perplexity: 1205.9113 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.6420 - val_perplexity: 2285232.2500\n", + "Epoch 38/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 594ms/step - categorical_accuracy: 0.2530 - loss: 5.8083 - perplexity: 927.8478 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.6140 - val_perplexity: 2222210.0000\n", + "Epoch 39/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 557ms/step - categorical_accuracy: 0.1913 - loss: 4.0802 - perplexity: 62.5591 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.5886 - val_perplexity: 2166540.2500\n", + "Epoch 40/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 757ms/step - categorical_accuracy: 0.2987 - loss: 4.2323 - perplexity: 90.9604 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.6538 - val_perplexity: 2312421.2500\n", + "Epoch 41/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 529ms/step - categorical_accuracy: 0.2453 - loss: 3.7488 - perplexity: 50.1163 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.6478 - val_perplexity: 2298534.5000\n", + "this is neural_network_spec_file 2025_11_23 16_55_cerebros_not-gpt_meta_42/model_architectures/tr_0000000000000000_subtrial_0000000000000000.txt\n", + "returning trial 0 oracles\n", + " categorical_accuracy loss perplexity val_categorical_accuracy \\\n", + "0 0.000000 11.769061 129192.796875 0.000000 \n", + "1 0.000000 11.635833 113077.960938 0.000000 \n", + "2 0.130435 11.652204 114944.367188 0.000000 \n", + "3 0.130435 11.464634 95285.593750 0.000000 \n", + "4 0.000000 11.768666 129141.796875 0.000000 \n", + "5 0.130435 10.994949 59572.500000 0.000000 \n", + "6 0.043478 11.276978 78982.257812 0.000000 \n", + "7 0.000000 11.120511 67542.414062 0.000000 \n", + "8 0.173913 10.726726 45557.273438 0.000000 \n", + "9 0.217391 10.059676 23380.933594 0.166667 \n", + "10 0.173913 10.355123 31417.570312 0.000000 \n", + "11 0.000000 10.472779 35340.300781 0.000000 \n", + "12 0.086957 10.171259 26140.964844 0.000000 \n", + "13 0.217391 9.254299 10449.392578 0.000000 \n", + "14 0.217391 8.896774 7308.360840 0.000000 \n", + "15 0.130435 9.018457 8254.035156 0.000000 \n", + "16 0.130435 8.039083 3099.770996 0.000000 \n", + "17 0.217391 7.848331 2561.456787 0.000000 \n", + "18 0.173913 7.948806 2832.192139 0.000000 \n", + "19 0.043478 7.698378 2204.769043 0.000000 \n", + "20 0.173913 7.669386 2141.766846 0.000000 \n", + "21 0.260870 6.773150 874.061218 0.166667 \n", + "22 0.217391 7.382279 1607.248413 0.166667 \n", + "23 0.130435 6.034015 417.387543 0.166667 \n", + "24 0.130435 6.000526 403.641022 0.166667 \n", + "25 0.043478 6.586512 725.246826 0.166667 \n", + "26 0.260870 5.741646 311.576935 0.166667 \n", + "27 0.130435 5.138083 170.388733 0.000000 \n", + "28 0.086957 5.670679 290.231415 0.000000 \n", + "29 0.217391 5.602477 271.096985 0.000000 \n", + "30 0.173913 6.986033 1081.422852 0.000000 \n", + "31 0.304348 4.127844 62.044033 0.000000 \n", + "32 0.217391 5.934126 377.709869 0.000000 \n", + "33 0.217391 5.564253 260.930054 0.000000 \n", + "34 0.173913 5.642823 282.258331 0.000000 \n", + "35 0.173913 4.475579 87.845474 0.166667 \n", + "36 0.043478 6.194771 490.179321 0.000000 \n", + "37 0.217391 5.472395 238.029572 0.000000 \n", + "38 0.173913 4.001881 54.700928 0.000000 \n", + "39 0.304348 3.707729 40.761116 0.000000 \n", + "40 0.260870 4.130568 62.213223 0.000000 \n", + "\n", + " val_loss val_perplexity trial_number subtrial_number \\\n", + "0 11.755738 1.274830e+05 0 0 \n", + "1 11.755464 1.274480e+05 0 0 \n", + "2 11.755542 1.274580e+05 0 0 \n", + "3 11.739563 1.254375e+05 0 0 \n", + "4 11.729648 1.241999e+05 0 0 \n", + "5 11.724008 1.235014e+05 0 0 \n", + "6 11.714873 1.223784e+05 0 0 \n", + "7 11.718637 1.228398e+05 0 0 \n", + "8 11.724952 1.236180e+05 0 0 \n", + "9 11.730713 1.243322e+05 0 0 \n", + "10 11.739503 1.254300e+05 0 0 \n", + "11 11.750234 1.267832e+05 0 0 \n", + "12 11.752646 1.270895e+05 0 0 \n", + "13 11.755273 1.274237e+05 0 0 \n", + "14 11.762258 1.283168e+05 0 0 \n", + "15 11.835355 1.380477e+05 0 0 \n", + "16 11.884326 1.449764e+05 0 0 \n", + "17 11.947881 1.544894e+05 0 0 \n", + "18 12.035375 1.686152e+05 0 0 \n", + "19 12.174404 1.937655e+05 0 0 \n", + "20 12.258733 2.108143e+05 0 0 \n", + "21 12.340481 2.287719e+05 0 0 \n", + "22 12.413980 2.462197e+05 0 0 \n", + "23 12.586273 2.925156e+05 0 0 \n", + "24 12.676117 3.200130e+05 0 0 \n", + "25 12.751137 3.449438e+05 0 0 \n", + "26 12.875606 3.906650e+05 0 0 \n", + "27 13.195358 5.378625e+05 0 0 \n", + "28 13.359014 6.334989e+05 0 0 \n", + "29 13.525296 7.481032e+05 0 0 \n", + "30 13.622841 8.247544e+05 0 0 \n", + "31 14.037686 1.248791e+06 0 0 \n", + "32 14.213058 1.488170e+06 0 0 \n", + "33 14.292789 1.611684e+06 0 0 \n", + "34 14.486815 1.956789e+06 0 0 \n", + "35 14.565562 2.117110e+06 0 0 \n", + "36 14.641978 2.285232e+06 0 0 \n", + "37 14.614013 2.222210e+06 0 0 \n", + "38 14.588642 2.166540e+06 0 0 \n", + "39 14.653806 2.312421e+06 0 0 \n", + "40 14.647781 2.298534e+06 0 0 \n", + "\n", + " model_name \n", + "0 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "1 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "2 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "3 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "4 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "5 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "6 2025_11_23 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16_55_cerebros_not-gpt_meta_42/mode... \n", + "23 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "24 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "25 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "26 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "27 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "28 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "29 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "30 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "31 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "32 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "33 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "34 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "35 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "36 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "37 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "38 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "39 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "40 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "Global task progress: 33%|\u001b[38;2;22;206;235mโ–ˆโ–ˆโ–ˆโ–Ž \u001b[0m| 1/3 [03:54<07:49, 234.85s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "SimpleCerebrosRandomSearch.input_shapes: [(40,)]\n", + "nan\n", + ">nnf>ceil\n", + "k is: 0 value is: [{'1': }]\n", + "0\n", + "k is: 1 value is: [{'2': }, {'2': }]\n", + "1\n", + "Trying to create level 1\n", + "We think level 1's predecessors are: [0]\n", + "k is: 2 value is: [{'128260': }]\n", + "2\n", + "Trying to create Final level 2\n", + "Trying to create level 2\n", + "We think level final level 2's predecessors are: [0, 1]\n", + "levels:\n", + "[0, 1, 2]\n", + "{'0': 'InputUnitModule'}\n", + "InputLevel.input_shapes [(40,)]\n", + "{'2': }\n", + "{'2': }\n", + "Debug: I am 2 selecting 1\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "Setting levels_unmaterialized[0] level_number 0 to have first successor: levels_unmaterialized[:1], having level_numbers of [1, 2]\n", + "Setting levels_unmaterialized[1] level_number 1 to have first successor: levels_unmaterialized[:2], having level_numbers of [2]\n", + "Debug: successor_connectivity_errors_2d []\n", + "$$$$$$>>>>> Base model: \n", + "InputUnit.input_shape: (40,)\n", + "{'2': }\n", + "{'2': }\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "Debug: successor_connectivity_errors_2d []\n", + "Debug: successor_connectivity_errors_2d []\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_1_DenseLevel_0000000000000001_tr_1_DenseUnit_0000000000000001_tr_1_0 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_1_DenseLevel_0000000000000001_tr_1_DenseUnit_0000000000000001_tr_1_1 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "{'128260': }\n", + "debug: meta_level_number\n", + "Debug: successor_connectivity_errors_2d []\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_1_FinalDenseLevel_0000000000000002_tr_1_FinalDenseUnit_0000000000000002_tr_1_0 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "inputs\n", + "\n", + "\n", + "outputs\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"NeuralNetworkFuture_0000000000000nan_tr_1_nn_materialized\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"NeuralNetworkFuture_0000000000000nan_tr_1_nn_materialized\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ - โ”‚\n", + "โ”‚ (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ functional โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m) โ”‚ \u001b[38;5;34m1,550,652\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mFunctional\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m1,584\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128260\u001b[0m) โ”‚ \u001b[38;5;34m50,919,220\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ], + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+              "โ”ƒ Layer (type)        โ”ƒ Output Shape      โ”ƒ    Param # โ”ƒ Connected to      โ”ƒ\n",
+              "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 40)        โ”‚          0 โ”‚ -                 โ”‚\n",
+              "โ”‚ (InputLayer)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ functional          โ”‚ (None, 12)        โ”‚  1,550,652 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Functional)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0]  โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0]  โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚          0 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[1][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚      1,584 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 128260)    โ”‚ 50,919,220 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+              "
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186.2288 - val_categorical_accuracy: 0.1667 - val_loss: 13.5879 - val_perplexity: 796426.8750\n", + "Epoch 29/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 973ms/step - categorical_accuracy: 0.2314 - loss: 5.0346 - perplexity: 261.9535 - val_categorical_accuracy: 0.1667 - val_loss: 13.5785 - val_perplexity: 788957.9375\n", + "Epoch 30/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 1s/step - categorical_accuracy: 0.3508 - loss: 3.9460 - perplexity: 55.9352 - val_categorical_accuracy: 0.1667 - val_loss: 13.5878 - val_perplexity: 796315.2500\n", + "Epoch 31/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 684ms/step - categorical_accuracy: 0.2141 - loss: 3.3061 - perplexity: 29.5618 - val_categorical_accuracy: 0.1667 - val_loss: 13.5959 - val_perplexity: 802850.9375\n", + "Epoch 32/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 659ms/step - categorical_accuracy: 0.1719 - loss: 4.1759 - perplexity: 72.8835 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.7057 - val_perplexity: 896031.1250\n", + "Epoch 33/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 725ms/step - categorical_accuracy: 0.1302 - loss: 5.0193 - perplexity: 177.1105 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.7885 - val_perplexity: 973393.5000\n", + "Epoch 34/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 632ms/step - categorical_accuracy: 0.2919 - loss: 2.9201 - perplexity: 24.4465 - val_categorical_accuracy: 0.0000e+00 - val_loss: 13.9237 - val_perplexity: 1114295.3750\n", + "Epoch 35/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 639ms/step - categorical_accuracy: 0.3197 - loss: 3.6359 - perplexity: 60.7448 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.0890 - val_perplexity: 1314598.2500\n", + "Epoch 36/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 527ms/step - categorical_accuracy: 0.2364 - loss: 3.6853 - perplexity: 93.4177 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.1742 - val_perplexity: 1431418.1250\n", + "Epoch 37/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 605ms/step - categorical_accuracy: 0.2731 - loss: 3.3295 - perplexity: 31.0892 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.2398 - val_perplexity: 1528469.6250\n", + "Epoch 38/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 755ms/step - categorical_accuracy: 0.5054 - loss: 4.2462 - perplexity: 96.5757 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.3218 - val_perplexity: 1659098.5000\n", + "Epoch 39/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 606ms/step - categorical_accuracy: 0.3638 - loss: 3.2328 - perplexity: 26.5526 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.3728 - val_perplexity: 1745870.1250\n", + "Epoch 40/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 745ms/step - categorical_accuracy: 0.5727 - loss: 1.9158 - perplexity: 9.5471 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.5209 - val_perplexity: 2024707.8750\n", + "Epoch 41/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 671ms/step - categorical_accuracy: 0.1068 - loss: 5.0107 - perplexity: 172.7614 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.5204 - val_perplexity: 2023570.8750\n", + "this is neural_network_spec_file 2025_11_23 16_55_cerebros_not-gpt_meta_42/model_architectures/tr_0000000000000001_subtrial_0000000000000000.txt\n", + "returning trial 1 oracles\n", + " categorical_accuracy loss perplexity val_categorical_accuracy \\\n", + "0 0.000000 11.719700 225372.531250 0.166667 \n", + "1 0.173913 11.155995 69982.140625 0.166667 \n", + "2 0.173913 10.995764 59621.039062 0.166667 \n", + "3 0.217391 10.042144 22974.583984 0.000000 \n", + "4 0.173913 9.805058 18125.181641 0.000000 \n", + "5 0.130435 9.198784 9885.100586 0.000000 \n", + "6 0.173913 8.641828 5663.671387 0.000000 \n", + "7 0.043478 8.808529 6691.075195 0.000000 \n", + "8 0.217391 7.256882 1417.828491 0.000000 \n", + "9 0.173913 6.904544 996.794250 0.000000 \n", + "10 0.217391 6.873430 966.256958 0.000000 \n", + "11 0.173913 5.982946 396.607025 0.000000 \n", + "12 0.043478 6.824471 920.089539 0.166667 \n", + "13 0.130435 6.259269 522.836731 0.166667 \n", + "14 0.217391 5.205779 182.322769 0.166667 \n", + "15 0.130435 5.462027 235.574463 0.000000 \n", + "16 0.217391 6.074162 434.485474 0.000000 \n", + "17 0.217391 5.354462 211.550262 0.000000 \n", + "18 0.304348 4.318021 75.040001 0.000000 \n", + "19 0.217391 5.875260 356.117035 0.000000 \n", + "20 0.217391 5.246053 189.815536 0.000000 \n", + "21 0.391304 4.035575 56.575462 0.000000 \n", + "22 0.173913 3.672752 39.360092 0.000000 \n", + "23 0.347826 4.800797 121.607239 0.000000 \n", + "24 0.260870 6.058529 427.745911 0.166667 \n", + "25 0.260870 6.874752 967.535400 0.166667 \n", + "26 0.304348 3.871903 48.033691 0.166667 \n", + "27 0.217391 5.597022 269.622284 0.166667 \n", + "28 0.260870 4.006342 54.945507 0.166667 \n", + "29 0.260870 4.286894 72.740173 0.166667 \n", + "30 0.217391 3.180355 24.055300 0.166667 \n", + "31 0.173913 4.073040 58.735218 0.000000 \n", + "32 0.173913 5.302594 200.857193 0.000000 \n", + "33 0.217391 3.763384 43.094006 0.000000 \n", + "34 0.217391 4.363249 78.511826 0.000000 \n", + "35 0.217391 5.450110 232.783875 0.000000 \n", + "36 0.260870 3.634080 37.866989 0.000000 \n", + "37 0.391304 5.082735 161.214310 0.000000 \n", + "38 0.391304 3.312840 27.463017 0.000000 \n", + "39 0.434783 2.846823 17.232950 0.000000 \n", + "40 0.086957 5.169964 175.908478 0.000000 \n", + "\n", + " val_loss val_perplexity trial_number subtrial_number \\\n", + "0 11.768844 1.291648e+05 1 0 \n", + "1 11.750198 1.267787e+05 1 0 \n", + "2 11.742490 1.258052e+05 1 0 \n", + "3 11.745057 1.261286e+05 1 0 \n", + "4 11.736234 1.250206e+05 1 0 \n", + "5 11.717111 1.226525e+05 1 0 \n", + "6 11.690784 1.194656e+05 1 0 \n", + "7 11.644112 1.140180e+05 1 0 \n", + "8 11.626712 1.120513e+05 1 0 \n", + "9 11.637473 1.132634e+05 1 0 \n", + "10 11.659701 1.158094e+05 1 0 \n", + "11 11.750339 1.267965e+05 1 0 \n", + "12 11.802508 1.335870e+05 1 0 \n", + "13 11.897525 1.469026e+05 1 0 \n", + "14 11.997716 1.623835e+05 1 0 \n", + "15 12.270196 2.132448e+05 1 0 \n", + "16 12.433421 2.510535e+05 1 0 \n", + "17 12.588585 2.931926e+05 1 0 \n", + "18 12.766930 3.504347e+05 1 0 \n", + "19 13.121078 4.993575e+05 1 0 \n", + "20 13.272231 5.808403e+05 1 0 \n", + "21 13.392925 6.553504e+05 1 0 \n", + "22 13.513103 7.390372e+05 1 0 \n", + "23 13.607346 8.120731e+05 1 0 \n", + "24 13.620921 8.231726e+05 1 0 \n", + "25 13.592243 7.999009e+05 1 0 \n", + "26 13.596767 8.035281e+05 1 0 \n", + "27 13.587891 7.964269e+05 1 0 \n", + "28 13.578468 7.889579e+05 1 0 \n", + "29 13.587750 7.963152e+05 1 0 \n", + "30 13.595924 8.028509e+05 1 0 \n", + "31 13.705730 8.960311e+05 1 0 \n", + "32 13.788544 9.733935e+05 1 0 \n", + "33 13.923733 1.114295e+06 1 0 \n", + "34 14.089040 1.314598e+06 1 0 \n", + "35 14.174176 1.431418e+06 1 0 \n", + "36 14.239779 1.528470e+06 1 0 \n", + "37 14.321785 1.659098e+06 1 0 \n", + "38 14.372764 1.745870e+06 1 0 \n", + "39 14.520935 2.024708e+06 1 0 \n", + "40 14.520374 2.023571e+06 1 0 \n", + "\n", + " model_name \n", + "0 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "1 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "2 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "3 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "4 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "5 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "6 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "7 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "8 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "9 2025_11_23 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16_55_cerebros_not-gpt_meta_42/mode... \n", + "26 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "27 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "28 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "29 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "30 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "31 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "32 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "33 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "34 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "35 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "36 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "37 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "38 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "39 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "40 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "Global task progress: 67%|\u001b[38;2;22;206;235mโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹ \u001b[0m| 2/3 [07:42<03:50, 230.58s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "SimpleCerebrosRandomSearch.input_shapes: [(40,)]\n", + "nan\n", + ">nnf>ceil\n", + "k is: 0 value is: [{'1': }]\n", + "0\n", + "k is: 1 value is: [{'2': }, {'2': }]\n", + "1\n", + "Trying to create level 1\n", + "We think level 1's predecessors are: [0]\n", + "k is: 2 value is: [{'128260': }]\n", + "2\n", + "Trying to create Final level 2\n", + "Trying to create level 2\n", + "We think level final level 2's predecessors are: [0, 1]\n", + "levels:\n", + "[0, 1, 2]\n", + "{'0': 'InputUnitModule'}\n", + "InputLevel.input_shapes [(40,)]\n", + "{'2': }\n", + "{'2': }\n", + "Debug: I am 2 selecting 1\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "Setting levels_unmaterialized[0] level_number 0 to have first successor: levels_unmaterialized[:1], having level_numbers of [1, 2]\n", + "Setting levels_unmaterialized[1] level_number 1 to have first successor: levels_unmaterialized[:2], having level_numbers of [2]\n", + "Debug: successor_connectivity_errors_2d []\n", + "$$$$$$>>>>> Base model: \n", + "InputUnit.input_shape: (40,)\n", + "{'2': }\n", + "{'2': }\n", + "debug: meta_level_number\n", + "debug: meta_level_number\n", + "Debug: successor_connectivity_errors_2d []\n", + "Debug: successor_connectivity_errors_2d []\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_2_DenseLevel_0000000000000001_tr_2_DenseUnit_0000000000000001_tr_2_0 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_2_DenseLevel_0000000000000001_tr_2_DenseUnit_0000000000000001_tr_2_1 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "{'128260': }\n", + "debug: meta_level_number\n", + "Debug: successor_connectivity_errors_2d []\n", + "materialize:_NeuralNetworkFuture_0000000000000nan_tr_2_FinalDenseLevel_0000000000000002_tr_2_FinalDenseUnit_0000000000000002_tr_2_0 called\n", + "materialized network layers\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "materialized_predecessor_units [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n", + "inputs\n", + "\n", + "\n", + "outputs\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"NeuralNetworkFuture_0000000000000nan_tr_2_nn_materialized\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"NeuralNetworkFuture_0000000000000nan_tr_2_nn_materialized\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ - โ”‚\n", + "โ”‚ (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ functional โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m) โ”‚ \u001b[38;5;34m1,550,652\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mFunctional\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m] โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m432\u001b[0m) โ”‚ \u001b[38;5;34m1,728\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m866\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mConcatenate\u001b[0m) โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ functional[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ โ”‚ โ”‚ โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m396\u001b[0m) โ”‚ \u001b[38;5;34m1,584\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalizatioโ€ฆ\u001b[0m โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128260\u001b[0m) โ”‚ \u001b[38;5;34m50,919,220\u001b[0m โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n", + "โ”‚ (\u001b[38;5;33mDense\u001b[0m) โ”‚ โ”‚ โ”‚ โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ], + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+              "โ”ƒ Layer (type)        โ”ƒ Output Shape      โ”ƒ    Param # โ”ƒ Connected to      โ”ƒ\n",
+              "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 40)        โ”‚          0 โ”‚ -                 โ”‚\n",
+              "โ”‚ (InputLayer)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ functional          โ”‚ (None, 12)        โ”‚  1,550,652 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Functional)        โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0]  โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚          0 โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0]  โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 432)       โ”‚      1,728 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 2)         โ”‚        866 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚          0 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Concatenate)       โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ functional[2][0], โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚                     โ”‚                   โ”‚            โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 396)       โ”‚      1,584 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (BatchNormalizatioโ€ฆ โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ NeuralNetworkFuturโ€ฆ โ”‚ (None, 128260)    โ”‚ 50,919,220 โ”‚ NeuralNetworkFutโ€ฆ โ”‚\n",
+              "โ”‚ (Dense)             โ”‚                   โ”‚            โ”‚                   โ”‚\n",
+              "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+              "
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0.1667 - val_loss: 14.6578 - val_perplexity: 2321627.0000\n", + "Epoch 29/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 794ms/step - categorical_accuracy: 0.3320 - loss: 3.2803 - perplexity: 27.8907 - val_categorical_accuracy: 0.1667 - val_loss: 14.7196 - val_perplexity: 2469625.0000\n", + "Epoch 30/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 660ms/step - categorical_accuracy: 0.2752 - loss: 5.4753 - perplexity: 249.7908 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.8572 - val_perplexity: 2834024.2500\n", + "Epoch 31/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 748ms/step - categorical_accuracy: 0.2925 - loss: 5.2035 - perplexity: 302.8727 - val_categorical_accuracy: 0.0000e+00 - val_loss: 14.9761 - val_perplexity: 3191841.5000\n", + "Epoch 32/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 585ms/step - categorical_accuracy: 0.2715 - loss: 3.0830 - perplexity: 22.1130 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.0934 - val_perplexity: 3589043.2500\n", + "Epoch 33/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 837ms/step - categorical_accuracy: 0.3638 - loss: 2.0138 - perplexity: 7.6831 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.1927 - val_perplexity: 3963894.5000\n", + "Epoch 34/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 622ms/step - categorical_accuracy: 0.4165 - loss: 2.3430 - perplexity: 12.4422 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.2933 - val_perplexity: 4383348.5000\n", + "Epoch 35/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 730ms/step - categorical_accuracy: 0.4832 - loss: 3.8156 - perplexity: 57.3130 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4055 - val_perplexity: 4903895.5000\n", + "Epoch 36/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 831ms/step - categorical_accuracy: 0.1641 - loss: 4.5182 - perplexity: 317.0210 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4245 - val_perplexity: 4998003.5000\n", + "Epoch 37/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 621ms/step - categorical_accuracy: 0.2752 - loss: 2.9753 - perplexity: 25.5228 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4590 - val_perplexity: 5173094.0000\n", + "Epoch 38/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 666ms/step - categorical_accuracy: 0.2280 - loss: 2.4058 - perplexity: 11.6680 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4232 - val_perplexity: 4991435.0000\n", + "Epoch 39/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 565ms/step - categorical_accuracy: 0.3592 - loss: 3.6356 - perplexity: 40.6227 - val_categorical_accuracy: 0.0000e+00 - val_loss: 15.4089 - val_perplexity: 4920268.0000\n", + "Epoch 40/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 665ms/step - categorical_accuracy: 0.2691 - loss: 3.4659 - perplexity: 47.4784 - val_categorical_accuracy: 0.1667 - val_loss: 15.3797 - val_perplexity: 4778703.0000\n", + "Epoch 41/41\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 693ms/step - categorical_accuracy: 0.4310 - loss: 2.7924 - perplexity: 20.8595 - val_categorical_accuracy: 0.1667 - val_loss: 15.4324 - val_perplexity: 5037467.5000\n", + "this is neural_network_spec_file 2025_11_23 16_55_cerebros_not-gpt_meta_42/model_architectures/tr_0000000000000002_subtrial_0000000000000000.txt\n", + "returning trial 2 oracles\n", + " categorical_accuracy loss perplexity val_categorical_accuracy \\\n", + "0 0.000000 11.761698 226943.984375 0.000000 \n", + "1 0.260870 11.090140 65521.941406 0.166667 \n", + "2 0.086957 10.702163 44451.890625 0.166667 \n", + "3 0.173913 9.990288 21813.576172 0.166667 \n", + "4 0.347826 9.246581 10369.053711 0.166667 \n", + "5 0.260870 8.266317 3890.594971 0.166667 \n", + "6 0.347826 7.704062 2217.337646 0.166667 \n", + "7 0.304348 7.122604 1239.675415 0.166667 \n", + "8 0.347826 6.225540 505.495789 0.166667 \n", + "9 0.086957 6.562615 708.120972 0.166667 \n", + "10 0.260870 5.511858 247.610764 0.166667 \n", + "11 0.217391 5.295715 199.480270 0.166667 \n", + "12 0.130435 6.463620 641.378784 0.000000 \n", + "13 0.260870 5.026217 152.355484 0.000000 \n", + "14 0.217391 5.910099 368.742676 0.000000 \n", + "15 0.217391 4.887461 132.616455 0.000000 \n", + "16 0.434783 3.347833 28.441027 0.000000 \n", + "17 0.347826 4.313054 74.668182 0.000000 \n", + "18 0.304348 4.665129 106.179253 0.000000 \n", + "19 0.217391 4.334057 76.253006 0.000000 \n", + "20 0.391304 2.807739 16.572411 0.000000 \n", + "21 0.391304 4.215992 67.761353 0.000000 \n", + "22 0.391304 3.522572 33.871445 0.000000 \n", + "23 0.478261 2.265072 9.631822 0.000000 \n", + "24 0.347826 5.091538 162.639801 0.000000 \n", + "25 0.347826 2.982907 19.745134 0.000000 \n", + "26 0.130435 3.861120 47.518566 0.000000 \n", + "27 0.434783 4.315707 74.866554 0.166667 \n", + "28 0.304348 3.004366 20.173416 0.166667 \n", + "29 0.217391 5.262289 192.922501 0.000000 \n", + "30 0.260870 5.697386 298.087250 0.000000 \n", + "31 0.347826 3.149921 23.334219 0.000000 \n", + "32 0.391304 2.063896 7.876601 0.000000 \n", + "33 0.391304 3.141111 23.129541 0.000000 \n", + "34 0.391304 3.663168 38.984657 0.000000 \n", + "35 0.217391 3.455597 31.677193 0.000000 \n", + "36 0.217391 3.796592 44.549091 0.000000 \n", + "37 0.217391 2.545129 12.744876 0.000000 \n", + "38 0.260870 4.018140 55.597588 0.000000 \n", + "39 0.173913 3.072442 21.594568 0.166667 \n", + "40 0.434783 2.709372 15.019834 0.166667 \n", + "\n", + " val_loss val_perplexity trial_number subtrial_number \\\n", + "0 11.722857 1.233594e+05 2 0 \n", + "1 11.644334 1.140433e+05 2 0 \n", + "2 11.609864 1.101793e+05 2 0 \n", + "3 11.582150 1.071679e+05 2 0 \n", + "4 11.588885 1.078919e+05 2 0 \n", + "5 11.611365 1.103448e+05 2 0 \n", + "6 11.635376 1.130263e+05 2 0 \n", + "7 11.736249 1.250225e+05 2 0 \n", + "8 11.836572 1.382158e+05 2 0 \n", + "9 11.940872 1.534104e+05 2 0 \n", + "10 12.069603 1.744865e+05 2 0 \n", + "11 12.378307 2.375913e+05 2 0 \n", + "12 12.568721 2.874260e+05 2 0 \n", + "13 12.738580 3.406394e+05 2 0 \n", + "14 12.861950 3.853664e+05 2 0 \n", + "15 13.139782 5.087856e+05 2 0 \n", + "16 13.314763 6.060774e+05 2 0 \n", + "17 13.506835 7.344190e+05 2 0 \n", + "18 13.664001 8.594092e+05 2 0 \n", + "19 13.938638 1.131028e+06 2 0 \n", + "20 14.083958 1.307933e+06 2 0 \n", + "21 14.162007 1.414105e+06 2 0 \n", + "22 14.258838 1.557883e+06 2 0 \n", + "23 14.377925 1.754904e+06 2 0 \n", + "24 14.447185 1.880756e+06 2 0 \n", + "25 14.536368 2.056196e+06 2 0 \n", + "26 14.590198 2.169913e+06 2 0 \n", + "27 14.657779 2.321627e+06 2 0 \n", + "28 14.719577 2.469625e+06 2 0 \n", + "29 14.857208 2.834024e+06 2 0 \n", + "30 14.976109 3.191842e+06 2 0 \n", + "31 15.093396 3.589043e+06 2 0 \n", + "32 15.192738 3.963894e+06 2 0 \n", + "33 15.293323 4.383348e+06 2 0 \n", + "34 15.405540 4.903896e+06 2 0 \n", + "35 15.424549 4.998004e+06 2 0 \n", + "36 15.458982 5.173094e+06 2 0 \n", + "37 15.423234 4.991435e+06 2 0 \n", + "38 15.408874 4.920268e+06 2 0 \n", + "39 15.379680 4.778703e+06 2 0 \n", + "40 15.432412 5.037468e+06 2 0 \n", + "\n", + " model_name \n", + "0 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "1 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "2 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "3 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "4 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "5 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "6 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "7 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "8 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "9 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "10 2025_11_23 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16_55_cerebros_not-gpt_meta_42/mode... \n", + "27 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "28 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "29 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "30 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "31 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "32 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "33 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "34 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "35 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "36 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "37 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "38 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "39 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n", + "40 2025_11_23 16_55_cerebros_not-gpt_meta_42/mode... \n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/usr/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "Global task progress: 100%|\u001b[38;2;22;206;235mโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ\u001b[0m| 3/3 [12:11<00:00, 243.86s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Index(['categorical_accuracy', 'loss', 'perplexity',\n", + " 'val_categorical_accuracy', 'val_loss', 'val_perplexity',\n", + " 'trial_number', 'subtrial_number', 'model_name'],\n", + " dtype='object')\n", + "metric_to_rank_by is: 'perplexity'\n", + "Type of metric_to_rank_by is: \n", + "metric_to_rank_by is: 'perplexity'\n", + "Type of metric_to_rank_by is: \n", + "Best result this trial was: 7.876600742340088\n", + "Type of best result: \n", + "Best model name: 2025_11_23 16_55_cerebros_not-gpt_meta_42/models/tr_0000000000000002_subtrial_0000000000000000.keras\n", + "Cerebros trained 3 models in 12.19 min. Average time per model: 4.06 min.\n", + "Cerebros best perplexity achieved in Phase I-a is 7.876600742340088\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Training Stage I-a - Model Evaluation (Subjective):\n", + "\n", + "- We retrieve the best model found during the NAS phase and test its text generation capabilities from a subjective standpoint.\n", + "- Keep in mind, this is trained on 10 text samples. It is impressive that it can generate anything, especially subjects and verbs that are on-topic and agree, and is otherwise sensible, despite being grammatically gibberish.\n", + "\n", + "FYI: The generative components we imported from cerebrosllmutils:\n", + "\n", + "## Model config\n", + "```python\n", + "\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='CerebrosNotGPTConfig')\n", + "class CerebrosNotGPTConfig:\n", + " def __init__(self, max_sequence_length=1536, padding_token=None):\n", + " self.max_sequence_length = max_sequence_length\n", + " self.padding_token = padding_token\n", + "\n", + " def get_config(self):\n", + " return {\n", + " 'max_sequence_length': self.max_sequence_length,\n", + " 'padding_token': self.padding_token\n", + " }\n", + "\n", + " @classmethod\n", + " def from_config(cls, config):\n", + " return cls(**config)\n", + "```\n", + "\n", + "## Model class we imported from cerebrosllmutil, having:\n", + "\n", + "- Greedy sampling\n", + "- Temperature scaling\n", + "- Top p sampling\n", + "- Top k sampling\n", + "- Presence penlaty\n", + "- Frequency penalty\n", + "- Repetition penalty\n", + "\n", + "```python\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='CerebrosNotGPT')\n", + "class CerebrosNotGPT(tf.keras.Model):\n", + " def __init__(self, config: Any, model: Any = None, **kwargs):\n", + " # 1. Store the nested model argument.\n", + " self.config = config\n", + " self.model = model\n", + " \n", + " # 2. Extract and remove custom kwargs (like 'model') before calling super.\n", + " # This is important to prevent 'unrecognized keyword argument' errors.\n", + " # The nested model is already extracted and stored, so it can be safely removed.\n", + " kwargs.pop('model', None)\n", + " \n", + " # 3. Call the parent constructor with the cleaned kwargs.\n", + " super().__init__(**kwargs)\n", + "\n", + " self.max_sequence_length = config.max_sequence_length\n", + " self.padding_token = config.padding_token\n", + "\n", + " def get_config(self):\n", + " base_config = super().get_config()\n", + " config_dict = {\n", + " 'config': self.config.get_config(),\n", + " }\n", + " \n", + " # Explicitly handle nested model serialization.\n", + " # This is required if Keras's automatic tracking fails.\n", + " if self.model is not None:\n", + " # Note: This approach might still suffer from weight loss.\n", + " # The recommended way is to let Keras handle it automatically.\n", + " config_dict['model'] = tf.keras.utils.serialize_keras_object(self.model)\n", + "\n", + " base_config.update(config_dict)\n", + " return base_config\n", + "\n", + " @classmethod\n", + " def from_config(cls, config):\n", + " # Separate the custom config.\n", + " config_obj_dict = config.pop('config')\n", + " config_obj = CerebrosNotGPTConfig.from_config(config_obj_dict)\n", + " \n", + " # Manually extract and load the nested model.\n", + " nested_model_config = config.pop('model', None)\n", + " if nested_model_config:\n", + " nested_model = tf.keras.utils.deserialize_keras_object(nested_model_config)\n", + " else:\n", + " nested_model = None\n", + " \n", + " # Reconstruct the outer model by passing the restored parts.\n", + " return cls(config=config_obj, model=nested_model, **config)\n", + "\n", + " def call(self, inputs, training=False):\n", + " if self.model is None:\n", + " raise ValueError(\"Inner model not initialized properly\")\n", + " return self.model(inputs, training=training)\n", + "\n", + " @staticmethod\n", + " def apply_top_k_probs(probs, k):\n", + " if k is None or k <= 0:\n", + " return probs\n", + " # Flatten and argsort for indices\n", + " sorted_indices = tf.argsort(probs, direction='DESCENDING')\n", + " keep_indices = sorted_indices[:k]\n", + " mask = tf.zeros_like(probs, dtype=tf.bool)\n", + " mask = tf.tensor_scatter_nd_update(mask, tf.reshape(keep_indices, (-1, 1)),\n", + " tf.ones((k,), dtype=tf.bool))\n", + " filtered_probs = tf.where(mask, probs, tf.zeros_like(probs))\n", + " # Renormalize\n", + " filtered_probs = filtered_probs / tf.reduce_sum(filtered_probs)\n", + " return filtered_probs\n", + "\n", + " @staticmethod\n", + " def apply_top_p_probs(probs, p):\n", + " if p is None or p >= 1.0:\n", + " return probs\n", + " sorted_indices = tf.argsort(probs, direction='DESCENDING')\n", + " sorted_probs = tf.gather(probs, sorted_indices)\n", + " cumulative_probs = tf.cumsum(sorted_probs)\n", + " mask = cumulative_probs <= p\n", + " # Always keep at least 1 token\n", + " mask = tf.concat([tf.constant([True]), mask[1:]], axis=0)\n", + " keep_indices = tf.boolean_mask(sorted_indices, mask)\n", + " filtered_probs = tf.where(\n", + " tf.reduce_any(tf.equal(tf.range(tf.shape(probs)[0])[:, None], keep_indices), axis=1), probs,\n", + " tf.zeros_like(probs))\n", + " # Renormalize\n", + " filtered_probs = filtered_probs / tf.reduce_sum(filtered_probs)\n", + " return filtered_probs\n", + "\n", + " def generate(self,\n", + " token_ids,\n", + " do_sample=False,\n", + " max_new_tokens=None,\n", + " temperature=1.0,\n", + " top_k=None,\n", + " top_p=None,\n", + " frequency_penalty=None,\n", + " presence_penalty=None,\n", + " repetition_penalty=None):\n", + " \"\"\"\n", + " Generate text autoregressively from token IDs.\n", + " Applies filtering in sequence: penalties -> temperature -> top-k -> top-p\n", + " \"\"\"\n", + " # Convert token_ids to list if it's not already\n", + " if not isinstance(token_ids, list):\n", + " token_ids = list(token_ids)\n", + "\n", + " # Determine the actual maximum number of new tokens\n", + " if max_new_tokens is None:\n", + " max_new_tokens = self.max_sequence_length - len(token_ids)\n", + " else:\n", + " max_new_tokens = min(max_new_tokens, self.max_sequence_length - len(token_ids))\n", + "\n", + " # Initialize the generated tokens list\n", + " generated_tokens = []\n", + " current_tokens = token_ids.copy()\n", + "\n", + " # Autoregressive generation loop\n", + " for _ in range(max_new_tokens):\n", + " # Pad or truncate to max_sequence_length\n", + " if len(current_tokens) > self.max_sequence_length:\n", + " input_tokens = current_tokens[-self.max_sequence_length:]\n", + " else:\n", + " padding_needed = self.max_sequence_length - len(current_tokens)\n", + " input_tokens = current_tokens + [self.padding_token] * padding_needed\n", + "\n", + " # Convert to tensor and get model prediction\n", + " input_tensor = tf.constant([input_tokens], dtype=tf.int32)\n", + " probs_nested = self.model(input_tensor)\n", + " probs = probs_nested[0] # Already softmax probabilities (NOT logits as comment says)\n", + " logits = tf.math.log(probs + 10 ** -20) # Convert to logits for penalty application\n", + "\n", + " if do_sample:\n", + " # Apply repetition/frequency/presence penalties to logits\n", + " if frequency_penalty is not None or presence_penalty is not None:\n", + " # Collect token counts from current_tokens\n", + " token_counts = {}\n", + " for t in current_tokens:\n", + " token_counts[t] = token_counts.get(t, 0) + 1\n", + "\n", + " # Prepare penalty tensor\n", + " vocab_size = tf.shape(logits)[0]\n", + " penalties = tf.zeros_like(logits)\n", + "\n", + " for token_id, count in token_counts.items():\n", + " if token_id >= vocab_size:\n", + " continue\n", + " penalty = 0.0\n", + " if presence_penalty is not None:\n", + " penalty += presence_penalty\n", + " if frequency_penalty is not None:\n", + " penalty += frequency_penalty * count\n", + "\n", + " penalties = tf.tensor_scatter_nd_add(\n", + " penalties,\n", + " [[token_id]],\n", + " [penalty]\n", + " )\n", + "\n", + " # Subtract penalties from logits\n", + " logits = logits - penalties\n", + "\n", + " # Apply repetition penalty (standard approach)\n", + " if repetition_penalty is not None and repetition_penalty != 1.0:\n", + " # Collect unique tokens that have appeared\n", + " unique_tokens = list(set(current_tokens))\n", + " vocab_size = tf.shape(logits)[0]\n", + "\n", + " for token_id in unique_tokens:\n", + " if token_id < vocab_size:\n", + " # Divide logits of repeated tokens by penalty\n", + " logits = tf.tensor_scatter_nd_update(\n", + " logits,\n", + " [[token_id]],\n", + " [logits[token_id] / repetition_penalty]\n", + " )\n", + "\n", + " # Apply temperature\n", + " if temperature != 1.0:\n", + " logits = logits / temperature\n", + "\n", + " # Convert to probabilities\n", + " probs = tf.nn.softmax(logits)\n", + "\n", + " # Apply top-k filtering (if specified)\n", + " if top_k is not None and top_k > 0:\n", + " k = min(top_k, tf.shape(probs)[0])\n", + " # Get top-k values and indices\n", + " top_k_values, top_k_indices = tf.nn.top_k(probs, k=k, sorted=False)\n", + " # Create mask for top-k positions\n", + " top_k_mask = tf.scatter_nd(\n", + " tf.expand_dims(top_k_indices, 1),\n", + " tf.ones_like(top_k_values, dtype=tf.bool),\n", + " tf.shape(probs)\n", + " )\n", + " # Zero out non-top-k probabilities\n", + " probs = tf.where(top_k_mask, probs, tf.zeros_like(probs))\n", + " # Renormalize\n", + " probs = probs / tf.reduce_sum(probs)\n", + " print(\n", + " f\">>> After top_k: {tf.shape(probs)} shape, {tf.reduce_sum(tf.cast(probs > 1e-8, tf.int32))} non-zero probs\")\n", + "\n", + " # Apply top-p filtering (if specified)\n", + " if top_p is not None and top_p < 1.0:\n", + " # Sort probabilities in descending order\n", + " sorted_indices = tf.argsort(probs, direction='DESCENDING')\n", + " sorted_probs = tf.gather(probs, sorted_indices)\n", + " cumulative_probs = tf.cumsum(sorted_probs)\n", + " # Create mask for top-p\n", + " mask = cumulative_probs <= top_p\n", + " # Always keep at least one token\n", + " mask = tf.concat([tf.constant([True]), mask[1:]], axis=0)\n", + " # Get indices to keep\n", + " keep_indices = tf.boolean_mask(sorted_indices, mask)\n", + " # Create mask for original indices\n", + " filter_mask = tf.scatter_nd(\n", + " tf.expand_dims(keep_indices, 1),\n", + " tf.ones_like(keep_indices, dtype=tf.bool),\n", + " tf.shape(probs)\n", + " )\n", + " # Apply mask and renormalize\n", + " probs = tf.where(filter_mask, probs, tf.zeros_like(probs))\n", + " probs = probs / tf.reduce_sum(probs)\n", + " print(\n", + " f\">>> After top_p: {tf.shape(probs)} shape, {tf.reduce_sum(tf.cast(probs > 1e-8, tf.int32))} non-zero probs\")\n", + "\n", + " # Sample from the final filtered distribution\n", + " # Get non-zero indices and their probabilities\n", + " non_zero_mask = probs > 1e-8\n", + " if tf.reduce_any(non_zero_mask):\n", + " filtered_indices = tf.where(non_zero_mask)[:, 0] # Get indices\n", + " filtered_probs = tf.boolean_mask(probs, non_zero_mask) # Get probabilities\n", + " # Sample\n", + " sampled_local_index = tf.random.categorical(tf.math.log(filtered_probs)[None, :], 1)[0, 0]\n", + " # Map back to vocabulary index\n", + " next_token_id = int(filtered_indices[sampled_local_index].numpy())\n", + " else:\n", + " # Fallback if all probabilities are zero\n", + " warn(\n", + " \"Token sampling had to revert to greedy sampling, because no probs had a value > 0, unexpected\")\n", + " next_token_id = int(tf.argmax(probs, axis=-1).numpy())\n", + "\n", + " else:\n", + " # Greedy sampling (argmax) - apply repetition penalty if needed\n", + " if repetition_penalty is not None and repetition_penalty != 1.0:\n", + " unique_tokens = list(set(current_tokens))\n", + " vocab_size = tf.shape(logits)[0]\n", + " for token_id in unique_tokens:\n", + " if token_id < vocab_size:\n", + " logits = tf.tensor_scatter_nd_update(\n", + " logits,\n", + " [[token_id]],\n", + " [logits[token_id] / repetition_penalty]\n", + " )\n", + "\n", + " next_token_id = int(tf.argmax(logits, axis=-1).numpy())\n", + "\n", + " # Check for termination condition\n", + " if next_token_id == self.padding_token:\n", + " break\n", + "\n", + " # Add to generated tokens and update current tokens\n", + " generated_tokens.append(int(next_token_id))\n", + " current_tokens.append(int(next_token_id))\n", + "\n", + " # Check if we've reached max sequence length\n", + " if len(current_tokens) >= self.max_sequence_length:\n", + " break\n", + "\n", + " return token_ids + generated_tokens\n", + "\n", + "```" + ], + "metadata": { + "id": "96KSf1hKoe0H" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "## How this LLM wrapper works under the hood: A Simple Overview\n", + "\n", + "- Think of a Large Language Model like the \"autocomplete\" on your cell phone's keyboard that suggests the next word.\n", + "- Now, imagine you continuously click the suggested next word.\n", + "- The model picks the mathematically most likely next word, and you just go with it, and pick the next, then the next ...\n", + "\n", + "### Here is the step-by-step flow of how it generates text.\n", + "\n", + "1. INPUT: The Prompt\n", + "\n", + "The process always starts with a piece of text from you, the user.\n", + "\n", + "\"Write a story\"\n", + "\n", + "2. STEP 1: Tokenization โ€” From Words to Numbers\n", + "\n", + "A computer doesn't understand letters or words; it understands numbers. The first step is to convert the prompt into a sequence of numbers the model can process. The tokenizer is a specialized dictionary for this job.\n", + "\n", + " What comes in: A string of text (\"Write a story\").\n", + " What goes out: A list of numerical IDs ([92, 21, 54, 21, 63, ...]).\n", + "\n", + "To make processing consistent, the input is always padded to a fixed length (e.g., 40 tokens). Any empty slots are filled with a special ID that is assigned by the tokenizer.\n", + "\n", + "\"Write a story\" -> tokenizer -> [92, 21, 54, 21, 63, 1234, 1234, ... (length 40)]\n", + "\n", + "For example it may look like:\n", + "```\n", + "92 = \"Write\"\n", + "21 = \" \"\n", + "54 = \"a\"\n", + "63 = \"story\"\n", + "1234 = \"\" (Repeated until there are 40 numbers)\n", + "```\n", + "\n", + "3. The Model's Core: Going From Token IDs to the Predicted Next Token:\n", + "\n", + "This is the \"black box\" part. Inside the model, 4 basic things happen:\n", + "\n", + " 1. Embedding (Converts the discrete, high-dimensional sequence of tokens into a continuous distribution of a smaller dimensionality).\n", + " 2. Positional embedding: Positional embedding: Takes the output of the embedding layer and represents their relative sequential order as a continuous distribution with a clear mathematical relationship.\n", + " 3. Prediction: Prediction: A lattice of Dense layers, arranged as columns and rows, each having randomized lateral connectivity with other Dense layers on the same row, and randomized vertical connectivity with Dense layers on other rows. This takes the positional embedding's output and returns a numerical answer from its head layer. This element, produced by the Cerebros NAS, serves as a more computationally efficient alternative to the attention block used in other LLMs. The output is of shape (BATCH_SIZE, VOCABULARY_SIZE) as logits.\n", + " 4. Output activation (Scales the output to a valid range). In this case, the raw output is a tensor of shape (BATCH_SIZE, VOCABULARY_SIZE). The numbers need to be cast as probabilities, so the valid range is:\n", + " - Each element in the list must be in the range between 0 and 1 (inclusive).\n", + " - The entire list of numbers must add up to 1.\n", + " - Softmax is used to accomplish this.\n", + "\n", + "As mentioned before, this is a \"Single Head\" model, unlike most LLMs (like GPT-3/4). Each call returns **only** the next token expected in the sequence, expressed as a list of probabilities (probs) of shape (BATCH_SIZE, VOCABULARY_SIZE).\n", + "\n", + "\n", + "4. Predicting the Next Word From the Output of The Final Layer:\n", + "\n", + "After the model returns a list of probabilities, we must **pick the next word** from this. There are VOCABULARY_SIZE words in the vocabulary, each assigned an index position on this list.\n", + "\n", + "5. Sampling\n", + "\n", + "- **Greedy Sampling** The naive strategy is to just pick the highest probability in this distribution (we call this greedy sampling) and assume it is the correct next token. You then decode that token ID and use it as the next word. Then de-code that toekn id and use that as the next word. Naively assuming the highest probability is correct makes for a few problems, including:\n", + " - The output will be identical every time you write the same prompt.\n", + " - Common words like \"the\", \"and\", ... will be used too often and used out of place.\n", + " - The text will seem \"dry\" and lack creative appeal.\n", + "- **Beam Sampling**: The better approach, is scaling then sampling from a few of the top choices. We apply scaling to the logits and recalculate the probabilities. Then, we eliminate unlikely possibilities. This leaves a smaller set of plausible tokens, from which we randomly select the next word. The methods we use are:\n", + " - **Presence penalty:** Steeply penalizes the logit for a token that has already been used recently or as the last word in the sequence, making it very unlikely to survive sampling and be selected. **Its purpose:** Mainly prevents the same word from being used twice **in immediate succession** \"This is **the the the** problem which **this this** scaling technique should fix.\"\n", + " - **Frequency penalty:** Mildly penalizes the logit for a token that has been **overused** in the text, but **not necessarily** the last or recent word, making it less likely to be chosen repeatedly but still possible. **For an example:** \"This technique **like** fixes **like** this from **like** happening. It's **like** really really annoying.\"\n", + " - **Repetition penalty scaling**: A penalty that balances the effects of both presence and frequency penalties, attempting to fix both problems at the same time.\n", + " - **Temperature scaling:** Temperature scaling divides logits by a number you set for 'tempterature' to control output \"creativity\" vs \"precision\". Low temperatures less than 1 make the model's top choices more likely, creating predictable text. High temperatures greater than 1, give less likely words a better chance, leading to more diverse and random text. Basically the higher you set it, the more creative and less factual the LLM's writing will be, the lower, the more precice and factual.\n", + " - After applying all scaling, we convert the logits back to probabilities using softmax. We then proceed to sampling:\n", + " - **Top k sampling**: Set a number 'k'. Eliminate all but the highest k numbers on this list of scaled probabilities.\n", + " - **Top p sampling:** Set a number 'p'. Starting from the most likely token, add up the probabilities until the sum reaches or exceeds 'p'. Keep only this cumulative set of tokens.\n", + " \n", + "Now that we have scaled and filtered the list of tokens, we randomly pick one from the remaining options.\n", + "\n", + "\n", + "6. ## The Generation Loop: We just do this on repeat.\n", + "\n", + "The model only predicts one word at a time. To complete text, we repeat this with the result of the original prompt + the result of predicting the next. We call this an **autoregressive** loop.\n", + "\n", + "\n", + " Start with a prompt \"\"Write, a, story\"\n", + " \n", + " Input: [Write, a, story]\n", + " Model predicts the token that decodes to: \"about\"\n", + "\n", + " Repeat 1: New Input: The appended sequence is fed back into the model.\n", + " New Input: [Write, a, story, about]\n", + " Model predicts: \"a\"\n", + "\n", + " Repeat 2:New Input:\n", + " New Input: [Write, a, story, about, a]\n", + " Model predicts: \"fox\"\n", + "\n", + "This loop continues until the model generates a special \"end-of-sequence\" token / pad token or it reaches its maximum length limit (40 tokens in our example).\n", + "\n", + "\n", + "\n", + "## Revisiting the analogy of the auto complete on repeat, this is what this looks like:\n" + ], + "metadata": { + "id": "kMW_6Vrq_Yi9" + } + }, + { + "cell_type": "markdown", + "source": [ + 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)" + ], + "metadata": { + "id": "NaMi9QniKqdO" + } + }, + { + "cell_type": "code", + "source": [ + "# Get the best model from the search\n", + "best_model_found = cerebros_automl.get_best_model(purge_model_storage_files='slate')\n", + "\n", + "# Create config and generative model wrapper\n", + "config = CerebrosNotGPTConfig(\n", + " max_sequence_length=MAX_SEQ_LENGTH,\n", + " padding_token=tokenizer.pad_token_id\n", + ")\n", + "generator = CerebrosNotGPT(config, model=best_model_found)\n", + "\n", + "# Test if the model can be built successfully\n", + "text = \"This is a test ...\"\n", + "input_ids = tokenizer(text, add_special_tokens=False)['input_ids']\n", + "current_tokens = input_ids.copy()\n", + "PADDING_TOKEN = tokenizer.pad_token_id\n", + "\n", + "if len(current_tokens) > MAX_SEQ_LENGTH:\n", + " input_tokens = current_tokens[-MAX_SEQ_LENGTH:]\n", + "else:\n", + " padding_needed = MAX_SEQ_LENGTH - len(current_tokens)\n", + " input_tokens = current_tokens + [PADDING_TOKEN] * padding_needed\n", + "\n", + "# A dummy pass to force the model to build\n", + "\n", + "input_tensor = tf.constant([input_tokens], dtype=tf.int32)\n", + "\n", + "try:\n", + " _ = generator(input_tensor)\n", + " print(\"โœ… Building LLM Model Successful!\")\n", + "except Exception as exc:\n", + " error_message = f\"โŒ Building model returned the error: {exc}\"\n", + " print(error_message)\n" + ], + "metadata": { + "id": "AEk-TtPCxleV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d253eeeb-831e-48ce-f256-c8f10540064a" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/layer.py:421: UserWarning: `build()` was called on layer 'interleaved_ro_pe', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "โœ… Building LLM Model Successful!\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Text Generation Utilities\n", + "\n", + "We define two helper functions for text generation:\n", + "\n", + "- One for greedy sampling\n", + "- One for beam sampling with various parameters." + ], + "metadata": { + "id": "u6-wAM0XyUZC" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Required parameter\n", + "\n", + "trial_number =1\n", + "\n", + "\n", + "# Utility function for greedy sampling\n", + "def complete_text_greedy(text: str, max_new_tokens: int = 10) -> str:\n", + " input_ids = tokenizer(text, add_special_tokens=False)['input_ids']\n", + " generated_tokens = generator.generate(\n", + " token_ids=input_ids,\n", + " do_sample=False,\n", + " max_new_tokens=max_new_tokens\n", + " )\n", + " generated_text = tokenizer.decode(generated_tokens).replace(text, \"\")\n", + " return generated_text\n", + "\n", + "# Utility function for beam sampling\n", + "def complete_text_beam(text: str,\n", + " max_new_tokens: int = 10,\n", + " temperature: float = 0.75,\n", + " top_k: int = 75,\n", + " top_p: float = 0.98,\n", + " repetition_penalty: float = None,\n", + " presence_penalty: float = 1.3,\n", + " frequency_penalty: float = 1.4) -> str:\n", + " input_ids = tokenizer(text, add_special_tokens=False)['input_ids']\n", + " generated_tokens = generator.generate(\n", + " token_ids=input_ids,\n", + " do_sample=True,\n", + " max_new_tokens=max_new_tokens,\n", + " temperature=temperature,\n", + " top_k=top_k,\n", + " top_p=top_p,\n", + " presence_penalty=presence_penalty,\n", + " frequency_penalty=frequency_penalty\n", + " )\n", + " generated_text = tokenizer.decode(generated_tokens).replace(text, \"\")\n", + " return generated_text\n" + ], + "metadata": { + "id": "f8XigcJcykLn" + }, + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Running Generation Tests\n", + "\n", + "We run a series of tests with different prompts and sampling parameters to evaluate the quality of the model from Stage I-a." + ], + "metadata": { + "id": "HG0IjcWEyrXn" + } + }, + { + "cell_type": "code", + "source": [ + "def test_text(test_prompt: str, max_new_tokens: int, result_cutoff: float, trial_id: int,\n", + " test_sample_number: int, result_0: float) -> None:\n", + " \"\"\"\n", + " If the result_0 < result_cutoff, this will run a matrix of different sampling values and print out the resulting text for human subjective evaluation.\n", + "\n", + " Parameters:\n", + " - test_prompt: a string to prompt generation\n", + " - max_new_tokens: int, number of tokens to generate unless we generate a stop token.\n", + " - sample_number: Metadata for sample...\n", + " - result_0: Perplexity score from this run\n", + " - result_cutoff: Perplexity score that would be expected to indicate a trial worth running this pn\n", + "\n", + " \"\"\"\n", + " if result_0 < result_cutoff:\n", + " generation_param_permutations = [\n", + " # #3\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.6,\n", + " 'top_k': 75,\n", + " 'top_p': 0.98,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.3,\n", + " 'frequency_penalty': 1.4\n", + " },\n", + " # #4\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.7,\n", + " 'top_k': 75,\n", + " 'top_p': 0.98,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.3,\n", + " 'frequency_penalty': 1.4\n", + " },\n", + " # #5\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.7,\n", + " 'top_k': 75,\n", + " 'top_p': 0.97,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.3,\n", + " 'frequency_penalty': 1.4},\n", + " # #6\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.75,\n", + " 'top_k': 75,\n", + " 'top_p': 0.98,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.4},\n", + " # #7\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.7,\n", + " 'top_k': 75,\n", + " 'top_p': 0.98,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.4},\n", + " # #8\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.6,\n", + " 'top_k': 75,\n", + " 'top_p': 0.98,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.4\n", + " },\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.6,\n", + " 'top_k': 40,\n", + " 'top_p': 0.96,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.4\n", + " },\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.7,\n", + " 'top_k': 45,\n", + " 'top_p': 0.97,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.3\n", + " }, #\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.6,\n", + " 'top_k': 75,\n", + " 'top_p': 0.99,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.4\n", + " },\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.65,\n", + " 'top_k': 75,\n", + " 'top_p': 0.985,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 1.4,\n", + " 'frequency_penalty': 1.4\n", + " },\n", + " {\n", + " 'max_new_tokens': max_new_tokens,\n", + " 'temperature': 0.8,\n", + " 'top_k': 75,\n", + " 'top_p': 0.99,\n", + " 'repetition_penalty': None,\n", + " 'presence_penalty': 0.7,\n", + " 'frequency_penalty': 0.7\n", + " }\n", + " ]\n", + " # Default cases, no params\n", + " response_1 = complete_text_greedy(text=test_prompt, max_new_tokens=max_new_tokens)\n", + " print(\n", + " f\"Trial #: {trial_id} Text Sample #: {test_sample_number} Perplexity: {result_0} GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: '{test_prompt}' RESPONSE: '{response_1}'\")\n", + " # print(f\"Sample {sample_number}: I ask the generator (greedy): {test_prompt}... It responds: '{response_1}'.\")\n", + " response_2 = complete_text_beam(text=test_prompt, max_new_tokens=max_new_tokens)\n", + " print(\n", + " f\"Trial #: {trial_id} Text Sample #: {test_sample_number} Perplexity: {result_0} GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: '{test_prompt}' RESPONSE: '{response_2}'.\")\n", + " # print(f\"Sample {sample_number}: I ask the generator (Beam defaults - max_new_tokens: 10, temperature: 0.75, top_k: 75, top_p: 0.98, repetition_penalty: None, presence_penalty: 1.3, frequency_penalty: 1.4): {test_prompt}... It responds: '{response_2}'.\")\n", + "\n", + " for perm_0 in generation_param_permutations:\n", + " response_0 = complete_text_beam(text=test_prompt,\n", + " max_new_tokens=max_new_tokens,\n", + " temperature=perm_0['temperature'],\n", + " top_k=perm_0['top_k'],\n", + " top_p=perm_0['top_p'],\n", + " repetition_penalty=perm_0['repetition_penalty'],\n", + " presence_penalty=perm_0['presence_penalty'],\n", + " frequency_penalty=perm_0['frequency_penalty'])\n", + " print(\n", + " f\"Trial #: {trial_id} Text Sample #: {test_sample_number} Perplexity: {result_0} GENERATE PARAMS: max_new_tokens={perm_0['max_new_tokens']} temperature={perm_0['temperature']}, top_k={perm_0['top_k']}, top_p={perm_0['top_p']}, repetition_penalty={perm_0['repetition_penalty']} presence_penalty={perm_0['presence_penalty']} frequency_penalty{perm_0['frequency_penalty']} PROMPT: '{test_prompt}' RESPONSE: '{response_0}'\")\n", + "\n", + "\n", + "prompt_samples = [\n", + " \"I saw the sun and it was as shining on the\",\n", + " \"And God said, Let there be light: and there \",\n", + " \"In the beginning God created the heavens\"\n", + "]\n", + "\n", + "\n", + "counter = 0\n", + "for sample in prompt_samples:\n", + " test_text(\n", + " test_prompt=sample,\n", + " max_new_tokens=MAX_NEW_TOKENS,\n", + " result_cutoff=15,\n", + " trial_id=trial_number,\n", + " test_sample_number=counter,\n", + " result_0=phase_i_a_result)\n", + " counter += 1\n", + "\n", + "\n", + "collect()\n" + ], + "metadata": { + "id": "hut-HAJjyvn-", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e05a9fb1-706e-4f26-e668-825f7df940c2" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth the the the the the the the the the the the the the the'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth God beginning'.\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth. beginning created God'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 13 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 9 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 14 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' created. beginning God earthless earth beginning'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth God.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 10 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 16 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' God earth beginning created heavens. earth created'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' beginning created earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 9 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' created beginning earth heavens. God earth'\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' created earth beginning God heavens. earth'\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 10 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 9 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' God earth beginning created beginning God. earth heavens created beginning heavens earth. earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 9 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 10 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 18 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' created earth beginning God heavens. created earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 7 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 9 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 12 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' beginning earth God created earth heavens'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 26 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 16 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 26 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' earth created heavens earth\\Order.cpt. the beginning'\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the the.. the the the the the the the the the the.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' earth. the'.\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' earth. the.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: 'And God said, Let there be light: and there. earth the'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the. the earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: 'And God said, Let there be light: and there. the earth'\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the. earth'\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the. earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: 'And God said, Let there be light: and there. the earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' the earth.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ''\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'In the beginning God created the heavens' RESPONSE: ' heavens heavens heavens heavens and heavens heavens heavens heavens heavens heavens heavens heavens and and'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth'.\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and. earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' was earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and. earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and. earth'\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and. earth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and earth.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 6 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 3 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 2 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 7.876600742340088 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and created earth.'\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "5885" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Syage I-b: Extended Training\n", + "\n", + "- Now, we take the best model from Stage I-a and continue training it on a larger dataset.\n", + "- This uses a streaming `tf.data.Dataset` generator to allow handling of much larger data sets without using more RAM.\n", + "- This would allow us to select far more samples, but for now, we select a small subset for this small scale environment.\n", + "\n", + "## Streaming Data Generator for Large Datasets\n", + "\n", + "\n", + "The **SampleExpansionGenerator** class, which we create below:\n", + "\n", + " - Applies and streams the same preprocessing logic to the raw text samples as we did in Stage I-a.\n", + " - However, it preprocesses one **sample expansion batch** at a time and stores the resulting expanded samples in memory.\n", + " - It then feeds the resulting expanded samples to the model in batches matching the **model's BATCH_SIZE** as requested by the training loop.\n", + " - **sample expansion batch** is not the same as **the model's BATCH_SIZE**.\n", + "\n", + "For example, we could train on a dataset of 10 \\** 6 samples, while setting the **sample expansion batch size** to 100 while the **model's batch size** is 10.\n", + " - 100 raw text samples will be expoanded at a time.\n", + " - This results in thousands of expanded sub-samples being queued and ready for the model to take.\n", + " - The model will take 10 of these at a time until it does not have 10 left to provide.\n", + " - Then, the generator will then preprocess another 100 text samples and garbage collect.\n", + "\n", + "This allows training on datasets that would be much larger than available memory after expansion, making the training scalable.\n", + "\n", + "\n", + "### The sample expansion batch size should be optimized to balance two opposing forces:\n", + "\n", + " - Memory pressure increases with the number of expanded samples held in memory.\n", + " - Delays are caused by switching back and forth between tensor operations and preprocessing when batches are too small.\n", + "\n" + ], + "metadata": { + "id": "tuhQx2kjy4nn" + } + }, + { + "cell_type": "code", + "source": [ + "# Replace your existing class and function with these:\n", + "class SampleExpansionGenerator:\n", + " def __init__(self,\n", + " raw_text_samples,\n", + " tokenizer,\n", + " sample_expansion_batch_size=50,\n", + " model_batch_size=10,\n", + " prompt_length_0=PROMPT_LENGTH,\n", + " max_seq_length=MAX_SEQ_LENGTH,\n", + " vocabulary_size=VOCABULARY_SIZE):\n", + "\n", + " self.raw_text_samples = raw_text_samples\n", + " self.tokenizer = tokenizer\n", + " self.sample_expansion_batch_size = sample_expansion_batch_size\n", + " self.model_batch_size = model_batch_size\n", + " self.prompt_length_0 = prompt_length_0\n", + " self.max_seq_length = max_seq_length\n", + " self.vocabulary_size = vocabulary_size\n", + " self.data = []\n", + " self.labels = []\n", + " self.current_index = 0\n", + "\n", + " def _expand_next_batch(self):\n", + " # If we've already processed all raw samples for this epoch, do nothing.\n", + " if self.current_index >= len(self.raw_text_samples):\n", + " return\n", + "\n", + " # Determine the next meta-batch\n", + " start_idx = self.current_index\n", + " end_idx = min(start_idx + self.sample_expansion_batch_size, len(self.raw_text_samples))\n", + "\n", + " batch_samples = self.raw_text_samples[start_idx:end_idx]\n", + " self.current_index = end_idx\n", + "\n", + " # Run prepare_data on this batch\n", + " input_ids_list, labels_list, _ = prepare_data(\n", + " data_0=batch_samples,\n", + " tokenizer_0=self.tokenizer,\n", + " max_seq_length=self.max_seq_length,\n", + " prompt_length=self.prompt_length_0)\n", + "\n", + " # Add the new data to our internal queues\n", + " self.data.extend(input_ids_list)\n", + " self.labels.extend(labels_list)\n", + "\n", + " def __iter__(self):\n", + " # Reset to initial state for new epoch\n", + " self.current_index = 0\n", + " self.data = []\n", + " self.labels = []\n", + " return self\n", + "\n", + " def __next__(self):\n", + " # If queues are empty, try to expand them from raw samples\n", + " if not self.data:\n", + " self._expand_next_batch()\n", + "\n", + " # If they are STILL empty after trying to expand, the epoch is over.\n", + " if not self.data:\n", + " raise StopIteration\n", + "\n", + " # Pop and return one sample\n", + " input_sample = self.data.pop(0)\n", + " label_sample = self.labels.pop(0)\n", + "\n", + " return ((input_sample,), label_sample)\n", + "\n", + "\n", + "# Create the tf.data.Dataset\n", + "def create_dataset(raw_text_samples, tokenizer, sample_expansion_batch_size=50, model_batch_size=10) -> tf.data.Dataset:\n", + " generator_0 = SampleExpansionGenerator(\n", + " raw_text_samples=raw_text_samples,\n", + " tokenizer=tokenizer,\n", + " sample_expansion_batch_size=sample_expansion_batch_size,\n", + " model_batch_size=model_batch_size # Pass this parameter\n", + " )\n", + "\n", + " dataset = tf.data.Dataset.from_generator(\n", + " lambda: generator_0,\n", + " # output_signature=(\n", + " # (tf.TensorSpec(shape=(generator_0.max_seq_length,), dtype=tf.int32),),\n", + " # # tf.TensorSpec(shape=(generator_0.max_seq_length,), dtype=tf.int32), # Use generator's parameter\n", + " # tf.TensorSpec(shape=(generator_0.vocabulary_size,), dtype=tf.float32) # Use generator's parameter\n", + " # )\n", + " output_signature=(\n", + " (tf.TensorSpec(shape=(generator_0.max_seq_length,), dtype=tf.int32),), # A tuple containing ONE TensorSpec\n", + " tf.TensorSpec(shape=(generator_0.vocabulary_size,), dtype=tf.float32) # A single TensorSpec\n", + " )\n", + " )\n", + "\n", + " # Batch it\n", + " dataset = dataset.batch(model_batch_size)\n", + " dataset = dataset.prefetch(tf.data.AUTOTUNE) # Prefetch for performance\n", + " return dataset\n", + "\n", + "# Create training and validation datasets\n", + "phase_i_b_train_dataset = create_dataset(\n", + " raw_text_samples=phase_i_b_train_samples,\n", + " tokenizer=tokenizer,\n", + " sample_expansion_batch_size=PHASE_I_B_SAMPLE_EXPANSION_BATCH_SIZE,\n", + " model_batch_size=batch_size\n", + ")\n", + "\n", + "phase_i_b_val_dataset = create_dataset(\n", + " raw_text_samples=phase_i_b_val_samples,\n", + " tokenizer=tokenizer,\n", + " sample_expansion_batch_size=PHASE_I_B_SAMPLE_EXPANSION_BATCH_SIZE,\n", + " model_batch_size=batch_size\n", + ")\n" + ], + "metadata": { + "id": "MHWWE0xIzLRD" + }, + "execution_count": 22, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "type(phase_i_b_train_dataset)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 121 + }, + "id": "HxwyQzSppQwp", + "outputId": "89a48aa5-c364-4057-98c4-fc4a291f448e" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "tensorflow.python.data.ops.prefetch_op._PrefetchDataset" + ], + "text/html": [ + "
\n", + " 
tensorflow.python.data.ops.prefetch_op._PrefetchDataset
def __init__(input_dataset, buffer_size, slack_period=None, name=None)
/usr/local/lib/python3.12/dist-packages/tensorflow/python/data/ops/prefetch_op.pyA `Dataset` that asynchronously prefetches its input.
\n", + " \n", + "
" + ] + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "## Model Compilation for Phase I-b\n", + "\n", + "- We recompile the model with the same base optimizer (AdamW), however this time with a custom learning rate scheduler (WarmupCosineDecayRestarts), and for disambiguation, relevant metrics for this training phase. We also add an EarlyStopping callback which is mainly being used to restore the weights from the best epoch, if that turns out to not be the last epoch.\n", + "\n", + "\n", + "## For those wanting to scale this up, a word to point out:\n", + "\n", + "The parameters for the learning rate scheduler may need to be optimized. They will be different for your data. Alternatively, you can remove the learning rate scheduler if this is too much trail and error.\n", + "\n", + "- We set the starting learning rate at: 0.0039295722955565125\n", + "- We set warmup steps to 1140, which for the data selected is 15 epochs.\n", + "- We set first decay steps to 1900, which for this data set is about 25 epochs.\n", + "\n", + "Also:\n", + "\n", + "Additionally, the early stopping callback will likely need to be adjusted. When training at scale, you may use a lower learning rate and a larger number of epochs, as well as a larger value for the start_from_epoch parameter (which specifies when to begin tracking the metric for early stopping).\n", + "\n", + "FYI, this is the custom scheduler we imported from cerebrosllmutils (CosineDecayRestarts augmented with warmup steps):\n", + "\n", + "\n", + "```python\n", + "# A custom schedule: Cosine decay with some warm - up steps\n", + "@tf.keras.utils.register_keras_serializable(package='cerebrosllmutils', name='WarmupCosineDecayRestarts')\n", + "class WarmupCosineDecayRestarts(tf.keras.optimizers.schedules.LearningRateSchedule):\n", + " \"\"\"\n", + " A learning rate schedule that combines a linear warmup with cosine decay restarts.\n", + " \"\"\"\n", + "\n", + " def __init__(self, initial_learning_rate, warmup_steps, first_decay_steps, t_mul=2.0, m_mul=1.0, alpha=0.0):\n", + " super().__init__()\n", + "\n", + " # Store all parameters as public attributes for get_config serialization\n", + " self.initial_learning_rate = initial_learning_rate\n", + " self.warmup_steps = warmup_steps\n", + " self.first_decay_steps = first_decay_steps\n", + " self.t_mul = t_mul\n", + " self.m_mul = m_mul\n", + " self.alpha = alpha\n", + "\n", + " # Create the CosineDecayRestarts schedule for internal logic.\n", + " # The parameters passed here are the same ones we just stored.\n", + " self.cosine_restarts_schedule = tf.keras.optimizers.schedules.CosineDecayRestarts(\n", + " initial_learning_rate=initial_learning_rate,\n", + " first_decay_steps=first_decay_steps,\n", + " t_mul=t_mul,\n", + " m_mul=m_mul,\n", + " alpha=alpha\n", + " )\n", + "\n", + "\n", + " def __call__(self, step):\n", + " step = tf.cast(step, dtype=tf.float32)\n", + "\n", + " # Calculate the learning rate for both phases unconditionally\n", + " warmup_lr = self.initial_learning_rate * step / self.warmup_steps\n", + "\n", + " # The cosine schedule is designed to start from step 0, so we give it\n", + " # the \"post-warmup\" step count.\n", + " decay_lr = self.cosine_restarts_schedule(step - self.warmup_steps)\n", + "\n", + " # Create a multiplier that is 1.0 during warmup and 0.0 after.\n", + " # tf.cast(condition, tf.float32) converts a boolean tensor to 1.0 or 0.0.\n", + " warmup_multiplier = tf.cast(step < self.warmup_steps, tf.float32)\n", + "\n", + " # The decay multiplier is the opposite.\n", + " decay_multiplier = 1.0 - warmup_multiplier\n", + "\n", + " # Combine the two learning rates. Only one will be active at a time.\n", + " return (warmup_multiplier * warmup_lr) + (decay_multiplier * decay_lr)\n", + "\n", + " def get_config(self):\n", + " # Use the stored public attributes for the config.\n", + " # This bypasses the issue of accessing private attributes (_t_mul) from\n", + " # the nested Keras object, which can be brittle.\n", + " config = {\n", + " \"initial_learning_rate\": self.initial_learning_rate,\n", + " \"warmup_steps\": self.warmup_steps,\n", + " \"first_decay_steps\": self.first_decay_steps,\n", + " \"t_mul\": self.t_mul,\n", + " \"m_mul\": self.m_mul,\n", + " \"alpha\": self.alpha,\n", + " }\n", + "\n", + " # Use from_config to properly allow deserialization\n", + " return config\n", + "```\n", + "\n" + ], + "metadata": { + "id": "DPaeJKEzzlPw" + } + }, + { + "cell_type": "code", + "source": [ + "# Define loss and metrics for Phase I-b\n", + "phase_i_b_loss = tf.keras.losses.CategoricalCrossentropy()\n", + "phase_i_b_categorical_accuracy = tf.keras.metrics.CategoricalAccuracy()\n", + "phase_i_b_perplexity = Perplexity(name=\"perplexity_phase_i_b\")\n", + "\n", + "# Create the learning rate schedule instance\n", + "lr_scheduler = WarmupCosineDecayRestarts(\n", + " initial_learning_rate=INITIAL_LR_STAGE_I_B,\n", + " warmup_steps=WARMUP_STEPS,\n", + " first_decay_steps=FIRST_DECAY_STEPS_STAGE_I_B,\n", + " t_mul=1.0,\n", + " m_mul=0.9,\n", + " alpha=0.01\n", + ")\n", + "\n", + "# Recompile the existing model\n", + "generator.model.compile(\n", + " loss=phase_i_b_loss,\n", + " metrics=[phase_i_b_categorical_accuracy, phase_i_b_perplexity],\n", + " optimizer=tf.keras.optimizers.AdamW(\n", + " learning_rate=lr_scheduler,\n", + " weight_decay=phase_i_b_weight_decay,\n", + " gradient_accumulation_steps=phase_i_b_gradient_accumulation_steps\n", + " ),\n", + " jit_compile=True\n", + ")\n", + "\n", + "# Define the Early Stopping callback\n", + "early_stopping = tf.keras.callbacks.EarlyStopping(\n", + " monitor='perplexity_phase_i_b', # Monitor validation perplexity\n", + " patience=10, # Number of epochs with no improvement after which training will be stopped.\n", + " verbose=1,\n", + " restore_best_weights=True, # Restores model weights from the epoch with the best value of the monitored metric.\n", + " mode='min',\n", + " start_from_epoch=40\n", + ")\n", + "\n", + "\n", + "callbacks_list = [early_stopping]\n" + ], + "metadata": { + "id": "GGkEVa2dzOtf" + }, + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Run Stage I-b Training\n", + "\n", + "- We start the training process using the model.fit method with the new datasets and callbacks to continue training the same model on another dataset. In our at scale runs, both the previous stage and this stage are dene on far more data." + ], + "metadata": { + "id": "y_K5nLzVz_-b" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "\n", + "\n", + "\n", + "# print(\"Calculating steps per epoch...\")\n", + "# train_steps = sum(1 for _ in phase_i_b_train_dataset)\n", + "# val_steps = sum(1 for _ in phase_i_b_val_dataset)\n", + "# print(f\"Calculated training steps per epoch: {train_steps}\")\n", + "# print(f\"Calculated validation steps: {val_steps}\")\n", + "\n", + "# Train the model\n", + "phase_i_b_history = generator.model.fit(\n", + " x=phase_i_b_train_dataset,\n", + " validation_data=phase_i_b_val_dataset,\n", + " epochs=phase_i_b_epochs,\n", + " callbacks=callbacks_list\n", + ")\n", + "\n", + "# Store history and get the best validation perplexity\n", + "phase_i_b_history = pd.DataFrame(phase_i_b_history.history)\n", + "result_phase_i_b = float(phase_i_b_history['perplexity_phase_i_b'].min())\n", + "f\"Result of Stage 1-b training {result_phase_i_b}\"\n" + ], + "metadata": { + "id": "3GGqvlIl0FvV", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "0daf05b2-7072-4818-8b47-a05558b33470" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/53\n", + " 76/Unknown \u001b[1m69s\u001b[0m 636ms/step - categorical_accuracy: 0.0389 - loss: 13.6508 - perplexity_phase_i_b: 966782.2500" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/trainers/epoch_iterator.py:160: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n", + " self._interrupted_warning()\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 690ms/step - categorical_accuracy: 0.0388 - loss: 13.6471 - perplexity_phase_i_b: 962529.6250 - val_categorical_accuracy: 0.0492 - val_loss: 11.5516 - val_perplexity_phase_i_b: 103939.8906\n", + "Epoch 2/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 609ms/step - categorical_accuracy: 0.0164 - loss: 13.8992 - perplexity_phase_i_b: 2969392.7500 - val_categorical_accuracy: 0.0492 - val_loss: 12.0771 - val_perplexity_phase_i_b: 175791.3594\n", + "Epoch 3/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 609ms/step - categorical_accuracy: 0.0250 - loss: 12.8039 - perplexity_phase_i_b: 402124.0625 - val_categorical_accuracy: 0.0656 - val_loss: 12.3528 - val_perplexity_phase_i_b: 231597.3438\n", + "Epoch 4/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 591ms/step - categorical_accuracy: 0.0415 - loss: 11.6595 - perplexity_phase_i_b: 140648.8125 - val_categorical_accuracy: 0.0492 - val_loss: 12.4123 - val_perplexity_phase_i_b: 245801.6250\n", + "Epoch 5/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 611ms/step - categorical_accuracy: 0.0439 - loss: 11.1954 - perplexity_phase_i_b: 73950.6797 - val_categorical_accuracy: 0.0492 - val_loss: 12.3395 - val_perplexity_phase_i_b: 228538.3750\n", + "Epoch 6/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 589ms/step - categorical_accuracy: 0.0816 - loss: 10.2579 - perplexity_phase_i_b: 29194.4102 - val_categorical_accuracy: 0.0656 - val_loss: 12.1179 - val_perplexity_phase_i_b: 183113.2031\n", + "Epoch 7/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 590ms/step - categorical_accuracy: 0.0590 - loss: 9.9608 - perplexity_phase_i_b: 22667.8711 - val_categorical_accuracy: 0.0492 - val_loss: 11.8740 - val_perplexity_phase_i_b: 143489.0312\n", + "Epoch 8/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 599ms/step - categorical_accuracy: 0.0593 - loss: 8.9806 - perplexity_phase_i_b: 8207.2861 - val_categorical_accuracy: 0.0328 - val_loss: 12.3863 - val_perplexity_phase_i_b: 239495.6562\n", + "Epoch 9/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 558ms/step - categorical_accuracy: 0.0661 - loss: 7.8740 - perplexity_phase_i_b: 2828.0859 - val_categorical_accuracy: 0.0164 - val_loss: 11.9790 - val_perplexity_phase_i_b: 159370.9219\n", + "Epoch 10/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 630ms/step - categorical_accuracy: 0.1062 - loss: 6.8127 - perplexity_phase_i_b: 987.0147 - val_categorical_accuracy: 0.0328 - val_loss: 11.2031 - val_perplexity_phase_i_b: 73360.1719\n", + "Epoch 11/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 604ms/step - categorical_accuracy: 0.0687 - loss: 5.7574 - perplexity_phase_i_b: 324.8636 - val_categorical_accuracy: 0.0164 - val_loss: 9.6458 - val_perplexity_phase_i_b: 15456.3154\n", + "Epoch 12/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 686ms/step - categorical_accuracy: 0.0943 - loss: 4.8160 - perplexity_phase_i_b: 124.1660 - val_categorical_accuracy: 0.0492 - val_loss: 8.6260 - val_perplexity_phase_i_b: 5574.9229\n", + "Epoch 13/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 608ms/step - categorical_accuracy: 0.1206 - loss: 4.4321 - perplexity_phase_i_b: 84.3652 - val_categorical_accuracy: 0.0328 - val_loss: 8.1588 - val_perplexity_phase_i_b: 3493.8950\n", + "Epoch 14/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 597ms/step - categorical_accuracy: 0.1237 - loss: 4.4953 - perplexity_phase_i_b: 91.3969 - val_categorical_accuracy: 0.0328 - val_loss: 8.3403 - val_perplexity_phase_i_b: 4189.2686\n", + "Epoch 15/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 579ms/step - categorical_accuracy: 0.0997 - loss: 4.2491 - perplexity_phase_i_b: 70.9299 - val_categorical_accuracy: 0.0656 - val_loss: 8.6163 - val_perplexity_phase_i_b: 5520.8823\n", + "Epoch 16/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 585ms/step - categorical_accuracy: 0.1204 - loss: 4.2542 - perplexity_phase_i_b: 70.9240 - val_categorical_accuracy: 0.0656 - val_loss: 8.7940 - val_perplexity_phase_i_b: 6594.3228\n", + "Epoch 17/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 577ms/step - categorical_accuracy: 0.1386 - loss: 4.2547 - perplexity_phase_i_b: 70.8944 - val_categorical_accuracy: 0.0984 - val_loss: 8.7318 - val_perplexity_phase_i_b: 6196.8022\n", + "Epoch 18/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 597ms/step - categorical_accuracy: 0.1209 - loss: 4.2489 - perplexity_phase_i_b: 70.4136 - val_categorical_accuracy: 0.0984 - val_loss: 8.9164 - val_perplexity_phase_i_b: 7453.2446\n", + "Epoch 19/53\n", + "\u001b[1m76/76\u001b[0m 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5970.1401\n", + "Epoch 22/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 589ms/step - categorical_accuracy: 0.1520 - loss: 4.1843 - perplexity_phase_i_b: 66.0555 - val_categorical_accuracy: 0.0656 - val_loss: 8.3286 - val_perplexity_phase_i_b: 4140.4941\n", + "Epoch 23/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 557ms/step - categorical_accuracy: 0.1807 - loss: 3.8604 - perplexity_phase_i_b: 48.0663 - val_categorical_accuracy: 0.0656 - val_loss: 8.6137 - val_perplexity_phase_i_b: 5506.4224\n", + "Epoch 24/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 608ms/step - categorical_accuracy: 0.1533 - loss: 3.9858 - perplexity_phase_i_b: 54.5812 - val_categorical_accuracy: 0.1148 - val_loss: 8.5935 - val_perplexity_phase_i_b: 5396.4331\n", + "Epoch 25/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 573ms/step - categorical_accuracy: 0.1230 - loss: 4.0118 - perplexity_phase_i_b: 55.6288 - val_categorical_accuracy: 0.1475 - val_loss: 8.6210 - val_perplexity_phase_i_b: 5547.1172\n", + "Epoch 26/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 564ms/step - categorical_accuracy: 0.1588 - loss: 3.8591 - perplexity_phase_i_b: 47.8675 - val_categorical_accuracy: 0.1148 - val_loss: 8.4999 - val_perplexity_phase_i_b: 4914.4688\n", + "Epoch 27/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 582ms/step - categorical_accuracy: 0.1900 - loss: 3.8535 - perplexity_phase_i_b: 47.2824 - val_categorical_accuracy: 0.0820 - val_loss: 8.7680 - val_perplexity_phase_i_b: 6425.2207\n", + "Epoch 28/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 593ms/step - categorical_accuracy: 0.1927 - loss: 3.6720 - perplexity_phase_i_b: 39.7386 - val_categorical_accuracy: 0.0656 - val_loss: 8.7999 - val_perplexity_phase_i_b: 6633.3721\n", + "Epoch 29/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 594ms/step - categorical_accuracy: 0.1848 - loss: 3.8259 - perplexity_phase_i_b: 46.2804 - val_categorical_accuracy: 0.0656 - val_loss: 8.6051 - val_perplexity_phase_i_b: 5459.4458\n", + "Epoch 30/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 597ms/step - categorical_accuracy: 0.1691 - loss: 3.6890 - perplexity_phase_i_b: 40.3801 - val_categorical_accuracy: 0.0984 - val_loss: 8.5689 - val_perplexity_phase_i_b: 5265.4810\n", + "Epoch 31/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 587ms/step - categorical_accuracy: 0.1774 - loss: 3.6971 - perplexity_phase_i_b: 40.6956 - val_categorical_accuracy: 0.0984 - val_loss: 8.7037 - val_perplexity_phase_i_b: 6025.3599\n", + "Epoch 32/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 573ms/step - categorical_accuracy: 0.1597 - loss: 3.6218 - perplexity_phase_i_b: 37.8592 - val_categorical_accuracy: 0.0984 - val_loss: 8.7827 - val_perplexity_phase_i_b: 6520.5991\n", + "Epoch 33/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 573ms/step - categorical_accuracy: 0.2066 - loss: 3.6265 - perplexity_phase_i_b: 38.0441 - val_categorical_accuracy: 0.0984 - val_loss: 8.7695 - val_perplexity_phase_i_b: 6434.8853\n", + "Epoch 34/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 550ms/step - categorical_accuracy: 0.1622 - loss: 3.7388 - perplexity_phase_i_b: 42.4272 - val_categorical_accuracy: 0.1148 - val_loss: 8.6601 - val_perplexity_phase_i_b: 5768.0454\n", + "Epoch 35/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 537ms/step - categorical_accuracy: 0.1974 - loss: 3.4737 - perplexity_phase_i_b: 32.6702 - val_categorical_accuracy: 0.1148 - val_loss: 8.6486 - val_perplexity_phase_i_b: 5702.0361\n", + "Epoch 36/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 603ms/step - categorical_accuracy: 0.1640 - loss: 3.5527 - perplexity_phase_i_b: 35.4395 - val_categorical_accuracy: 0.1148 - val_loss: 8.7015 - val_perplexity_phase_i_b: 6011.7910\n", + "Epoch 37/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 590ms/step - categorical_accuracy: 0.1779 - loss: 3.5903 - perplexity_phase_i_b: 36.4963 - val_categorical_accuracy: 0.1148 - val_loss: 8.7223 - val_perplexity_phase_i_b: 6138.1729\n", + "Epoch 38/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 598ms/step - categorical_accuracy: 0.1935 - loss: 3.5401 - perplexity_phase_i_b: 34.7298 - val_categorical_accuracy: 0.1148 - val_loss: 8.6995 - val_perplexity_phase_i_b: 5999.7402\n", + "Epoch 39/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 622ms/step - categorical_accuracy: 0.2109 - loss: 3.5383 - perplexity_phase_i_b: 34.5639 - val_categorical_accuracy: 0.1148 - val_loss: 8.6650 - val_perplexity_phase_i_b: 5796.6436\n", + "Epoch 40/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 555ms/step - categorical_accuracy: 0.2047 - loss: 3.5124 - perplexity_phase_i_b: 33.9720 - val_categorical_accuracy: 0.1148 - val_loss: 8.7431 - val_perplexity_phase_i_b: 6267.4624\n", + "Epoch 41/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 576ms/step - categorical_accuracy: 0.1514 - loss: 3.5711 - perplexity_phase_i_b: 35.7887 - val_categorical_accuracy: 0.0656 - val_loss: 8.9814 - val_perplexity_phase_i_b: 7953.5283\n", + "Epoch 42/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 590ms/step - categorical_accuracy: 0.1761 - loss: 3.6074 - perplexity_phase_i_b: 37.1983 - val_categorical_accuracy: 0.0984 - val_loss: 9.0303 - val_perplexity_phase_i_b: 8352.2227\n", + "Epoch 43/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 571ms/step - categorical_accuracy: 0.1727 - loss: 3.6003 - perplexity_phase_i_b: 36.7872 - val_categorical_accuracy: 0.0328 - val_loss: 8.9927 - val_perplexity_phase_i_b: 8044.2207\n", + "Epoch 44/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 619ms/step - categorical_accuracy: 0.1786 - loss: 3.7416 - perplexity_phase_i_b: 42.6958 - val_categorical_accuracy: 0.1148 - val_loss: 9.1039 - val_perplexity_phase_i_b: 8990.1494\n", + "Epoch 45/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 594ms/step - categorical_accuracy: 0.2062 - loss: 3.6020 - perplexity_phase_i_b: 37.0046 - val_categorical_accuracy: 0.0984 - val_loss: 9.3867 - val_perplexity_phase_i_b: 11928.1768\n", + "Epoch 46/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 583ms/step - categorical_accuracy: 0.2035 - loss: 3.6276 - perplexity_phase_i_b: 37.9026 - val_categorical_accuracy: 0.0820 - val_loss: 9.5581 - val_perplexity_phase_i_b: 14159.1719\n", + "Epoch 47/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 590ms/step - categorical_accuracy: 0.1784 - loss: 3.4276 - perplexity_phase_i_b: 31.0932 - val_categorical_accuracy: 0.1148 - val_loss: 9.1575 - val_perplexity_phase_i_b: 9485.0088\n", + "Epoch 48/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 583ms/step - categorical_accuracy: 0.1864 - loss: 3.4227 - perplexity_phase_i_b: 31.1301 - val_categorical_accuracy: 0.1148 - val_loss: 9.1156 - val_perplexity_phase_i_b: 9095.7666\n", + "Epoch 49/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 622ms/step - categorical_accuracy: 0.2266 - loss: 3.4226 - perplexity_phase_i_b: 30.7439 - val_categorical_accuracy: 0.0820 - val_loss: 9.4648 - val_perplexity_phase_i_b: 12897.0039\n", + "Epoch 50/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 589ms/step - categorical_accuracy: 0.2455 - loss: 3.4171 - perplexity_phase_i_b: 30.9408 - val_categorical_accuracy: 0.0820 - val_loss: 9.4194 - val_perplexity_phase_i_b: 12325.3525\n", + "Epoch 51/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 596ms/step - categorical_accuracy: 0.2168 - loss: 3.2941 - perplexity_phase_i_b: 27.1144 - val_categorical_accuracy: 0.0984 - val_loss: 9.3049 - val_perplexity_phase_i_b: 10991.2559\n", + "Epoch 52/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 642ms/step - categorical_accuracy: 0.1940 - loss: 3.3548 - perplexity_phase_i_b: 28.8572 - val_categorical_accuracy: 0.0984 - val_loss: 9.1126 - val_perplexity_phase_i_b: 9068.9150\n", + "Epoch 53/53\n", + "\u001b[1m76/76\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 610ms/step - categorical_accuracy: 0.2291 - loss: 3.3674 - perplexity_phase_i_b: 29.2831 - val_categorical_accuracy: 0.1311 - val_loss: 9.1200 - val_perplexity_phase_i_b: 9136.3135\n", + "Restoring model weights from the end of the best epoch: 53.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Result of Stage 1-b training 29.637819290161133'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 25 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Stage I-b: Model Evaluation and Serialization\n", + "\n", + "After extended training, we evaluate the final model performance and save the model and tokenizer for future use.\n" + ], + "metadata": { + "id": "y8Ej2P7D0T8R" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Final Generation Tests on the Stage I-b model checkpoint\n", + "\n", + "Confirm the model works after Stage I-b training." + ], + "metadata": { + "id": "dWlYvYBq0dio" + } + }, + { + "cell_type": "code", + "source": [ + "print(\"########### Phase I-b Model Checkpoint Generation Samples: ###########\")\n", + "\n", + "counter = 0\n", + "for sample in prompt_samples:\n", + " test_text(\n", + " test_prompt=sample,\n", + " max_new_tokens=MAX_NEW_TOKENS,\n", + " result_cutoff=60, #\n", + " trial_id=trial_number,\n", + " test_sample_number=counter,\n", + " result_0=result_phase_i_b\n", + " )\n", + " counter += 1\n" + ], + "metadata": { + "id": "YhGaTbGF0X_d", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8071bc5a-8520-4d13-82e1-cbd941297b4b" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "########### Phase I-b Model Checkpoint Generation Samples: ###########\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ',,, and fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 5 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' for morning, over tree with, fruit lights bring fruit great livestock.''.\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', serve'to lights produce according each kind'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', greater and that creeping waters for fifth'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' for, lights bird produce fourth to its with'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 64 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: 'Be and, image said birds creeping day. God'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 50 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 4 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' night, image to over fish creature earth.''\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' for, to birds bird, according forth every.' man animals'\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 19 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 30 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 30 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 21 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', for plant domin fruition day with'\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 12 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 15 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 10 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' for'lights, great waters eachBe.' fruit also'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 15 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', saying produce livestock for every lights-bearing day fifth give to.''\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 64 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ', fifth give to livestock light fruitful its that day every so.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 65 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 68 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 68 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 67 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 67 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 66 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 67 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 64 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 59 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 14 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 17 non-zero probs\n", + "Trial #: 1 Text Sample #: 0 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'I saw the sun and it was as shining on the' RESPONSE: ' waters,, for to bring its, fruit.' kind.' and its wild'\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'And God said, Let there be light: and there ' RESPONSE: 'And God said, Let there be light: and there,, and fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit fruit'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 50 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' to each the kind fruit that in great birds day. its'.\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 24 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' man was forth fruit great with lesser thing animals'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 50 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' man to each thing multiply the so fruit in that as saw'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 17 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' fly created the so.' livestock fruit according'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 58 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' each fruitful-bearing in animals as man was'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 26 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 21 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 19 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 20 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 1 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' that themBeh fruit man in great according forth signs fruit.''\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 13 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 22 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' them. fruit'\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 26 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' each created so animals'\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 30 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 15 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 14 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' man-bearing lesser so animals each.' wild'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 19 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 21 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 17 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' to man each bring kind fruit forth. its animals'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 50 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 18 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' so man each. fruit in as'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 65 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 67 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 66 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 66 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 64 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + "Trial #: 1 Text Sample #: 1 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'And God said, Let there be light: and there ' RESPONSE: ' man-bearing said forth so in them according signs fruit'\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE SAMPLING PARAMS: Greedy max_new_tokens=10 otherwise - N/A: PROMPT: 'In the beginning God created the heavens' RESPONSE: ',,,,, and day day day lesser'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 15 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: Beam Default - max_new_tokens = 10, temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None, presence_penalty=1.3, frequency_penalty=1.4: PROMPT: 'In the beginning God created the heavens' RESPONSE: ',Let and was living he lesser so multiply seed fruitful livestock.'.\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' set, and said them over was man each it'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 49 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and fruitful, to was forth them'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 47 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 50 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.97, repetition_penalty=None presence_penalty=1.3 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', earth trees seed was and day good rule forth.'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 54 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 49 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 49 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 50 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 48 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 49 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 49 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 21 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 21 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.75, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', and trees them said he day good upon,' thing. fruit'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 46 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 39 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', and trees was day to seed lesser he living earth each'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 30 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 8 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.98, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', and trees was he said day. each living he fruit so'\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 15 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 25 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 22 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 20 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 20 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 19 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 20 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 24 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 27 non-zero probs\n", + ">>> After top_k: [128260] shape, 40 non-zero probs\n", + ">>> After top_p: [128260] shape, 24 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=40, top_p=0.96, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', it earth creatures day living man lesser and he each'\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 34 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 30 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 45 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.7, top_k=45, top_p=0.97, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.3 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' and was living day he rule trees., according'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 35 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 51 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 38 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 40 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 37 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.6, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ', and trees it said upon man was forth day fruit each tree wing multiply'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 36 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 41 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 52 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 45 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 44 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 42 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 43 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.65, top_k=75, top_p=0.985, repetition_penalty=None presence_penalty=1.4 frequency_penalty1.4 PROMPT: 'In the beginning God created the heavens' RESPONSE: ' waters, and was so according each lesser multiply'\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 55 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 63 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 64 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 62 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 61 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 60 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 57 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 56 non-zero probs\n", + ">>> After top_k: [128260] shape, 75 non-zero probs\n", + ">>> After top_p: [128260] shape, 53 non-zero probs\n", + "Trial #: 1 Text Sample #: 2 Perplexity: 29.637819290161133 GENERATE PARAMS: max_new_tokens=15 temperature=0.8, top_k=75, top_p=0.99, repetition_penalty=None presence_penalty=0.7 frequency_penalty0.7 PROMPT: 'In the beginning God created the heavens' RESPONSE: ',ed and to be it, multiply fruitful each'\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Save Model and Tokenizer\n", + "\n", + "- Finally, we save the tokenizer and the trained model weights to disk." + ], + "metadata": { + "id": "-oCAeR4n0mPW" + } + }, + { + "cell_type": "code", + "source": [ + "trial_number = 1 # Make sure to set this to a unique number:\n", + "# Serialize tokenizer\n", + "TOKENIZER_SAVE_PATH = f\"tokenizer-tr-{trial_number}-stage-i-b\"\n", + "tokenizer.save_pretrained(TOKENIZER_SAVE_PATH)\n", + "print(f\"Tokenizer saved to {TOKENIZER_SAVE_PATH}\")\n", + "\n", + "# Serialize model\n", + "MODEL_SAVE_PATH = f\"final_phase_ib_model_tr_{trial_number}-stage-i-b.keras\"\n", + "generator.save(MODEL_SAVE_PATH)\n", + "print(f\"Final model saved to {MODEL_SAVE_PATH}\")\n" + ], + "metadata": { + "id": "ziYdmmII0qfu", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "37a1153f-09a0-4274-9ca2-e280112e65e6" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Tokenizer saved to tokenizer-tr-1-stage-i-b\n", + "Final model saved to final_phase_ib_model_tr_1-stage-i-b.keras\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Serialization Test\n", + "\n", + "- We run an external script (test_llm_serialization.py) to validate that the saved model and tokenizer can be loaded and used correctly." + ], + "metadata": { + "id": "y9Pvhcvl0uGt" + } + }, + { + "cell_type": "code", + "source": [ + "print(f\"๐Ÿงช Running serialization test for Stage I-b trial {trial_number}...\")\n", + "result = subprocess.run(\n", + " f\"python3 test_llm_serialization.py {TOKENIZER_SAVE_PATH} {MODEL_SAVE_PATH}\",\n", + " capture_output=True,\n", + " shell=True,\n", + " text=True # Use text=True for string output\n", + ")\n", + "\n", + "if result.returncode == 0:\n", + " print(\"โœ… Serialization test passed.\")\n", + " print(\"STDOUT:\", result.stdout)\n", + "else:\n", + " print(\"โŒ Serialization test failed.\")\n", + " print(\"STDERR:\", result.stderr)\n", + " if result.stdout:\n", + " print(\"STDOUT:\", result.stdout)\n" + ], + "metadata": { + "id": "qA5Cord40yID", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "389fe0bf-c935-4f49-dd4f-8eea8672c634" + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "๐Ÿงช Running serialization test for Stage I-b trial 1...\n", + "โœ… Serialization test passed.\n", + "STDOUT: โœ… Tokenizer loaded successfully.\n", + "โœ… CerebrosNotGPT model loaded successfully.\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 19 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 29 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 26 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 31 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 30 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 28 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 32 non-zero probs\n", + ">>> After top_k: [128260] shape, 50 non-zero probs\n", + ">>> After top_p: [128260] shape, 33 non-zero probs\n", + "๐Ÿง  (serialized) Prompt: In the beginning God created the Generated Text from Serialized Model: 'In the beginning God created the, waters each trees and to living man according them'\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# And there you have it: What it takes to build an LLM from scratch using our novel architecture.\n" + ], + "metadata": { + "id": "z1lSMQ6i03XC" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "W6lcAxij-Z5r" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From b05b98ddc158b14142d3dcda859263481f4346d6 Mon Sep 17 00:00:00 2001 From: David Thrower Date: Mon, 24 Nov 2025 19:21:53 -0500 Subject: [PATCH 4/4] Update automerge.yml Trigger CICD tests to run. --- .github/workflows/automerge.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/automerge.yml b/.github/workflows/automerge.yml index 81232c1..7508e7f 100644 --- a/.github/workflows/automerge.yml +++ b/.github/workflows/automerge.yml @@ -6,7 +6,7 @@ name: Python application on: push: - branches: [ "main", "279-add-a-jupyter-notebook-for-llm-training" ] + branches: [ "main", "281-fix-misspelling-in-llm-from-scratch-jupyter" ] permissions: