rollback to TF 1.12

Author: Yuval Fernbach

0_Running_TensorFlow_In_SageMaker.ipynb

@@ -119,8 +119,10 @@
     "\n",
     "**Create a copy of the script (training_script/cifar10_keras.py) and save it as training_script/cifar10_keras_sm.py.**\n",
     "\n",
-    "In cifar10_keras_sm.py, update the train,validation,eval arguments to get the data by default from the relevant environment variable: SM_CHANNEL_TRAIN, SM_CHANNEL_VALIDATION, SM_CHANNEL_EVAL\n",
-    "Add the default configuration to the arguments in **cifar10_keras_sm.py**:\n",
+    "In cifar10_keras_sm.py, scroll down to the **if __name__ == '__main__':** section.  \n",
+    "Update the train,validation,eval arguments to get the data by default from the relevant environment variable: SM_CHANNEL_TRAIN, SM_CHANNEL_VALIDATION, SM_CHANNEL_EVAL\n",
+    "Add the default configuration to the arguments in **cifar10_keras_sm.py**.  \n",
+    "The lines should look as following:\n",
     "```python\n",
     "parser.add_argument(\n",
     "        '--train',\n",
@@ -248,7 +250,7 @@
     "                       entry_point='cifar10_keras_sm.py',\n",
     "                       source_dir='training_script',\n",
     "                       role=role,\n",
-    "                       framework_version='1.15.0',\n",
+    "                       framework_version='1.12.0',\n",
     "                       py_version='py3',\n",
     "                       hyperparameters={'epochs' : 1},\n",
     "                       train_instance_count=1, train_instance_type='local')"
@@ -416,8 +418,17 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.5"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

training_script/cifar10_keras.py

@@ -150,10 +150,10 @@ def _input(epochs, batch_size, channel, channel_name):
 def _train_preprocess_fn(image):
     """Preprocess a single training image of layout [height, width, depth]."""
     # Resize the image to add four extra pixels on each side.
-    image = tf.image.resize_with_crop_or_pad(image, HEIGHT + 8, WIDTH + 8)
+    image = tf.image.resize_image_with_crop_or_pad(image, HEIGHT + 8, WIDTH + 8)
 
     # Randomly crop a [HEIGHT, WIDTH] section of the image.
-    image = tf.image.random_crop(image, [HEIGHT, WIDTH, DEPTH])
+    image = tf.random_crop(image, [HEIGHT, WIDTH, DEPTH])
 
     # Randomly flip the image horizontally.
     image = tf.image.random_flip_left_right(image)
@@ -164,12 +164,12 @@ def _train_preprocess_fn(image):
 def _dataset_parser(value):
     """Parse a CIFAR-10 record from value."""
     featdef = {
-        'image': tf.io.FixedLenFeature([], tf.string),
-        'label': tf.io.FixedLenFeature([], tf.int64),
+        'image': tf.FixedLenFeature([], tf.string),
+        'label': tf.FixedLenFeature([], tf.int64),
     }
 
-    example = tf.io.parse_single_example(value, featdef)
-    image = tf.io.decode_raw(example['image'], tf.uint8)
+    example = tf.parse_single_example(value, featdef)
+    image = tf.decode_raw(example['image'], tf.uint8)
     image.set_shape([DEPTH * HEIGHT * WIDTH])
 
     # Reshape from [depth * height * width] to [depth, height, width].