Update README.md

Author: serizba

README.md

@@ -1,57 +1,39 @@
 # CppFlow 2
 
-This version is under development.
-
 Run TensorFlow models in c++ without Bazel, without TensorFlow installation and without compiling Tensorflow. Perform tensor manipulation, use eager execution and run saved models directly from C++.
 
 ```c++
-    // Read the graph
-    cppflow::model model("saved_model_folder");
-    
-    // Load an image
-    auto input = cppflow::decode_jpeg(cppflow::read_file(std::string("image.jpg")));
-
-    // Cast it to float, normalize to range [0, 1], and add batch_dimension
-    input = cppflow::cast(input, TF_UINT8, TF_FLOAT);
-    input = input / 255.f;
-    input = cppflow::expand_dims(input, 0);
-
-    // Run
-    auto output = model(input);
-
-    // Show the predicted class
-    std::cout << cppflow::arg_max(output, 1) << std::endl;
-```
-
-You can take a look to the [examples](https://github.com/serizba/cppflow/tree/cppflow2/examples) to see a full example on how to load a deep network and feed it with a sample image.
-
-## Documentation
-
-Check the docs at [https://serizba.github.io/cppflow/](https://serizba.github.io/cppflow/).
+// Read the graph
+cppflow::model model("saved_model_folder");
 
-There you can find quickstart guides and more information about how to install the library and run the examples.
-
-## Development
+// Load an image
+auto input = cppflow::decode_jpeg(cppflow::read_file(std::string("image.jpg")));
 
-CppFlow is basically a wrapper over TensorFlow C API. The basic class, [tensor](https://github.com/serizba/cppflow/blob/cppflow2/include/cppflow/tensor.h) is a wrapper of a TF eager tensor, and it just constains a pointer to its TF representation. 
+// Cast it to float, normalize to range [0, 1], and add batch_dimension
+input = cppflow::cast(input, TF_UINT8, TF_FLOAT);
+input = input / 255.f;
+input = cppflow::expand_dims(input, 0);
 
-The TF C API provides the tools to call all the TF [raw ops](https://www.tensorflow.org/api_docs/python/tf/raw_ops), but using them is confusing. CppFlow includes a facade over these functions, so they can be called easily as normal C++ functions. To achieve this, the file [ops](https://github.com/serizba/cppflow/blob/cppflow2/include/cppflow/raw_ops.h) contains (mostly) all the TF raw ops functions, but with a simple C++ interface. This file has been generated automatically using a [small script](https://github.com/serizba/cppflow/blob/cppflow2/include/cppflow/ops_generator/generator.py).
+// Run
+auto output = model(input);
 
-CppFlow also includes a wrapper on TF saved models, the [model](https://github.com/serizba/cppflow/blob/cppflow2/include/cppflow/model.h) class, so they can be easily opened and executed.
+// Show the predicted class
+std::cout << cppflow::arg_max(output, 1) << std::endl;
+```
 
-As this is still a work under development, there are still many things to do... some of them may be:
+You can take a look to the [examples](https://github.com/serizba/cppflow/tree/master/examples/) to see a full example on how to load a deep network and feed it with a sample image.
 
+CppFlow uses [Tensorflow C API](https://www.tensorflow.org/install/lang_c) to run the models, meaning you can use it without installing Tensorflow and without compiling the whole Tensorflow repository with bazel, you just need to download the C API. With this project you can manage and run your models in C++ without worrying about void, malloc or free. With CppFlow you easily can:
 
-  - Model complex invoking: Enable calling model with more than one input and to produce more than one output
-  - Model eager API: Calling model with the eager API instead of the TF_SessionRun API. I have tried using TF_GraphToFunction but I could not achieve it.
-  - Cover more raw_ops: Currently, the generator that creates the raw_ops facade converts many of the raw_ops but not all of them. Improve the generator to cover these cases (which are marked in the generator code).
-  - Include testing
+* Open saved models created with Python
+* Execute Tensorflow neural networks in C++
+* Perform tensor manipulation directly from C++
 
 ## How To Run It
 
-Since it uses TensorFlow 2 C API you just have to [download it](https://www.tensorflow.org/install/lang_c).  
+Since it uses TensorFlow 2 C API you just have to [download it](https://www.tensorflow.org/install/lang_c), check the [docs](https://serizba.github.io/cppflow/installation.html) to see a guide on how to do it.  
 
-You can either install the library system wide by following the tutorial on the Tensorflow page or you can place the contents of the archive in a folder called `libtensorflow2` in the home directory.
+You can either install the library system wide or you can just place the contents of the archive in a folder called `libtensorflow2` in your HOME directory.
 
 Afterwards, you can run the examples:
 
@@ -64,3 +46,30 @@ cmake ..
 make
 ./example
 ```
+
+
+## Documentation
+
+Check the docs at [https://serizba.github.io/cppflow/](https://serizba.github.io/cppflow/).
+
+There you can find quickstart guides and more information about how to install the library and run the examples.
+
+## Development
+
+CppFlow is basically a wrapper over Tensorflow C API. The basic class, [tensor](https://github.com/serizba/cppflow/blob/master/include/cppflow/tensor.h) is a wrapper of a TF eager tensor, and it just constains a pointer to its TF representation. 
+
+The TF C API provides the tools to call all the TF [raw ops](https://www.tensorflow.org/api_docs/python/tf/raw_ops), but using them is confusing. CppFlow includes a facade over these functions, so they can be called easily as normal C++ functions. To achieve this, the file [ops](https://github.com/serizba/cppflow/blob/master/include/cppflow/raw_ops.h) contains (mostly) all the TF raw ops functions, but with a simple C++ interface. This file has been generated automatically using a [small script](https://github.com/serizba/cppflow/blob/master/include/cppflow/ops_generator/generator.py).
+
+CppFlow also includes a wrapper on TF saved models, the [model](https://github.com/serizba/cppflow/blob/master/include/cppflow/model.h) class, so they can be easily opened and executed.
+
+There are still many things to implement... some of them may be:
+
+
+* Model complex invoking
+* Model eager API: Calling model with the eager API instead of the TF_SessionRun API. I have tried using TF_GraphToFunction but I could not achieve it.
+* Cover more raw_ops: Currently, the generator that creates the raw_ops facade converts many of the raw_ops but not all of them. Improve the generator to cover these cases (which are marked in the generator code).
+* Include testing
+
+## Cppflow 1
+
+You can also use the [older version](https://github.com/serizba/cppflow/tree/243ff2fc4e33632b91676cad7d6cfc3c92308601) of this work.