# keras_to_tensorflow

**Repository Path**: deeplearningrepos/keras_to_tensorflow

## Basic Information

- **Project Name**: keras_to_tensorflow
- **Description**: General code to convert a trained keras model into an inference tensorflow model
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-30
- **Last Updated**: 2021-08-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Keras to TensorFlow
The **keras_to_tensorflow** is a tool that converts a trained keras model into a ready-for-inference TensorFlow model. *The tool is NOT tailored for TensorFlow 2.0 it SEEMS to be working fine.*

#### Summary
- In the default behaviour, this tool **freezes** the nodes (converts all TF variables to TF constants), and saves the inference graph and weights into a binary protobuf (.pb) file. During freezing, TensorFlow also applies node pruning which removes nodes with no contribution to the output tensor.

- This tool supports multiple output networks and enables the user to rename the output tensors via the `--output_nodes_prefix` flag.
 
- If the `--output_meta_ckpt` flag is set, the checkpoint and metagraph files for TensorFlow will also be exported
which can later be used in the `tf.train.Saver` class to continue training.   


## How to use
Keras models can be saved as a single [`.hdf5` or `h5`] file, which stores both the architecture and weights, using the `model.save()` function.
 This model can be then converted to a TensorFlow model by calling this tool as follows:
    
    python keras_to_tensorflow.py 
        --input_model="path/to/keras/model.h5" 
        --output_model="path/to/save/model.pb"
     
Keras models can also be saved in two separate files where a [`.hdf5` or `h5`] file stores the weights, using the `model.save_weights()` function, and another `.json` file stores the network architecture using the `model.to_json()` function.
In this case, the model can be converted as follows:

    python keras_to_tensorflow.py 
        --input_model="path/to/keras/model.h5" 
        --input_model_json="path/to/keras/model.json" 
        --output_model="path/to/save/model.pb"

Try 

    python keras_to_tensorflow.py --help
to learn about other supported flags (quantize, output_nodes_prefix, save_graph_def).


## Dependencies
- keras
- tensorflow
- absl
- pathlib

## Legacy code
The code on how to freeze and save keras models in previous versions of tensorflow is also available. Back then, the freeze_graph tool (```/tensorflow/python/tools/freeze_graph.py```) was used to convert the variables into constants. This functionality is now handled by ```graph_util.convert_variables_to_constants```