https://github.com/amir-abdi/keras_to_tensorflow
General code to convert a trained keras model into an inference tensorflow model
https://github.com/amir-abdi/keras_to_tensorflow
deep-learning keras machine-learning python tensorflow
Last synced: about 1 month ago
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General code to convert a trained keras model into an inference tensorflow model
- Host: GitHub
- URL: https://github.com/amir-abdi/keras_to_tensorflow
- Owner: amir-abdi
- License: mit
- Created: 2017-05-10T04:43:29.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2020-11-23T20:22:56.000Z (over 4 years ago)
- Last Synced: 2025-04-01T03:35:16.614Z (about 1 month ago)
- Topics: deep-learning, keras, machine-learning, python, tensorflow
- Language: Python
- Size: 146 KB
- Stars: 1,661
- Watchers: 48
- Forks: 538
- Open Issues: 63
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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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```