How to convert TensorFlow model from .pb to .h5

A detailed guide on how to convert a TensorFlow model from the Protocol Buffers (.pb) format to HDF5 (.h5) format, allowing for easy storage and loading of models for machine learning applications.

In this article, we will explore how to convert a TensorFlow model that has been saved in the Protocol Buffers (.pb) format into HDF5 (.h5) format. The .h5 format allows you to store the model, including its architecture, weights, and training configuration.

Python Code

import tensorflow as tf

def convert_pb_to_h5(pb_model_path, h5_model_path):
    # Load the TensorFlow model from .pb file
    model = tf.saved_model.load(pb_model_path)

    # Convert the loaded model to Keras model
    concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
    keras_model = tf.keras.Model(inputs=concrete_func.inputs, outputs=concrete_func.outputs)

    # Save the Keras model to .h5 format
    keras_model.save(h5_model_path)

# Paths to the .pb file and desired .h5 file
pb_model_path = 'path/to/your/model.pb'
h5_model_path = 'path/to/your/model.h5'

# Call the function to perform the conversion
convert_pb_to_h5(pb_model_path, h5_model_path)

Detailed explanation:

1. import tensorflow as tf: Imports the TensorFlow library.
2. def convert_pb_to_h5(pb_model_path, h5_model_path): Defines a function to convert the file from .pb to .h5.
3. model = tf.saved_model.load(pb_model_path): Loads the model from the .pb file.
4. concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]: Retrieves the default signature of the model.
5. keras_model = tf.keras.Model(...): Creates a Keras model from the loaded model.
6. keras_model.save(h5_model_path): Saves the Keras model in .h5 format.
7. pb_model_path and h5_model_path: Paths to the respective input and output files.
8. convert_pb_to_h5(...): Calls the function to execute the conversion.

System requirements:

• Python 3.x
• TensorFlow 2.x

How to install the libraries:

Use pip to install TensorFlow:

pip install tensorflow

Tips:

• Ensure that your TensorFlow model is built and saved correctly to avoid errors when loading.
• Thoroughly test the model after conversion to ensure it behaves as expected.

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