Is it possible to convert a model from json format back to h5?

/ / Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, Importing Keras model into TensorFlow.js

The process of converting models between different serialization formats is a common requirement in the field of deep learning, particularly when moving between environments or frameworks, such as from Keras (using HDF5 files, `.h5`) to TensorFlow.js (using JSON), and vice versa. The specific question of whether it is possible to convert a model from the TensorFlow.js JSON format back to the Keras HDF5 (`.h5`) format involves consideration of several technical aspects, including what information is present in each format, the available tooling, and potential limitations in the conversion process.

Understanding the Model Formats

1. Keras HDF5 (`.h5`) Format:
– This is the standard serialization format used by Keras for saving entire models, including the model architecture, weights, training configuration (e.g., optimizer, loss, metrics), and the state of the optimizer.
– The HDF5 format is binary and well-supported in Python-based environments.

2. TensorFlow.js JSON Format:
– TensorFlow.js saves models using a JSON file for the model topology (architecture) and one or more binary `.bin` files for the weights.
– The JSON format is text-based, designed for use in web browsers with JavaScript, and generally omits Python-specific configuration, such as certain custom layers or advanced serialization features.

Conversion Workflow

The typical workflow involves converting a Keras model to the TensorFlow.js format for deployment in web environments:

– Keras `.h5` → TensorFlow.js (JSON + weights `.bin`)
Using the `tensorflowjs_converter` command-line tool.

The reverse process—converting from TensorFlow.js's JSON format back to a Keras `.h5` file—may be required if, for instance, a model has been updated or fine-tuned in the browser, and you want to further work with it in Python, or archive the updated model in a server environment.

Is Reverse Conversion Possible?

Yes, it is possible to convert a model from TensorFlow.js's JSON (with weights in `.bin` files) back to the Keras HDF5 (`.h5`) format, although there are specific considerations and requirements:

1. Tool Support:
– The TensorFlow.js library provides a Python module, `tensorflowjs`, that includes functionality for loading TensorFlow.js models and converting them back to Keras format. This is not as prominently documented as the Keras-to-TFJS workflow but is available.
– The function `tensorflowjs.converters.load_keras_model` can be used to load a TensorFlow.js model into Python as a Keras model, after which it can be saved as a `.h5` file.

2. Requirements:
– The original model must have been converted from Keras (or TensorFlow SavedModel) to TensorFlow.js format using supported tools. Custom layers, ops, or certain Python-specific configuration might not be preserved or may require additional handling.
– The JSON topology and weights `.bin` files must be present and accessible.

3. Limitations:
– Any custom layers or functions used in the original Keras model must be provided when loading the model; otherwise, the loading process will fail.
– Some optimizer state or configuration may be lost in the conversion, particularly if the model was trained or modified in the browser using TensorFlow.js. The conversion process focuses on model architecture and weights.
– There may be incompatibilities if the TensorFlow.js model was created or modified in ways not directly supported by Keras.

Practical Example:

Assume you have a TensorFlow.js model directory with the following files:
– `model.json` (model topology and weight manifest)
– `group1-shard1of1.bin` (weight binary file)

To convert this model back to Keras `.h5`, follow these steps:

1. Install the Required Package:

bash
   pip install tensorflowjs

2. Load the Model in Python:

python
   from tensorflowjs.converters import load_keras_model

   # Path to the directory containing model.json and .bin files
   tfjs_model_dir = './tfjs_model_dir'

   # Load the model
   model = load_keras_model(tfjs_model_dir)

3. Save the Model as `.h5`:

python
   model.save('restored_model.h5')

This process reconstructs the Keras model in memory and allows you to save it in the HDF5 format. The restored `.h5` file will contain the model architecture and weights. However, training configuration (optimizer state, compiled loss, and metrics) may need to be re-specified if you plan to continue training.

Special Considerations:

– Custom Layers:
If your model uses custom layers or objects, you need to provide them when loading the model, both in the conversion to TensorFlow.js and back. For instance:

python
  custom_objects = {'MyCustomLayer': MyCustomLayer}
  model = load_keras_model(tfjs_model_dir, custom_objects=custom_objects)

– Model Compatibility:
Ensure that the model was originally compatible with Keras serialization. Models created entirely in TensorFlow.js without reference to Python/Keras may use ops or features not present in Keras, resulting in conversion errors or incomplete models.

– Version Compatibility:
Differences in versioning between TensorFlow.js converters and Keras may lead to incompatibility. It is advisable to use matched versions or test the conversion pipeline for your particular use case.

– Training State:
The conversion will preserve the architecture and weights but may not preserve the training state (e.g., optimizer configuration, epoch, batch size). If you need to resume training, you should recompile the model in Keras with the appropriate optimizer, loss, and metrics.

Alternative Approach: Manual Reconstruction

In scenarios where automatic conversion fails due to incompatibility or missing features, one can manually reconstruct the model:

1. Parse the `model.json` file to understand the architecture.
2. Manually define the architecture in Keras.
3. Load the weights from the binary `.bin` files into the Keras model using the appropriate APIs.

While this method is more labor-intensive, it can be necessary for highly customized models or when using unsupported layers.

References and Documentation

– [TensorFlow.js Converter Documentation](https://www.tensorflow.org/js/tutorials/conversion/import_keras)
– [Keras Model Serialization](https://keras.io/guides/serialization_and_saving/)
– [TensorFlow.js GitHub Issues](https://github.com/tensorflow/tfjs)

Converting models from TensorFlow.js's JSON format back to Keras's HDF5 format is supported and feasible, provided the model was originally exported from a compatible Keras model and has not been fundamentally altered in ways incompatible with Keras. The process requires the TensorFlow.js Python API, careful handling of custom layers, and awareness of possible loss in training configuration. Manual reconstruction is a viable alternative when automated tools cannot handle particular customizations.

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