Keras load_model with custom objects doesn't work properly
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Keras, a high-level neural networks API written in Python, runs on top of TensorFlow, and is widely used for its simplicity and ease of use. One of the common challenges faced by deep learning practitioners using Keras is loading models that include custom objects. This issue arises frequently due to differences in serialization and deserialization processes of custom layers, activations, loss functions, or other components within the model architecture.
Understanding the Problem
When you save a Keras model that includes custom objects, such as a custom layer or activation function, you may run into trouble while loading it back. Keras uses the HDF5 format for saving models and their weights. However, custom objects aren't automatically understood by the Keras library when deserializing.
How Model Loading Works
Loading a Keras model involves the following steps:
- Architecture: The model architecture is reconstructed.
- Weights: The weights of the model are loaded.
- Compiler: The model is re-compiled using the saved configuration.
These steps require the entire model, including any custom objects, to be fully interpretable by the Keras backend.
Example of `load_model` Failure
Consider the following custom activation function:
- Dependency Changes: If a custom object relies on another library or method that has changed, it can still cause loading failures.
- Multiple Custom Objects: Managing multiple custom objects can make maintaining the `custom_objects` dict cumbersome.
- Standardize Custom Objects: Wrap custom objects in a standardized format like a class, where possible.
- Testing: Regularly test saving and loading models during development to catch issues early.
- Documentation: Maintain comprehensive documentation for any custom components used within models.
- Environment Management: Use virtual environments or containers to ensure consistent dependencies.
- Version Control: Track changes in custom utilities and continuously integrate them with your versions control system to trace issues promptly.
Related reading
- keras loss function for 360 degree prediction
- Keras `Loss` Function with Additional Dynamic Parameter
- Keras loss keeps increasing
- Keras LSTM - Validation `Loss` Increasing From Epoch 1
- Keras LSTM - why different results with same model same weights?
- Keras LSTM a time-series multi-step multi-features forecasting - poor results
- Keras LSTM input dimension setting
- Keras LSTM model for binary classification with sequences
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ML System Design practice on Codemia
Design recommenders, ranking systems and training pipelines the way ML interviews actually ask for them, with worked solutions.