Ensure backwards compatibility for legacy H5 saving format #682
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fchollet
reviewed
Aug 8, 2023
| model.add(layers.TimeDistributed(layers.Dense(3))) | ||
| model.compile(optimizer="rmsprop", loss="mse") | ||
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| tf_keras_model = tf.keras.Sequential() |
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In the future tf.keras will be Keras Core. So can we avoid depending on it, and instead depend on a static artifact? Maybe either downloaded or checked into git?
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Agreed, however, for now as I develop and expand this testing suite I will keep this dependency for debugging purposes/changes. After the full testing suite is done (in the next PR or so), I will generate them as h5 artifacts and add them to that PR.
| @@ -484,6 +484,12 @@ def deserialize(config, custom_objects=None): | |||
| arg_spec = inspect.getfullargspec(cls.from_config) | |||
| custom_objects = custom_objects or {} | |||
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| # TODO(nkovela): Replace during Keras 3.0 release | |||
| # Replace keras refs with keras_core | |||
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After the swap, the replacement should go the other way
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Makes sense, fixed the TODO comment.
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This PR ensures backwards compatibility for the legacy H5 saving format, particularly paying attention to differences in configs, arguments, and deserialization methods between tf.keras and Keras Core.
A few tests have been added showing a tf.keras model saved into H5 and then loaded into Keras Core to become a Keras Core model.
The test suite will be expanded in subsequent PRs to ensure greater coverage.