Weights-only saving & loading

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save_weights method

Model.save_weights(filepath, overwrite=True, max_shard_size=None)

Saves all weights to a single file or sharded files.

By default, the weights will be saved in a single .weights.h5 file. If sharding is enabled (max_shard_size is not None), the weights will be saved in multiple files, each with a size at most max_shard_size (in GB). Additionally, a configuration file .weights.json will contain the metadata for the sharded files.

The saved sharded files contain:

• *.weights.json: The configuration file containing 'metadata' and 'weight_map'.
• *_xxxxxx.weights.h5: The sharded files containing only the weights.

Arguments

• filepath: str or pathlib.Path object. Path where the weights will be saved. When sharding, the filepath must end in .weights.json. If .weights.h5 is provided, it will be overridden.
• overwrite: Whether to overwrite any existing weights at the target location or instead ask the user via an interactive prompt.
• max_shard_size: int or float. Maximum size in GB for each sharded file. If None, no sharding will be done. Defaults to None.

Example

# Instantiate a EfficientNetV2L model with about 454MB of weights.
model = keras.applications.EfficientNetV2L(weights=None)

# Save the weights in a single file.
model.save_weights("model.weights.h5")

# Save the weights in sharded files. Use `max_shard_size=0.25` means
# each sharded file will be at most ~250MB.
model.save_weights("model.weights.json", max_shard_size=0.25)

# Load the weights in a new model with the same architecture.
loaded_model = keras.applications.EfficientNetV2L(weights=None)
loaded_model.load_weights("model.weights.h5")
x = keras.random.uniform((1, 480, 480, 3))
assert np.allclose(model.predict(x), loaded_model.predict(x))

# Load the sharded weights in a new model with the same architecture.
loaded_model = keras.applications.EfficientNetV2L(weights=None)
loaded_model.load_weights("model.weights.json")
x = keras.random.uniform((1, 480, 480, 3))
assert np.allclose(model.predict(x), loaded_model.predict(x))

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load_weights method

Model.load_weights(filepath, skip_mismatch=False, **kwargs)

Load the weights from a single file or sharded files.

Weights are loaded based on the network's topology. This means the architecture should be the same as when the weights were saved. Note that layers that don't have weights are not taken into account in the topological ordering, so adding or removing layers is fine as long as they don't have weights.

Partial weight loading

If you have modified your model, for instance by adding a new layer (with weights) or by changing the shape of the weights of a layer, you can choose to ignore errors and continue loading by setting skip_mismatch=True. In this case any layer with mismatching weights will be skipped. A warning will be displayed for each skipped layer.

Sharding

When loading sharded weights, it is important to specify filepath that ends with *.weights.json which is used as the configuration file. Additionally, the sharded files *_xxxxx.weights.h5 must be in the same directory as the configuration file.

Arguments

• filepath: str or pathlib.Path object. Path where the weights will be saved. When sharding, the filepath must end in .weights.json.
• skip_mismatch: Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weights.

Example

# Load the weights in a single file.
model.load_weights("model.weights.h5")

# Load the weights in sharded files.
model.load_weights("model.weights.json")