save

FullyConnected.save(filepath, overwrite=True, save_format=None, **kwargs)

Saves a model as a TensorFlow SavedModel or HDF5 file.

See the [Serialization and Saving guide](

https://keras.io/guides/serialization_and_saving/) for details.

Parameters:

• model – Keras model instance to be saved.

• filepath – str or pathlib.Path object. Path where to save the model.

• overwrite – Whether we should overwrite any existing model at the target location, or instead ask the user via an interactive prompt.

• save_format – Either “keras”, “tf”, “h5”, indicating whether to save the model in the native Keras format (.keras), in the TensorFlow SavedModel format (referred to as “SavedModel” below), or in the legacy HDF5 format (.h5). Defaults to “tf” in TF 2.X, and “h5” in TF 1.X.

SavedModel format arguments:

include_optimizer: Only applied to SavedModel and legacy HDF5

formats. If False, do not save the optimizer state. Defaults to True.

signatures: Only applies to SavedModel format. Signatures to save

with the SavedModel. See the signatures argument in tf.saved_model.save for details.

options: Only applies to SavedModel format.

tf.saved_model.SaveOptions object that specifies SavedModel saving options.

save_traces: Only applies to SavedModel format. When enabled, the

SavedModel will store the function traces for each layer. This can be disabled, so that only the configs of each layer are stored. Defaults to True. Disabling this will decrease serialization time and reduce file size, but it requires that all custom layers/models implement a get_config() method.

Example:

```python model = tf.keras.Sequential([

tf.keras.layers.Dense(5, input_shape=(3,)), tf.keras.layers.Softmax()])

model.save(“model.keras”) loaded_model = tf.keras.models.load_model(“model.keras”) x = tf.random.uniform((10, 3)) assert np.allclose(model.predict(x), loaded_model.predict(x)) ```

Note that model.save() is an alias for tf.keras.models.save_model().