tf.saved_model.LoadOptions

Options for loading a SavedModel.

tf.saved_model.LoadOptions(
    allow_partial_checkpoint=False,
    experimental_io_device=None,
    experimental_skip_checkpoint=False,
    experimental_variable_policy=None,
    experimental_load_function_aliases=False
)

Used in the notebooks

Used in the tutorials
Save and load a model using a distribution strategy

This function may be used in the options argument in functions that load a SavedModel (tf.saved_model.load, tf.keras.models.load_model).

Args
allow_partial_checkpoint	bool. Defaults to False. When enabled, allows the SavedModel checkpoint to not entirely match the loaded object.
experimental_io_device	string. Applies in a distributed setting. Tensorflow device to use to access the filesystem. If None (default) then for each variable the filesystem is accessed from the CPU:0 device of the host where that variable is assigned. If specified, the filesystem is instead accessed from that device for all variables. This is for example useful if you want to load from a local directory, such as "/tmp" when running in a distributed setting. In that case pass a device for the host where the "/tmp" directory is accessible.
experimental_skip_checkpoint	bool. Defaults to False. If set to True, checkpoints will not be restored. Note that this in the majority of cases will generate an unusable model.
experimental_variable_policy	string. The policy to apply to variables when loading. This is either a saved_model.experimental.VariablePolicy enum instance or one of its value strings (case is not important). See that enum documentation for details. A value of None corresponds to the default policy.
experimental_load_function_aliases	bool. Defaults to False. If set to True, a function_aliases attribute will be added to the loaded SavedModel object.

Attributes
allow_partial_checkpoint
experimental_io_device
experimental_load_function_aliases
experimental_skip_checkpoint
experimental_variable_policy