tf.keras.layers.TFSMLayer | TensorFlow v2.16.1 (original) (raw)

Reload a Keras model/layer that was saved via SavedModel / ExportArchive.

tf.keras.layers.TFSMLayer(
    filepath,
    call_endpoint='serve',
    call_training_endpoint=None,
    trainable=True,
    name=None,
    dtype=None
)

Arguments
filepath	str or pathlib.Path object. The path to the SavedModel.
call_endpoint	Name of the endpoint to use as the call() method of the reloaded layer. If the SavedModel was created via model.export(), then the default endpoint name is 'serve'. In other cases it may be named 'serving_default'.

Example:

model.export("path/to/artifact")
reloaded_layer = TFSMLayer("path/to/artifact")
outputs = reloaded_layer(inputs)

The reloaded object can be used like a regular Keras layer, and supports training/fine-tuning of its trainable weights. Note that the reloaded object retains none of the internal structure or custom methods of the original object -- it's a brand new layer created around the saved function.

Limitations:

Only call endpoints with a single inputs tensor argument (which may optionally be a dict/tuple/list of tensors) are supported. For endpoints with multiple separate input tensor arguments, consider subclassing TFSMLayer and implementing a call() method with a custom signature.
If you need training-time behavior to differ from inference-time behavior (i.e. if you need the reloaded object to support a training=True argument in __call__()), make sure that the training-time call function is saved as a standalone endpoint in the artifact, and provide its name to the TFSMLayer via the call_training_endpoint argument.

Attributes
input	Retrieves the input tensor(s) of a symbolic operation.Only returns the tensor(s) corresponding to the _first time_the operation was called.
output	Retrieves the output tensor(s) of a layer.Only returns the tensor(s) corresponding to the _first time_the operation was called.

Methods

`from_config`

View source

@classmethod from_config( config )

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config	A Python dictionary, typically the output of get_config.

Returns
A layer instance.

`symbolic_call`

View source

symbolic_call(
    *args, **kwargs
)