tf.keras.layers.TFSMLayer

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

Inherits From: Layer, Operation

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

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.

Methods

from_config

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@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).

symbolic_call

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symbolic_call(
    *args, **kwargs
)