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► Keras 3 API documentation / Layers API / Backend-specific layers / Tensorflow SavedModel layer

Tensorflow SavedModel layer

[source]

TFSMLayer class

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

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

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.
Tensorflow SavedModel layer
TFSMLayer class