tf.keras.Function

Class that encapsulates a computation graph of Keras operations.

Inherits From: Operation

tf.keras.Function(     inputs, outputs, name=None )

You can use a Function to capture the computation graph linking some input tensors to some output tensors, and reapply the same computation on new inputs.

A Function is similar to a Functional Model, with the difference that it is stateless (it does not track state variables) and does not implement the Layer API.

Example:

input_1 = keras.KerasTensor(shape=(None, 2, 3)) input_2 = keras.KerasTensor(shape=(None, 2, 3)) x = input_1 + input_2 output = keras.ops.sigmoid(x) fn = keras.Function(inputs=[input_1, input_2], outputs=output)  input_1_val = np.random.random((4, 2, 3)) input_2_val = np.random.random((4, 2, 3)) output_val = fn([input_1_val, input_2_val])

inputs KerasTensor instance or nested structured of KerasTensor instances.
outputs KerasTensor instance or nested structured of KerasTensor instances. They should be computable given only the values of inputs.
name String. The name of the function.

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

inputs

operations

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

outputs

Methods

call

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call(     inputs )

Computes output tensors for new inputs.

compute_output_spec

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compute_output_spec(     inputs )

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).

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

Returns
A layer instance.

get_config

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get_config()

Returns the config of the object.

An object config is a Python dictionary (serializable) containing the information needed to re-instantiate it.

quantized_call

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quantized_call(     *args, **kwargs )

symbolic_call

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symbolic_call(     *args, **kwargs )

__call__

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__call__(     *args, **kwargs )

Call self as a function.