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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])
Methods
call
call(
inputs
)
Computes output tensors for new inputs.
compute_output_spec
compute_output_spec(
inputs
)
from_config
@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
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
quantized_call(
*args, **kwargs
)
symbolic_call
symbolic_call(
*args, **kwargs
)
__call__
__call__(
*args, **kwargs
)
Call self as a function.