tf.keras.layers.ActivityRegularization

Layer that applies an update to the cost function based input activity.

tf.keras.layers.ActivityRegularization(
    l1=0.0, l2=0.0, **kwargs
)

Args
`l1`	L1 regularization factor (positive float).
`l2`	L2 regularization factor (positive float).

Input shape
Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model.

Output shape
Same shape as input.

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.

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.

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

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

Returns
An operation instance.