tf.keras.layers.GaussianDropout

Apply multiplicative 1-centered Gaussian noise.

tf.keras.layers.GaussianDropout(
    rate, seed=None, **kwargs
)

As it is a regularization layer, it is only active at training time.

Args
`rate`	Float, drop probability (as with `Dropout`). The multiplicative noise will have standard deviation `sqrt(rate / (1 - rate))`.
`seed`	Integer, optional random seed to enable deterministic behavior.

Call arguments
`inputs`	Input tensor (of any rank).
`training`	Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (doing nothing).

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.