tf.keras.layers.GaussianNoise

Apply additive zero-centered Gaussian noise.

Inherits From: Layer, Operation

tf.keras.layers.GaussianNoise(
    stddev, seed=None, **kwargs
)

This is useful to mitigate overfitting (you could see it as a form of random data augmentation). Gaussian Noise (GS) is a natural choice as corruption process for real valued inputs.

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

Args
stddev	Float, standard deviation of the noise distribution.
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 noise) 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.

Methods

from_config

View source

@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.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

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.