layer_gaussian_noise: Apply additive zero-centered Gaussian noise.

In keras: R Interface to 'Keras'

Apply additive zero-centered Gaussian noise.

Description

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.

Usage

layer_gaussian_noise(object, stddev, seed = NULL, ...)

Arguments

object	What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is: missing or NULL, the Layer instance is returned. a Sequential model, the model with an additional layer is returned. a Tensor, the output tensor from layer_instance(object) is returned.
stddev	float, standard deviation of the noise distribution.
seed	Integer, optional random seed to enable deterministic behavior.
...	standard layer arguments.

Input shape

Arbitrary. Use the keyword argument input_shape (list 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.

See Also

Other noise layers: layer_alpha_dropout(), layer_gaussian_dropout()

keras documentation built on May 29, 2024, 3:20 a.m.