# Apply additive zero-centered Gaussian noise.

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.

layer_gaussian_noise(object, stddev, input_shape = NULL,
batch_input_shape = NULL, batch_size = NULL, dtype = NULL,
name = NULL, trainable = NULL, weights = NULL)

## Arguments

 object Model or layer object stddev float, standard deviation of the noise distribution. input_shape Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model. batch_input_shape Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors. batch_size Fixed batch size for layer dtype The data type expected by the input, as a string (float32, float64, int32...) name An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided. trainable Whether the layer weights will be updated during training. weights Initial weights for layer.

## 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.

Other noise layers: layer_alpha_dropout, layer_gaussian_dropout