ยป Keras API reference / Layers API / Regularization layers / GaussianNoise layer

GaussianNoise layer

GaussianNoise class

tf.keras.layers.GaussianNoise(stddev, **kwargs)

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.

Arguments

  • stddev: Float, standard deviation of the noise distribution.

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).

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.