TensorFlow 1 version | View source on GitHub |
Apply additive zero-centered Gaussian noise.
Inherits From: Layer
tf.keras.layers.GaussianNoise(
stddev, **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.
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.