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Apply additive zero-centered Gaussian noise.
Compat aliases for migration
See Migration guide for more details.
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.
| ||Float, standard deviation of the noise distribution.|
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).
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.
Same shape as input.
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Code samples licensed under the Apache 2.0 License.