Apply additive zero-centered Gaussian noise.
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Compat aliases for migration
See Migration guide for more details.
`tf.compat.v1.keras.layers.GaussianNoise`
tf.keras.layers.GaussianNoise(
stddev, seed=None, **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.
Args | |
---|---|
stddev
|
Float, standard deviation of the noise distribution. |
seed
|
Integer, optional random seed to enable deterministic behavior. |
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. |