View source on GitHub
description: Apply multiplicative 1-centered Gaussian noise.
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View source on GitHub
|
Apply multiplicative 1-centered Gaussian noise.
tf.keras.layers.GaussianDropout(
rate, **kwargs
)
As it is a regularization layer, it is only active at training time.
Arguments | |
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rate
|
Float, drop probability (as with Dropout).
The multiplicative noise will have
standard deviation sqrt(rate / (1 - rate)).
|
inputs: Input tensor (of any rank).training: Python boolean indicating whether the layer should behave in
training mode (adding dropout) 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.