Apply multiplicative 1-centered Gaussian noise.
Inherits From: Layer, Module
tf.keras.layers.GaussianDropout(
rate, seed=None, **kwargs
)
As it is a regularization layer, it is only active at training time.
Args |
|---|
rate
|
Float, drop probability (as with Dropout).
The multiplicative noise will have
standard deviation sqrt(rate / (1 - rate)).
|
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 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.
|
Output shape |
|---|
|
Same shape as input.
|
View source on GitHub