TensorFlow 2 version | View source on GitHub |
Apply multiplicative 1-centered Gaussian noise.
Inherits From: Layer
tf.keras.layers.GaussianDropout(
rate, **kwargs
)
As it is a regularization layer, it is only active at training time.
Arguments | |
---|---|
rate
|
Float, drop probability (as with Dropout ).
The multiplicative noise will have
standard deviation sqrt(rate / (1 - rate)) .
|
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).
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.