tf.keras.layers.GaussianDropout

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Apply multiplicative 1-centered Gaussian noise.

Inherits From: Layer

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.GaussianDropout, `tf.compat.v2.keras.layers.GaussianDropout`

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.