layer_gaussian_dropout: Apply multiplicative 1-centered Gaussian noise.

In keras: R Interface to 'Keras'

Apply multiplicative 1-centered Gaussian noise.

Description

As it is a regularization layer, it is only active at training time.

Usage

layer_gaussian_dropout(object, rate, seed = NULL, ...)

Arguments

object	What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is: missing or NULL, the Layer instance is returned. a Sequential model, the model with an additional layer is returned. a Tensor, the output tensor from layer_instance(object) is returned.
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.
...	standard layer arguments.

Input shape

Arbitrary. Use the keyword argument input_shape (list 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.

References

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting Srivastava, Hinton, et al. 2014

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/layers/GaussianDropout

Other noise layers: layer_alpha_dropout(), layer_gaussian_noise()

keras documentation built on Aug. 16, 2023, 1:07 a.m.