tf.keras.layers.AlphaDropout

TensorFlow 1 version View source on GitHub

Class AlphaDropout

Applies Alpha Dropout to the input.

Inherits From: Layer

Aliases:

Alpha Dropout is a Dropout that keeps mean and variance of inputs to their original values, in order to ensure the self-normalizing property even after this dropout. Alpha Dropout fits well to Scaled Exponential Linear Units by randomly setting activations to the negative saturation value.

Arguments:

  • rate: float, drop probability (as with Dropout). The multiplicative noise will have standard deviation sqrt(rate / (1 - rate)).
  • seed: A Python integer to use as random seed.

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.

__init__

View source

__init__(
    rate,
    noise_shape=None,
    seed=None,
    **kwargs
)