layer_alpha_dropout
Applies Alpha Dropout to the input.
Description
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.
Usage
layer_alpha_dropout(object, rate, noise_shape = NULL, seed = NULL, ...) Arguments
| Arguments | Description |
|---|---|
| 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 layer_dropout()). The multiplicative noise will have standard deviation sqrt(rate / (1 - rate)). |
| noise_shape | Noise shape |
| seed | An integer to use as random seed. |
| … | standard layer arguments. |
Details
Alpha Dropout fits well to Scaled Exponential Linear Units by randomly setting activations to the negative saturation value.
Section
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
See Also
https://www.tensorflow.org/api_docs/python/tf/keras/layers/AlphaDropout Other noise layers: layer_gaussian_dropout(), layer_gaussian_noise()