Applies Dropout to the input.

Dropout consists in randomly setting a fraction rate of input units to 0 at each update during training time, which helps prevent overfitting.

layer_dropout(object, rate, noise_shape = NULL, seed = NULL,
  batch_size = NULL, name = NULL, trainable = NULL, weights = NULL)

Arguments

object	Model or layer object
rate	float between 0 and 1. Fraction of the input units to drop.
noise_shape	1D integer tensor representing the shape of the binary dropout mask that will be multiplied with the input. For instance, if your inputs have shape `(batch_size, timesteps, features)` and you want the dropout mask to be the same for all timesteps, you can use `noise_shape=c(batch_size, 1, features)`.
seed	integer to use as random seed.
batch_size	Fixed batch size for layer
name	An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
trainable	Whether the layer weights will be updated during training.
weights	Initial weights for layer.