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,
  input_shape = NULL, batch_input_shape = 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.
input_shape	Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.
batch_input_shape	Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors.
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.

See also

Other core layers: layer_activation, layer_activity_regularization, layer_dense, layer_flatten, layer_input, layer_lambda, layer_masking, layer_permute, layer_repeat_vector, layer_reshape

Other dropout layers: layer_spatial_dropout_1d, layer_spatial_dropout_2d, layer_spatial_dropout_3d