R/layers-dropout.R

layer_dropout

Applies Dropout to the input.

Description

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

Usage

 
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

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 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_attention(), layer_dense_features(), 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()