layer_dropout: Applies Dropout to the input.

View source: R/layers-dropout.R

layer_dropoutR Documentation

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

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(), layer_dense_features(), 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()


keras documentation built on May 29, 2024, 3:20 a.m.