keras: layer_dropout – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

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

`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.

layer_dropout

Description

Usage

Arguments

See Also

keras

We don't support your browser anymore