layer_dropout {keras} | R 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` | 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`

[Package keras version 2.2.4 Index]