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()