Spatial 1D version of Dropout.

This version performs the same function as Dropout, however it drops entire 1D feature maps instead of individual elements. If adjacent frames within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, layer_spatial_dropout_1d will help promote independence between feature maps and should be used instead.

layer_spatial_dropout_1d(object, rate, 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.

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.

Input shape

3D tensor with shape: (samples, timesteps, channels)

Output shape

Same as input

References

- Efficient Object Localization Using Convolutional Networks

See also