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)



Model or layer object


float between 0 and 1. Fraction of the input units to drop.


Fixed batch size for layer


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.


Whether the layer weights will be updated during training.


Initial weights for layer.

Input shape

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

Output shape

Same as input


- Efficient Object Localization Using Convolutional Networks

See also