This version performs the same function as Dropout, however it drops entire
2D feature maps instead of individual elements. If adjacent pixels 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
layer_spatial_dropout_2d will help promote independence between
feature maps and should be used instead.
layer_spatial_dropout_2d( object, rate, data_format = NULL, batch_size = NULL, name = NULL, trainable = NULL, weights = NULL )
What to call the new
float between 0 and 1. Fraction of the input units to drop.
'channels_first' or 'channels_last'. In 'channels_first'
mode, the channels dimension (the depth) is at index 1, in 'channels_last'
mode is it at index 3. It defaults to the
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.
4D tensor with shape:
(samples, channels, rows, cols)
if data_format='channels_first' or 4D tensor with shape:
(samples, rows, cols, channels) if data_format='channels_last'.
Same as input