Spatial 2D version of Dropout.


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 this case, layer_spatial_dropout_2d will help promote independence between feature maps and should be used instead.


  data_format = NULL, 
  batch_size = NULL, 
  name = NULL, 
  trainable = NULL, 
  weights = NULL 


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.
data_format ‘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 image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be “channels_last”.
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

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’.

Output shape

Same as input


See Also

Other dropout layers: layer_dropout(), layer_spatial_dropout_1d(), layer_spatial_dropout_3d()