► Keras 3 API documentation / Layers API / Regularization layers / SpatialDropout2D layer

SpatialDropout2D layer

[source]

SpatialDropout2D class

keras.layers.SpatialDropout2D(
    rate, data_format=None, seed=None, name=None, dtype=None
)

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, SpatialDropout2D will help promote independence between feature maps and should be used instead.

Arguments

  • 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".

Call arguments

  • inputs: A 4D tensor.
  • training: Python boolean indicating whether the layer should behave in training mode (applying dropout) or in inference mode (pass-through).

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.

Reference

SpatialDropout2D layer
SpatialDropout2D class