tf.keras.layers.SpatialDropout2D

Spatial 2D version of Dropout.

Inherits From: Dropout, Layer, Module

tf.keras.layers.SpatialDropout2D(
    rate, data_format=None, **kwargs
)

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.

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. When unspecified, uses image_data_format value found in your Keras config file at ~/.keras/keras.json (if exists) else 'channels_last'. Defaults to 'channels_last'.

inputs A 4D tensor.
training Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (doing nothing).

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. References: - Efficient Object Localization Using Convolutional Networks