tf.keras.layers.SpatialDropout2D

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Spatial 2D version of Dropout.

Inherits From: Dropout

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.SpatialDropout2D, `tf.compat.v2.keras.layers.SpatialDropout2D`

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.

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 (adding dropout) or in inference mode (doing nothing).

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.

References:

• Efficient Object Localization Using Convolutional Networks