SpatialDropout2D layer
SpatialDropout2D class
tf_keras.layers.SpatialDropout2D(rate, data_format=None, **kwargs)
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. When unspecified, uses
image_data_formatvalue found in your TF-Keras config file at~/.keras/keras.json(if exists) else 'channels_last'. Defaults to '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