class torch.nn.Dropout2d(p: float = 0.5, inplace: bool = False)
[source]
Randomly zero out entire channels (a channel is a 2D feature map, e.g., the $j$ -th channel of the $i$ -th sample in the batched input is a 2D tensor $\text{input}[i, j]$ ). Each channel will be zeroed out independently on every forward call with probability p
using samples from a Bernoulli distribution.
Usually the input comes from nn.Conv2d
modules.
As described in the paper Efficient Object Localization Using Convolutional Networks , if adjacent pixels within feature maps are strongly correlated (as is normally the case in early convolution layers) then i.i.d. dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease.
In this case, nn.Dropout2d()
will help promote independence between feature maps and should be used instead.
Examples:
>>> m = nn.Dropout2d(p=0.2) >>> input = torch.randn(20, 16, 32, 32) >>> output = m(input)
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Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.nn.Dropout2d.html