class torch.nn.Dropout2d(p=0.5, inplace=False)[source]

Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj-th channel of the ii-th sample in the batched input is a 2D tensor input[i,j]\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.

  • p (float, optional) – probability of an element to be zero-ed.

  • inplace (bool, optional) – If set to True, will do this operation in-place


Due to historical reasons, this class will perform 1D channel-wise dropout for 3D inputs (as done by nn.Dropout1d). Thus, it currently does NOT support inputs without a batch dimension of shape (C,H,W)(C, H, W). This behavior will change in a future release to interpret 3D inputs as no-batch-dim inputs. To maintain the old behavior, switch to nn.Dropout1d.

  • Input: (N,C,H,W)(N, C, H, W) or (N,C,L)(N, C, L).

  • Output: (N,C,H,W)(N, C, H, W) or (N,C,L)(N, C, L) (same shape as input).


>>> m = nn.Dropout2d(p=0.2)
>>> input = torch.randn(20, 16, 32, 32)
>>> output = m(input)


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