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Showing content from https://docs.pytorch.org/docs/stable/generated/torch.nn.Dropout.html below:

Dropout — PyTorch 2.8 documentation

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

During training, randomly zeroes some of the elements of the input tensor with probability p.

The zeroed elements are chosen independently for each forward call and are sampled from a Bernoulli distribution.

Each channel will be zeroed out independently on every forward call.

This has proven to be an effective technique for regularization and preventing the co-adaptation of neurons as described in the paper Improving neural networks by preventing co-adaptation of feature detectors .

Furthermore, the outputs are scaled by a factor of 1 1 − p \frac{1}{1-p} 1−p1​ during training. This means that during evaluation the module simply computes an identity function.

Parameters

• p (float) – probability of an element to be zeroed. Default: 0.5

• inplace (bool) – If set to True, will do this operation in-place. Default: False

Shape:

• Input: ( ∗ ) (*) (∗). Input can be of any shape

• Output: ( ∗ ) (*) (∗). Output is of the same shape as input

Examples:

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

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