Dropout — PyTorch 2.7 documentation (original) (raw)

class torch.nn.Dropout(p=0.5, inplace=False)[source][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 paperImproving neural networks by preventing co-adaptation of feature detectors .

Furthermore, the outputs are scaled by a factor of 11−p\frac{1}{1-p} 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)