Dropout¶

class
torch.nn.
Dropout
(p: float = 0.5, inplace: bool = False)[source]¶ During training, randomly zeroes some of the elements of the input tensor with probability
p
using samples 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 coadaptation of neurons as described in the paper Improving neural networks by preventing coadaptation of feature detectors .
Furthermore, the outputs are scaled by a factor of $\frac{1}{1p}$ during training. This means that during evaluation the module simply computes an identity function.
 Parameters
p – probability of an element to be zeroed. Default: 0.5
inplace – If set to
True
, will do this operation inplace. 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)