torch / nn / torch.nn
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 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 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 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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