Dropout#

classtorch.nn.modules.dropout.Dropout(p=0.5,inplace=False)[source]#

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

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 andpreventing the co-adaptation of neurons as described in the paperImproving neural networks by preventing co-adaptation of featuredetectors .

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

Parameters

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

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

Shape:

• Input:$(\ast )$(∗). Input can be of any shape

• Output:$(\ast )$(∗). Output is of the same shape as input

Examples:

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

forward(input)[source]#

Runs the forward pass.

Return type

Tensor