torch.nn.functional.dropout — PyTorch 1.10.1 documentation
pytorch.org › torchtorch.nn.functional.dropout(input, p=0.5, training=True, inplace=False) [source] During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution. See Dropout for details. Parameters p – probability of an element to be zeroed. Default: 0.5 training – apply dropout if is True.
Implementing Dropout in PyTorch: With Example
wandb.ai › authors › ayusht1. Add Dropout to a PyTorch Model Adding dropout to your PyTorch models is very straightforward with the torch.nn.Dropout class, which takes in the dropout rate – the probability of a neuron being deactivated – as a parameter. self.dropout = nn.Dropout (0.25) We can apply dropout after any non-output layer. 2.
Dropout — PyTorch 1.10.1 documentation
pytorch.org › generated › torchDropout — PyTorch 1.9.1 documentation Dropout 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 using samples from a Bernoulli distribution. Each channel will be zeroed out independently on every forward call.