dropout1d(x: Tensor, p: float = 0.5, training: bool = True) -> Tensor

The documentation is referenced from:

Randomly zero out entire channels (a channel is a 2D feature map, e.g., the \(j\)-th channel of the \(i\)-th sample in the batched input is a 2D tensor \(\text{input}[i, j]\)) of the input tensor). Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.

See Dropout2d for details.

  • p – probability of a channel to be zeroed. Default: 0.5

  • training – apply dropout if is True. Default: True