Using Dropout in Pytorch: nn.Dropout vs. F.dropout

Question

By using pyTorch there is two ways to dropout torch.nn.Dropout and torch.nn.functional.Dropout.

I struggle to see the difference between t

Answer 1

Check torch.nn.functional's implementation:

 if p < 0. or p > 1.:
        raise ValueError("dropout probability has to be between 0 and 1, "
                         "but got {}".format(p))
    return (_VF.dropout_(input, p, training)
            if inplace
            else _VF.dropout(input, p, training))

Check: torch.nn.dropout's implementation:

def forward(self, input):
        return F.dropout(input, self.p, self.training, self.inplace)

So: their internal operation is the same. Interfaces are different. As for _VF, I guess that's some C/C++ code.