Method __init__

intermediate_source/parametrizations.py:335–339

(self, X, p_drop=0.2)

# of a randomized pruning method:
class PruningParametrization(nn.Module):
    def __init__(self, X, p_drop=0.2):
        super().__init__()
        # sample zeros with probability p_drop
        mask = torch.full_like(X, 1.0 - p_drop)
        self.mask = torch.bernoulli(mask)

    def forward(self, X):
        return X * self.mask