nn.py 文件源码

def forward(self, x, samples):
        mean = self.mean_drop(x)
        mean = self.mean_lin1(mean)
        mean = F.relu(mean)
        mean = self.mean_drop(mean)
        mean = self.mean_lin2(mean)

        variances = self.vars_drop(x)
        variances = self.vars_lin1(variances)
        variances = F.relu(variances)
        variances = self.vars_drop(variances)
        variances = self.vars_lin2(variances)
        variances = F.softplus(variances) * self.softplus_boost

        return True, torch.cat([mean, variances], dim=1) # TODO: Transform mean and variances in the same fashion as in ProposalNormal