model.py 文件源码

def forward(self, input):
        torch.randn(self.epsilon_input.size(), out=self.epsilon_input)
        torch.randn(self.epsilon_output.size(), out=self.epsilon_output)

        func = lambda x: torch.sign(x) * torch.sqrt(torch.abs(x))
        eps_in = func(self.epsilon_input)
        eps_out = func(self.epsilon_output)

        bias = self.bias
        if bias is not None:
            bias = bias + self.sigma_bias * Variable(eps_out.t())
        noise_v = Variable(torch.mul(eps_in, eps_out))
        return F.linear(input, self.weight + self.sigma_weight * noise_v, bias)