model.py 文件源码

def forward(self, input):
        torch.randn(self.epsilon_weight.size(), out=self.epsilon_weight)
        bias = self.bias
        if bias is not None:
            torch.randn(self.epsilon_bias.size(), out=self.epsilon_bias)
            bias = bias + self.sigma_bias * Variable(self.epsilon_bias)
        return F.linear(input, self.weight + self.sigma_weight * Variable(self.epsilon_weight), bias)