layers.py 文件源码

def forward(self, X):
        X = super(ProbabilisticDense, self).forward(X)
        sigma_prior = math.exp(-3)
        W_eps = Variable(torch.zeros(self.input_dim, self.output_dim))
        W_eps = torch.normal(W_eps, std=sigma_prior)
        self.W = W = self.W_mu + torch.log1p(torch.exp(self.W_rho)) * W_eps
        b_eps = Variable(torch.zeros(self.output_dim))
        b_eps = torch.normal(b_eps, std=sigma_prior)
        self.b = b = self.b_mu + torch.log1p(torch.exp(self.b_rho)) * b_eps
        XW = X @ W
        return XW + b.expand_as(XW)