7da71b22f18f5d02ca57a8e5cb5ae47338ae8a31,qucumber/rbm/purification_rbm.py,PurificationRBM,gamma_plus,#PurificationRBM#,344

Before Change

                  :math:`\langle\sigma|\Gamma^{(+)}|\sigma"\rangle`
        :rtype: torch.Tensor
        
        if len(v.shape) < 2 and len(vp.shape) < 2:
            temp = torch.dot(v + vp, self.visible_bias)
            temp += F.softplus(F.linear(v, self.weights_W, self.hidden_bias)).sum()
            temp += F.softplus(F.linear(vp, self.weights_W, self.hidden_bias)).sum()
        else:
            temp = torch.mv(v, self.visible_bias).unsqueeze_(1) + torch.mv(
                vp, self.visible_bias
            ).unsqueeze_(0)

            temp += (
                F.softplus(F.linear(v, self.weights_W, self.hidden_bias))
                .sum(1)

After Change

                  :math:`\langle\sigma|\Gamma^{(+)}|\sigma"\rangle`
        :rtype: torch.Tensor
        
        if v.dim() < 2 and vp.dim() < 2:
            temp = torch.dot(v + vp, self.visible_bias)
            temp += F.softplus(F.linear(v, self.weights_W, self.hidden_bias)).sum()
            temp += F.softplus(F.linear(vp, self.weights_W, self.hidden_bias)).sum()
        else:
            temp1 = torch.mv(v, self.visible_bias) + (
                F.softplus(F.linear(v, self.weights_W, self.hidden_bias)).sum(1)
            )

            temp2 = torch.mv(vp, self.visible_bias) + (
                F.softplus(F.linear(vp, self.weights_W, self.hidden_bias)).sum(1)
            )

            if expand:
                temp = temp1.unsqueeze_(1) + temp2.unsqueeze_(0)
            else:
                temp = temp1 + temp2

        return 0.5 * temp

    def gamma_minus(self, v, vp, expand=True):

Instances