convolution_rbm.py 文件源码

def reconstruct(self, v):
        """

        :param v: Variable Matrix(batch_size, in_channels, image_height, image_width)
        :return: reconstructed_v, Variable Matrix(batch_size, in_channels, image_height, image_width)
        """
        batch_size = v.data.shape[0]
        xp = cuda.get_array_module(v.data)
        if self.real == 0:
            h = F.sigmoid(self.conv(v))
        else:
            std_ch = xp.reshape(self.std, (1, self.in_channels, 1, 1))
            h = F.sigmoid(self.conv(v / std_ch))
        # F.sigmoid(F.matmul(v, self.l.W, transb=True) + F.broadcast_to(self.l.b, (batch_size, self.n_hidden)))
        W_flipped = F.swapaxes(CF.flip(self.conv.W, axes=(2, 3)), axis1=0, axis2=1)
        reconstructed_v = F.sigmoid(F.convolution_2d(h, W_flipped, self.conv.a, pad=self.ksize-1))
            # = F.sigmoid(F.matmul(h, self.l.W) + F.broadcast_to(self.l.a, (batch_size, self.n_visible)))
        return reconstructed_v