model.py 文件源码

def __call__(self, x1, x2):
        xp = self.xp
        out_size = self.out_size
        batch_size, len1, dim1 = x1.shape
        if not self.nobias[0]:
            x1 = F.concat((x1, xp.ones((batch_size, len1, 1),
                                       dtype=xp.float32)), axis=2)
            dim1 += 1
        len2, dim2 = x2.shape[1:]
        if not self.nobias[1]:
            x2 = F.concat((x2, xp.ones((batch_size, len2, 1),
                                       dtype=xp.float32)), axis=2)
            dim2 += 1
        x1_reshaped = F.reshape(x1, (batch_size * len1, dim1))
        W_reshaped = F.reshape(F.transpose(self.W, (0, 2, 1)),
                               (dim1, out_size * dim2))
        affine = F.reshape(F.matmul(x1_reshaped, W_reshaped),
                           (batch_size, len1 * out_size, dim2))
        biaffine = F.transpose(
            F.reshape(batch_matmul(affine, x2, transb=True),
                      (batch_size, len1, out_size, len2)),
            (0, 1, 3, 2))
        if not self.nobias[2]:
            biaffine += F.broadcast_to(self.b, biaffine.shape)
        return biaffine