models.py 文件源码

def __call__(self, x):
        minibatch_size = x.shape[0]
        activation = F.reshape(self.t(x), (-1, self.n_kernels, self.kernel_dim))
        activation_ex = F.expand_dims(activation, 3)
        activation_ex_t = F.expand_dims(F.transpose(activation, (1, 2, 0)), 0)
        activation_ex, activation_ex_t = F.broadcast(activation_ex, activation_ex_t)
        diff = activation_ex - activation_ex_t

        xp = chainer.cuda.get_array_module(x.data)
        eps = F.expand_dims(xp.eye(minibatch_size, dtype=xp.float32), 1)
        eps = F.broadcast_to(eps, (minibatch_size, self.n_kernels, minibatch_size))
        sum_diff = F.sum(abs(diff), axis=2)
        sum_diff = F.broadcast_to(sum_diff, eps.shape)
        abs_diff = sum_diff + eps

        minibatch_features = F.sum(F.exp(-abs_diff), 2)
        return F.concat((x, minibatch_features), axis=1)