有没有SKnet模块的keras实现？

ddef SKConv(input, M, r, L=32, stride=1, is_training=True):
    input_feature = input.get_shape().as_list()[3]
    d = max(int(input_feature / r), L)
    net = input
    with slim.arg_scope([slim.conv2d, slim.fully_connected], activation_fn=tf.nn.relu):
        for i in range(M):
            net = slim.conv2d(net, input_feature, [3+i*2, 3+i*2], rate=1+i, stride=stride)
            net = slim.batch_norm(net, decay=0.9, center=True, scale=True, epsilon=1e-5,
                                  updates_collections=tf.GraphKeys.UPDATE_OPS, is_training=is_training)
            net = tf.nn.relu(net)
            if i == 0:
                fea_U = net
            else:
                fea_U = tf.add(fea_U, net)
        gap = tflearn.global_avg_pool(fea_U)
        fc  = slim.fully_connected(gap, d, activation_fn=None)
        fcs = fc
        for _ in range(M):
            fcs = slim.fully_connected(fcs, input_feature, activation_fn=None)
            if _ == 0:
                att_vec = fcs
            else:
                att_vec = tf.add(att_vec, fcs)
        att_vec = tf.expand_dims(att_vec, axis=1)
        att_vec = tf.expand_dims(att_vec, axis=1)
        att_vec_softmax = tf.nn.softmax(att_vec)
        fea_v = tf.multiply(fea_U, att_vec_softmax)
    return fea_v

这是tensroflow版本的，在keras上运行会报错“'tuple' object has no attribute 'layer'”，有没有人可以改写一下或者有其他keras实现的版本？

keras本身底层也依赖 tf 哦
看看下面的
https://github.com/yuto3o/SKNet-Keras