inception网络中的resnet和concat，在最后的连接处的"累加"，处理是否相同?

昨天被问到如下问题，这下应该记得住了，记录如下：
inception网络中的resnet和concat，在最后的连接处的"累加"，处理是否相同?
如图红色部分：

 

下来看了一下代码，部分如下：
# Inception-Resnet-C
def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
    """Builds the 8x8 resnet block."""
    with tf.variable_scope(scope, 'Block8', [net], reuse=reuse):
        with tf.variable_scope('Branch_0'):
            tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
        with tf.variable_scope('Branch_1'):
            tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1')
            tower_conv1_1 = slim.conv2d(tower_conv1_0, 192, [1, 3],
                                        scope='Conv2d_0b_1x3')
            tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [3, 1],
                                        scope='Conv2d_0c_3x1')
        mixed = tf.concat([tower_conv, tower_conv1_2], 3)
        up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
                         activation_fn=None, scope='Conv2d_1x1')
        net += scale * up
        if activation_fn:
            net = activation_fn(net)
    return net
 
def reduction_a(net, k, l, m, n):
    with tf.variable_scope('Branch_0'):
        tower_conv = slim.conv2d(net, n, 3, stride=2, padding='VALID',
                                 scope='Conv2d_1a_3x3')
    with tf.variable_scope('Branch_1'):
        tower_conv1_0 = slim.conv2d(net, k, 1, scope='Conv2d_0a_1x1')
        tower_conv1_1 = slim.conv2d(tower_conv1_0, l, 3,
                                    scope='Conv2d_0b_3x3')
        tower_conv1_2 = slim.conv2d(tower_conv1_1, m, 3,
                                    stride=2, padding='VALID',
                                    scope='Conv2d_1a_3x3')
    with tf.variable_scope('Branch_2'):
        tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID',
                                     scope='MaxPool_1a_3x3')
     net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
    return net
   

concat在最后一维加大网络(最后一维的数据量增大)，+则直接把内容加和

找到以前的笔记：

"Inception所使用的结合方式 是 concatenate合并 成一个更大的向量的方式，而 ResNet 的结合方式是 sum。 两个结合方式各有优点。
concatenate当需要用不同的维度去 组合成新观念的时候更有益。 而 sum则更适用于 并存的判断。"

来自推荐过的文章：

https://zhuanlan.zhihu.com/p/27642620

温故一下+和tf.concat的效果对比吧：
import tensorflow as tf
t1=tf.constant([[1,1,1],[2,2,2]],dtype=tf.int32)
t2=tf.constant([[3,3,3],[4,4,4]],dtype=tf.int32)
t3=t1+t2
t4=tf.concat([t1, t2], 0)
t5=tf.concat([t1, t2], 1)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for temp in [t1,t2,t3,t4,t5]:
        print ('\n',sess.run(temp))

以下为结果       
 [[1 1 1]
 [2 2 2]]

 [[3 3 3]
 [4 4 4]]

 [[4 4 4]
 [6 6 6]]

 [[1 1 1]
 [2 2 2]
 [3 3 3]
 [4 4 4]]

 [[1 1 1 3 3 3]
 [2 2 2 4 4 4]]