最近真的写的都是非常基础的小点，也是希望自己能够好好积累。
在训练中，我们通常会定义各种各样的loss，那么最简单的一个loss就是L2的distance，这个要怎么写呢？
假设我们有两个feature，一个是我们predict出来的，我们假设为pred，另外一个是我们得到的ground_truth, 假设为gt，
pred=np.random.randint(1,5,[2,5,5,3])print("pred",pred.transpose(0,3,1,2))gt=np.random.randint(2,8,[2,5,5,3])print("pred",pred.transpose(0,3,1,2))tf_square=tf.square(pred-gt)tf_reduce_sum=tf.reduce_sum(tf_square,axis=3,keepdims=True)tf_reduce_sum_2=tf.reduce_sum(tf_reduce_sum,axis=[1,2,3])tf_reduce_mean=tf.reduce_mean(tf_reduce_sum_2,axis=0)sess=tf.Session()print("tf_square: ",np.squeeze(sess.run(tf_square)))print("tf_reduce_sum: ",np.squeeze(sess.run(tf_reduce_sum)))print("tf_reduce_sum_2: ",np.squeeze(sess.run(tf_reduce_sum_2)))print("tf_reduce_mean: ",np.squeeze(sess.run(tf_reduce_mean)))# use the tensorflow opres=tf.nn.l2_loss((pred-gt).astype(np.float32))print("tf.nn.l2_loss: ",np.squeeze(sess.run(res)))写的比较复杂了，其实可以写在一行里面，但是看的可能不是很清楚。
下面是展现的结果，其实这些只要自己用个简单的小例子，就能理解的很透彻了
pred [[[[2 1 1 4 1] [3 1 4 2 3] [2 3 2 3 2] [2 2 4 4 3] [3 1 2 4 4]] [[2 2 2 1 3] [4 2 2 2 2] [3 2 4 3 2] [2 2 4 2 3] [3 3 2 3 2]] [[3 2 1 4 2] [3 3 2 3 2] [2 4 4 3 1] [2 3 1 3 1] [3 2 1 4 4]]] [[[4 3 2 1 2] [2 3 1 2 3] [4 4 4 2 2] [3 1 4 4 1] [4 1 1 3 4]] [[2 3 2 4 2] [3 3 2 4 3] [3 1 4 3 1] [3 3 3 4 3] [4 4 2 2 1]] [[3 4 4 2 2] [3 3 2 1 2] [4 2 1 2 1] [1 2 4 2 3] [1 2 1 2 3]]]] pred [[[[2 1 1 4 1] [3 1 4 2 3] [2 3 2 3 2] [2 2 4 4 3] [3 1 2 4 4]] [[2 2 2 1 3] [4 2 2 2 2] [3 2 4 3 2] [2 2 4 2 3] [3 3 2 3 2]] [[3 2 1 4 2] [3 3 2 3 2] [2 4 4 3 1] [2 3 1 3 1] [3 2 1 4 4]]] [[[4 3 2 1 2] [2 3 1 2 3] [4 4 4 2 2] [3 1 4 4 1] [4 1 1 3 4]] [[2 3 2 4 2] [3 3 2 4 3] [3 1 4 3 1] [3 3 3 4 3] [4 4 2 2 1]] [[3 4 4 2 2] [3 3 2 1 2] [4 2 1 2 1] [1 2 4 2 3] [1 2 1 2 3]]]] tf_square: [[[[ 0 4 4] [16 0 0] [ 9 0 36] [ 0 9 4] [ 9 16 9]] [[ 0 4 1] [25 9 1] [ 1 9 4] [25 1 1] [ 9 9 25]] [[25 0 25] [ 9 4 9] [25 1 4] [ 1 1 0] [ 4 25 25]] [[ 9 1 1] [16 25 1] [ 0 0 25] [ 4 16 9] [ 1 4 1]] [[ 1 1 0] [ 4 9 4] [25 25 1] [ 1 1 9] [ 1 1 4]]] [[[ 0 1 0] [ 4 9 4] [16 16 1] [ 9 4 1] [ 9 1 9]] [[25 0 9] [ 4 4 0] [ 9 16 1] [ 0 0 36] [16 16 0]] [[ 0 4 9] [ 1 4 1] [ 0 1 16] [ 9 9 1] [ 0 36 16]] [[ 9 1 25] [16 0 1] [ 4 16 1] [ 0 1 4] [36 0 9]] [[ 4 4 25] [25 4 4] [25 9 25] [16 0 25] [ 4 9 9]]]] tf_reduce_sum: [[[ 8 16 45 13 34] [ 5 35 14 27 43] [50 22 30 2 54] [11 42 25 29 6] [ 2 17 51 11 6]] [[ 1 17 33 14 19] [34 8 26 36 32] [13 6 17 19 52] [35 17 21 5 45] [33 33 59 41 22]]] tf_reduce_sum_2: [598 638] tf_reduce_mean: 618 tf.nn.l2_loss: 618.0可以看到我们写了好多行，使用tf的接口一行就搞定啦！
(⊙v⊙)嗯，今日份小总结，水平有限，能力一般。
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