8、数据统计

 1 #向量的范数，默认为2范数，平方和开根号
 2 a = tf.ones([2,2])
 3 print(tf.norm(a))  # tf.Tensor(2.0, shape=(), dtype=float32)
 4 
 5 #自己实现
 6 a1 = tf.sqrt(tf.reduce_sum(tf.square(a)))
 7 print(a1) # tf.Tensor(2.0, shape=(), dtype=float32)
 8 
 9 b = tf.ones([4,28,28,3])
10 b1 = tf.norm(b)
11 print(b1) #tf.Tensor(96.99484, shape=(), dtype=float32)
12 
13 #自己实现
14 b2 = tf.sqrt(tf.reduce_sum(tf.square(b)))
15 print(b2) #tf.Tensor(96.99484, shape=(), dtype=float32)

 1 # L1  norm
 2 b = tf.ones([2,2])
 3 n1 = tf.norm(b)
 4 print(n1) #tf.Tensor(2.0, shape=(), dtype=float32)
 5 
 6 n2 = tf.norm(b,ord=2,axis=1) #压缩列，求每一行的2范数
 7 print(n2) # tf.Tensor([1.4142135 1.4142135], shape=(2,), dtype=float32)
 8 
 9 n3 = tf.norm(b,ord=1) #1范数，默认求所有的值
10 print(n3) # tf.Tensor(4.0, shape=(), dtype=float32)
11 
12 n3 = tf.norm(b,ord=1,axis=0) #压缩行，求每一列的1范数
13 print(n3) # tf.Tensor([2. 2.], shape=(2,), dtype=float32)
14 
15 n4 = tf.norm(b,ord=1,axis=1) #压缩列，求每一行的1范数
16 print(n4) # tf.Tensor([2. 2.], shape=(2,), dtype=float32)

 1 #reduce_min/max/mean  加一个reduce是因为在求最值的过程中会出现维度的减少
 2 a = tf.random.normal([4,10]) #0-1分布的数据
 3 
 4 # ①默认是所有维度的统计
 5 max_a = tf.reduce_max(a)
 6 min_a = tf.reduce_min(a)
 7 mean_a = tf.reduce_mean(a)
 8 print(max_a,"\n",min_a,"\n",mean_a)
 9 
10 # ②指定维度
11 max_a = tf.reduce_max(a,axis=1)
12 min_a = tf.reduce_min(a,axis=1)
13 mean_a = tf.reduce_mean(a,axis=1)
14 print(max_a,"\n",min_a,"\n",mean_a)

tf.Tensor(1.866617, shape=(), dtype=float32) 
 tf.Tensor(-2.5952716, shape=(), dtype=float32) 
 tf.Tensor(-0.25601098, shape=(), dtype=float32)

tf.Tensor([ 1.866617    1.0276536   1.6409966  -0.11667389], shape=(4,), dtype=float32) 
 tf.Tensor([-2.5952716  -2.0198066  -0.93489224 -1.7569251 ], shape=(4,), dtype=float32) 
 tf.Tensor([-0.43045864 -0.26896027  0.43675566 -0.76138073], shape=(4,), dtype=float32)

a = tf.random.normal([4,10]) #0-1分布的数据
print(a)

b = tf.argmax(a) #默认是求得每一列的最大值的索引
print(b)

tf.Tensor(
[[-0.7919774  -2.972948   -3.1243117   1.2346513  -0.652347    0.35998186
   1.4628253  -0.12529439  0.18237525 -0.29699537]
 [-1.2740427  -0.13558723  0.5396547  -0.46394014  0.27174982  0.06232562
  -0.73767865  1.03348    -0.36170274  1.170251  ]
 [-0.1372166   0.3829013  -0.42844287  0.7059885   0.78422594 -0.11698396
   0.08661779 -0.22002225 -1.4231229   2.1049361 ]
 [ 0.19104291 -1.4988437   0.47450095 -0.04199754 -0.14426158  0.6865424
   0.85595787  1.442973   -1.9023395   1.2549413 ]], shape=(4, 10), dtype=float32)

tf.Tensor([3 2 1 0 2 3 0 3 0 2], shape=(10,), dtype=int64)