琐碎知识点——语法

1.random

random.randint(a,b) [a,b]
np.random.randint(a,b) [a,b)

2.for xxx in xxx if xxx

links.extend(link for link in get_links(html) if re.match(link_regex, link))
# 上面的语句分开写,如下
# 但下面需要使用links.append,如果依然使用links.extend会出现错误的结果
for link in get_links(html):
    if re.match(link_regex, link):
          links.append(link)

3.tf.ones_like（）和tf.zeros_like（）

https://www.w3cschool.cn/tensorflow_python/tensorflow_python-wy5d2fsl.html
给定一个tensor（tensor 参数），该操作返回一个具有和给定tensor相同形状（shape）和相同数据类型（dtype），但是所有的元素都被设置为1或者0的tensor。也可以为返回的tensor指定一个新的数据类型。

import tensorflow as tf
import numpy as np
#tf.convert_to_tensor将python的数据类型转换成TensorFlow可用的tensor数据类型
a = tf.convert_to_tensor(np.random.random([2, 3, 4]), dtype=tf.float32)## 生成一个shape=[2, 3, 4]的tensor，
b = tf.ones_like(a, dtype=tf.float32, name='ones_like')# ones_like
c = tf.zeros_like(a, dtype=tf.float32, name='zeros_like')# zeros_like
print(b)
print('##########################')
print(c)
with tf.Session() as sess:
    b_, c_ = sess.run([b, c])
    print("b's shape: ", b_.shape)
    print("c's shape: ", c_.shape)
    print('##########################')
    print(b_)
    print('##########################')
    print(c_)

运行结果

Tensor("ones_like:0", shape=(2, 3, 4), dtype=float32)
##########################
Tensor("zeros_like:0", shape=(2, 3, 4), dtype=float32)
2020-03-24 17:22:08.210991: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
b's shape:  (2, 3, 4)
c's shape:  (2, 3, 4)
##########################
[[[1. 1. 1. 1.]
  [1. 1. 1. 1.]
  [1. 1. 1. 1.]]

 [[1. 1. 1. 1.]
  [1. 1. 1. 1.]
  [1. 1. 1. 1.]]]
##########################
[[[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]

 [[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]]

Process finished with exit code 0

4.tf.concat（）

按维度拼接

import tensorflow as tf
import numpy as np
a = tf.convert_to_tensor(np.random.randint(1,9,[2, 3, 4]), dtype=tf.float32)
b=tf.convert_to_tensor(np.random.randint(1,9,[2, 3, 4]), dtype=tf.float32)
t1 = [[1, 2, 3], [4, 5, 6]]
t2 = [[7, 8, 9], [10, 11, 12]]
with tf.Session() as sess:
    print(sess.run(tf.concat([t1, t2], 0)))
    print(sess.run(tf.concat([t1, t2], 1)))
    print('张量a')
    print(sess.run(a))
    print('张量b')
    print(sess.run(b))
    print('一维拼接')
    print(sess.run(tf.concat([a, b], 0)))
    print('二维拼接')
    print(sess.run(tf.concat([a, b], 1)))
    print('三维拼接')
    print(sess.run(tf.concat([a, b], 2)))

运行结果

[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
[[ 1  2  3  7  8  9]
 [ 4  5  6 10 11 12]]
张量a
[[[6. 3. 5. 2.]
  [8. 7. 7. 5.]
  [2. 4. 2. 5.]]

 [[3. 7. 2. 4.]
  [6. 5. 7. 6.]
  [3. 8. 8. 4.]]]
张量b
[[[8. 5. 7. 1.]
  [6. 8. 5. 4.]
  [1. 1. 1. 7.]]

 [[3. 3. 6. 1.]
  [8. 2. 5. 1.]
  [6. 8. 6. 6.]]]
一维拼接
[[[6. 3. 5. 2.]
  [8. 7. 7. 5.]
  [2. 4. 2. 5.]]

 [[3. 7. 2. 4.]
  [6. 5. 7. 6.]
  [3. 8. 8. 4.]]

 [[8. 5. 7. 1.]
  [6. 8. 5. 4.]
  [1. 1. 1. 7.]]

 [[3. 3. 6. 1.]
  [8. 2. 5. 1.]
  [6. 8. 6. 6.]]]
二维拼接
[[[6. 3. 5. 2.]
  [8. 7. 7. 5.]
  [2. 4. 2. 5.]
  [8. 5. 7. 1.]
  [6. 8. 5. 4.]
  [1. 1. 1. 7.]]

 [[3. 7. 2. 4.]
  [6. 5. 7. 6.]
  [3. 8. 8. 4.]
  [3. 3. 6. 1.]
  [8. 2. 5. 1.]
  [6. 8. 6. 6.]]]
三维拼接
[[[6. 3. 5. 2. 8. 5. 7. 1.]
  [8. 7. 7. 5. 6. 8. 5. 4.]
  [2. 4. 2. 5. 1. 1. 1. 7.]]

 [[3. 7. 2. 4. 3. 3. 6. 1.]
  [6. 5. 7. 6. 8. 2. 5. 1.]
  [3. 8. 8. 4. 6. 8. 6. 6.]]]

5.tf.gather(params,indices,axis=0 )

从params的axis维根据indices的参数值获取切片

import tensorflow as tf
import numpy as np
print("先测试一维张量")
t = np.random.randint(1, 10, 5)
g1 = tf.gather(t, [2, 1, 4])
sess = tf.Session()
print(t)
print(sess.run(g1))
print("再测试二维张量")
t = np.random.randint(1, 10, [4, 5])
t1 = np.random.randint(0, 4, [2, 3])
g2 = tf.gather(t, t1, axis=0)
g3 = tf.gather(t, t1, axis=1)
print('t为  ')
print(t)
print('t1为  ')
print(t1)
print('g2为  ')
print(sess.run(g2))
print('g3为       ')
print(sess.run(g3))

运行结果

先测试一维张量
[7 8 4 3 8]
[4 8 8]
再测试二维张量
t为  #shape（4,5）
[[7 1 4 9 6]
 [1 9 3 1 9]
 [4 9 1 4 3]
 [6 9 1 9 3]]
t1为   #主要看t1的shape（2,3）
[[1 3 0]
 [1 3 0]]
g2为  #shape（2,3，5）
[[[1 9 3 1 9]
  [6 9 1 9 3]
  [7 1 4 9 6]]

 [[1 9 3 1 9]
  [6 9 1 9 3]
  [7 1 4 9 6]]]
g3为       #shape（4,2,3）
[[[1 9 7]
  [1 9 7]]

 [[9 1 1]
  [9 1 1]]

 [[9 4 4]
  [9 4 4]]

 [[9 9 6]
  [9 9 6]]]

6.tf.tile（）

在一个维度上复制n遍，不复制的维度为1
https://blog.csdn.net/xwd18280820053/article/details/72867818

7.x.get_shape().as_list()

把shape这个元组变成列表
https://blog.csdn.net/abc13526222160/article/details/85135517

8.tf.expand_dims（input, dim, name=None）

维度增加
https://blog.csdn.net/jasonzzj/article/details/60811035

# 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]

# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]

9.tf.add( x,y, name=None)

矩阵相加
https://blog.csdn.net/xc_zhou/article/details/85415202

10.tf.less(x, y,name=None)

返回两个张量各元素比较（x<y）得到的真假值组成的张量
https://blog.csdn.net/stt12345678/article/details/83987675

11.tf.cast()

tf.cast()函数的作用是执行 tensorflow 中张量数据类型转换
https://blog.csdn.net/dcrmg/article/details/79747814

12.tf.reduce_any和all

https://www.w3cschool.cn/tensorflow_python/tensorflow_python-d2y32hgr.html
与numpy相同
np.array.any()是或操作，任意一个元素为True，输出为True。
np.array.all()是与操作，所有元素为True，输出为True。

13.tf.math.top_k 和tf.nn.top_k(input, k, name=None)

返回 input 中每行最大的 k 个数，并且返回它们所在位置的索引。
https://www.w3cschool.cn/tensorflow_python/tensorflow_python-d2y32hgr.html

import tensorflow as tf
import numpy as np
input = tf.constant(np.random.randint(1,9,[3, 4]))
k = 2
output = tf.nn.top_k(input, k)
with tf.Session() as sess:
    print(sess.run(input))
    print(sess.run(output))

运行结果

[[8 4 4 7]
 [4 3 6 7]
 [4 3 4 6]]
TopKV2(values=array([[8, 7],
       [7, 6],
       [6, 4]]), indices=array([[0, 3],
       [3, 2],
       [3, 0]]))

14.tf.batch_gather

批引用
https://blog.csdn.net/zby1001/article/details/86551667

import tensorflow as tf
tensor_a = tf.Variable([[1,2,3],[4,5,6],[7,8,9]])
tensor_b = tf.Variable([[0],[1],[2]],dtype=tf.int32)
tensor_c = tf.Variable([[0],[0],[0]],dtype=tf.int32)
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(tf.batch_gather(tensor_a,tensor_b)))
    print(sess.run(tf.batch_gather(tensor_a,tensor_c)))

运行结果

[[1]
 [5]
 [9]]
[[1]
 [4]
 [7]]