TensorFlow 官网API

tf.summary.scalar

tf.summary.FileWriter

tf.summary.histogram

tf.summary.merge_all

tf.equal

tf.argmax

tf.cast

tf.div(x, y, name=None)

tf.pow(x, y, name=None)

tf.unstack(value, num=None, axis=0, name='unstack')

tf.stack(values, axis=0, name='stack')

tf.transpose(a, perm=None, name='transpose')

tf.set_random_seed(seed)

tf.reshape(tensor, shape, name=None)

tf.multiply(x, y, name=None

tf.name_scope(args, *kwds)

tf.variable_scope(args, *kwds)

tf.nn.dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None)

tf.nn.softmax_cross_entropy_with_logits(_sentinel=None, labels=None, logits=None, dim=-1, name=None)

tf.nn.moments(x, axes, shift=None, name=None, keep_dims=False)

tf.contrib.legacy_seq2seq.sequence_loss_by_example(logits, targets, weights, average_across_timesteps=True, softmax_loss_function=None, name=None)

tf.gradients

apply_gradients

tf.distributions.Normal

tf.summary.scalar

TensorFlow 官网API

https://www.tensorflow.org/api_docs/python/tf/summary/scalar

tf.summary.FileWriter:

TensorFlow 官网API

https://www.tensorflow.org/api_docs/python/tf/summary/FileWriter

tf.summary.histogram

TensorFlow 官网API

https://www.tensorflow.org/api_docs/python/tf/summary/histogram

tf.summary.merge_all

TensorFlow 官网API

https://www.tensorflow.org/api_docs/python/tf/summary/merge_all

tf.equal：

https://www.tensorflow.org/api_docs/python/tf/equal

tf.argmax：

TensorFlow 官网API

https://www.tensorflow.org/api_docs/python/tf/argmax

tf.cast

TensorFlow 官网API

https://www.tensorflow.org/api_docs/python/tf/cast

tf.div(x, y, name=None)

参考链接：https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/div

tf.pow(x, y, name=None)

TensorFlow 官网API

参考链接：https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/pow

tf.unstack(value, num=None, axis=0, name='unstack')

https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/unstack

tf.stack(values, axis=0, name='stack')

https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/stack

 1 ###tf.stack()/unstack():
 2 import tensorflow as tf
 3 
 4 a = tf.constant([1, 2, 3])
 5 b = tf.constant([4, 5, 6])
 6 c = tf.stack([a, b], axis=0)
 7 d = tf.stack([a, b], axis=1)
 8 e = tf.unstack(c, axis=0)
 9 f = tf.unstack(c, axis=1)
10 with tf.Session() as sess:
11     print(sess.run(c))
12     print(sess.run(d))
13     print(sess.run(e))
14     print(sess.run(f))

[[1 2 3]
 [4 5 6]]
[[1 4]
 [2 5]
 [3 6]]

[array([1, 2, 3], dtype=int32), 
array([4, 5, 6], dtype=int32)]

[array([1, 4], dtype=int32), 
array([2, 5], dtype=int32), 
array([3, 6], dtype=int32)]

参考链接：https://blog.csdn.net/u012193416/article/details/77411535

1 ###tf.stack()/unstack():
2 import tensorflow as tf
3 
4 g = tf.constant([[[1, 2, 3, 4],[5, 6, 7, 8],[9, 10, 11, 12]],[[13, 14, 15, 16],[17, 18, 19, 20],[21, 22, 23, 24]]])
5 h = tf.unstack(g)
6 with tf.Session() as sess:
7     print(sess.run(h))

[array([[ 1,  2,  3,  4],
       [ 5,  6,  7,  8],
       [ 9, 10, 11, 12]], dtype=int32), 
array([[13, 14, 15, 16],
       [17, 18, 19, 20],
       [21, 22, 23, 24]], dtype=int32)]

tf.transpose(a, perm=None, name='transpose')

官方链接：https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/transpose

 1 ###tf.transpose()
 2 import tensorflow as tf
 3 
 4 a = tf.constant([[1, 2, 3],
 5                  [4, 5, 6]])
 6 b = tf.constant([[[1, 2, 3, 4],[5, 6, 7, 8],[9, 10, 11, 12]],[[13, 14, 15, 16],[17, 18, 19, 20],[21, 22, 23, 24]]])
 7 c = tf.transpose(a, [0, 1])
 8 d = tf.transpose(a, [1, 0])
 9 e = tf.transpose(b, [0, 1, 2])
10 f = tf.transpose(b, [1, 0, 2])
11 g = tf.transpose(b, [0, 2, 1])
12 with tf.Session() as sess:
13     print(sess.run(c))
14     print(sess.run(d))
15     print(sess.run(e))
16     print(sess.run(f))
17     print(sess.run(g))

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

 [[13 14 15 16]
  [17 18 19 20]
  [21 22 23 24]]]
[[[ 1  2  3  4]
  [13 14 15 16]]

 [[ 5  6  7  8]
  [17 18 19 20]]

 [[ 9 10 11 12]
  [21 22 23 24]]]
[[[ 1  5  9]
  [ 2  6 10]
  [ 3  7 11]
  [ 4  8 12]]

 [[13 17 21]
  [14 18 22]
  [15 19 23]
  [16 20 24]]]

博客链接：https://www.cnblogs.com/studyDetail/p/6533316.html

tf.set_random_seed(seed)

实例运行参见: Jupyter notebook:TensorFlowAPI

https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/set_random_seed

tf.reshape(tensor, shape, name=None)

Args:

tensor: A Tensor.
shape: A Tensor. Must be one of the following types: int32, int64. Defines the shape of the output tensor.
name: A name for the operation (optional).

mport tensorflow as tf

t = tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9])
m = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
n =  tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18])

with tf.Session() as sess:
    print('t->[3, 3]:nn', sess.run(tf.reshape(t, [3,3 ])), 'n')
    
    print('m->[2, 4]:nn', sess.run(tf.reshape(m, [2,4 ])), 'n')
    
    print('n->[3, 2, 3]:nn', sess.run(tf.reshape(n, [3, 2, 3 ])), 'n')
    
    print('n->[2, -1]:nn', sess.run(tf.reshape(n, [2, -1])), 'n')
    
    print('n->[-1, 9]:nn', sess.run(tf.reshape(n, [-1, 9])), 'n')
    
    print('n->[2, -1, 3]:nn', sess.run(tf.reshape(n, [2, -1, 3])), 'n')

t->[3, 3]:

 [[1 2 3]
 [4 5 6]
 [7 8 9]] 

m->[2, 4]:

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

n->[3, 2, 3]:

 [[[ 1  2  3]
  [ 4  5  6]]

 [[ 7  8  9]
  [10 11 12]]

 [[13 14 15]
  [16 17 18]]] 

n->[2, -1]:

 [[ 1  2  3  4  5  6  7  8  9]
 [10 11 12 13 14 15 16 17 18]] 

n->[-1, 9]:

 [[ 1  2  3  4  5  6  7  8  9]
 [10 11 12 13 14 15 16 17 18]] 

n->[2, -1, 3]:

 [[[ 1  2  3]
  [ 4  5  6]
  [ 7  8  9]]

 [[10 11 12]
  [13 14 15]
  [16 17 18]]]

https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/reshape

tf.multiply(x, y, name=None

import tensorflow as tf    
  
#两个矩阵相乘  
x=tf.constant([[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]])    
y=tf.constant([[0,0,1.0],[0,0,1.0],[0,0,1.0]])  
#注意这里这里x,y要有相同的数据类型，不然就会因为数据类型不匹配而出错  
z=tf.multiply(x,y)  
  
#两个数相乘  
x1=tf.constant(1)  
y1=tf.constant(2)  
#注意这里这里x1,y1要有相同的数据类型，不然就会因为数据类型不匹配而出错  
z1=tf.multiply(x1,y1)  
  
#数和矩阵相乘  
x2=tf.constant([[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]])  
y2=tf.constant(2.0)  
#注意这里这里x1,y1要有相同的数据类型，不然就会因为数据类型不匹配而出错  
z2=tf.multiply(x2,y2)  
  
with tf.Session() as sess:    
    print(sess.run(z))  
    print(sess.run(z1))  
    print(sess.run(z2))

[[0. 0. 3.]
 [0. 0. 3.]
 [0. 0. 3.]]
2
[[2. 4. 6.]
 [2. 4. 6.]
 [2. 4. 6.]]

https://blog.csdn.net/m0_37041325/article/details/77036513

https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/multiply　

tf.name_scope(args, *kwds)

Returns a context manager for use when defining a Python op.

https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/name_scope

tf.variable_scope(args, *kwds)

Returns a context manager for defining ops that creates variables (layers).

This context manager validates that the (optional) values are from the same graph, ensures that graph is the default graph, and pushes a name scope and a variable scope.

https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/variable_scope　　　

class tf.contrib.rnn.BasicLSTMCell

TensorFlow 官网API