tensorflow框架学习（二）——Variable变量、Constant常量、Placeholder占位符

import tensorflow as tf
import numpy as np#创建shape&＃61;1*1的变量
v1&＃61;tf.Variable(1)#创建shape&＃61;2*2的变量,并命名
v2&＃61;tf.Variable([[1,2],[3,4]],name&＃61;&＃39;v2&＃39;)#用numpy数组创建变量&＃xff0c;设置数据类型为tf.float32
v3&＃61;tf.Variable(np.arange(0,9).reshape(3,3),dtype&＃61;tf.float32)#用tensoflow的随机tensor创建变量
v4&＃61;tf.Variable(tf.random_normal(shape&＃61;[3,3],mean&＃61;0,stddev&＃61;1))#用另一个变量的初始值*2来创建&＃xff0c;这里别调用的变量传入要初始化
v5 &＃61; tf.Variable(v4.initialized_value()*2, name&＃61;"w_twice")#创建初始化节点
init&＃61;tf.global_variables_initializer()#创建Saver保存数据
saver &＃61; tf.train.Saver()# #保存单个变量,用字典的形式传入数据{&＃39;储存名&＃39;:&＃39;变量名&＃39;},对变量v2进行保存
# saver &＃61; tf.train.Saver({&＃39;save_v2&＃39;:&＃39;v2&＃39;})

with tf.Session() as sess:sess.run(init)print(&＃39;v1:\n&＃39;,sess.run(v1))print(&＃39;v2:\n&＃39;, sess.run(v2))print(&＃39;v3:\n&＃39;, sess.run(v3))print(&＃39;v4:\n&＃39;, sess.run(v4))print(&＃39;v5:\n&＃39;, sess.run(v5))

import tensorflow as tf
import numpy as npconst&＃61;tf.constant(np.arange(1,2),dtype&＃61;tf.float32)with tf.Session() as sess:print(sess.run(const))

import tensorflow as tf
import numpy as np
import random#创建两个2维占位符[None,1]表示不确定行数1列的数据
p&＃61;tf.placeholder(tf.float32,[None,1])#创建10*1的随机数组
arr&＃61;np.random.randn(10,1)#让两个占位符进行加法运算
add_&＃61;tf.add(p,1)#创建会话
with tf.Session() as sess:for i in range(3):#传入i&＃43;1行1列的数据p_in&＃61;arr[:i&＃43;1]print(&＃39;传入的数据为:\n&＃39;,p_in)#执行加法运算&＃xff0c;由于有placeholder&＃xff0c;所以要对feed_dict传入字典{占位符:传入数据}print(&＃39;执行加法结果为&＃xff1a;\n&＃39;,sess.run(add_,feed_dict&＃61;{p:p_in}))