TebsorFlow基本语法

import tensorflow as tf

# 定义常量
a = tf.constant(10)
b = tf.constant(20)

# 定义一种操作，建立一种关系
c = tf.add(a, b)

# 通过会话真正执行代码操作
with tf.Session() as sess:
    ret = sess.run(c)
    print(ret)

# 定义变量
c = tf.Variable([2])
d = tf.Variable([3])

e = tf.add(c, d)

# 获取已经默认的图，一个程序默认是一个图--之前设置好的关系图
g = tf.get_default_graph()
print(g)

# 初始化变量，才能使用
init = tf.global_variables_initializer()

# 这里才是真正的执行
with tf.Session() as sess:

    # 执行定义的变量
   sess.run(init)
    ret = sess.run(e)
    print(ret)

# placeholder 占位符
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
add = tf.add(input1, input2)

# op操作嵌套
input4 = tf.multiply(input1, add)

with tf.Session() as sess:

    # feed_dict 以字典形式给占位变量赋值
    result = sess.run(input4, feed_dict={input1:[3], input2:[4]})
    print(result)

weight = tf.Variable([[2.0, 3.0, 4.0]])

# 设置w2的形状和weight一样  并同等赋值 如果不执行还是变量可更改
w2 = tf.Variable(weight.initialized_value())
w3 = tf.Variable(weight.initialized_value() * 0.2)

# assign(一个可变的tensor, 相同类型的tensor)  赋值操作
# 简单说：将w3的值 赋值给 同形状的w2
update = tf.assign(w2, w3)

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)

    # 查看w2是变量 
    print(w2)

    w3 = sess.run(w3)
    print(w3)

    # 执行操作
    sess.run(update)

    w2 = sess.run(w2)
    print(w2)