tensorflow的基础

import tensorflow as tf
#创建一个常量po
m1 = tf.constant([[3,3]])
m2 = tf.constant([[2],[3]])
product = tf.matmul(m1,m2)
print(product)
#定义一个会话，启动默认图
sess = tf.Session()
#调用sess的run方法
#run(product)触发了图中的三个op
result = sess.run(product)
print(result)
sess.close()
with tf.Session() as sess:
    #调用sess的run方法
#run(product)触发了图中的三个op
    result = sess.run(product)
    print(result)

x = tf.Variable([1,2])
a = tf.constant([3,3])
#增加一个减法
sub = tf.subtract(x,a)
#增加一个加法
add = tf.add(x,sub)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    print(sess.run(sub))
    print(sess.run(add))

x = tf.Variable([1,2])
a = tf.constant([3,3])
#增加一个减法
sub = tf.subtract(x,a)
#增加一个加法
add = tf.add(x,sub)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    print(sess.run(sub))
    print(sess.run(add))

#创建一个变量初始化为0
state = tf.Variable(0,name='counter')
#创建一个op，作用是使state加1
new_value = tf.add(state,1)
#赋值op
update = tf.assign(state,new_value)
#变量初始化
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    print(sess.run(state))
    for _ in range(5):
        sess.run(update)
        print(sess.run(state))

#Fetch
input1 = tf.constant(3.0)
input2 = tf.constant(2.0)
input3 = tf.constant(5.0)
#加法
add = tf.add(input2,input3)
#乘法
mul = tf.multiply(input1,add)

with tf.Session() as sess:
    result = sess.run([mul,add])
    print(result)

#Feed
#创建占位符
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
output = tf.multiply(input1,input2)

with tf.Session() as sess:
    #feed的数据以字典的形式传入
    print(sess.run(output,feed_dict={input1:[7.],input2:[2.]}))

#一个案例
import numpy as np
#使用numpy生成100个随机点
x_data = np.random.rand(100)
y_data = x_data*0.1+0.2
#构造一个线性模型
b = tf.Variable(1.1)
k = tf.Variable(0.5)
y = k*x_data+b
#二次代价函数
loss = tf.reduce_mean(tf.square(y_data-y))#误差的平方求平均值
#定义一个梯度下降法来进行训练的优化器
optimizer = tf.train.GradientDescentOptimizer(0.2)#0.2是学习率
#最小化代价函数
train = optimizer.minimize(loss)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for step in range(201):
        sess.run(train)
        if step%20==0:
            print(step,sess.run([k,b]))#k接近0.1，b接近0.2
#因为进入step的循环后 但打印之前 有sess.run(train),这一步k和b变化了.
#在for循环之前打印k和b,都是0
#可以直接调用封装好的模型 最小化代价函数