tensorflow学习2

import tensorflow as tf

#创建一个常量OP
m1 = tf.constant([[3,3]])
#创建一个常量OP
m2 = tf.constant([[2],[3]])
#创建一个矩阵乘法op,把m1和m2传入
product = tf.matmul(m1,m2)
print(product)

#定义一个会话，启动默认图
sess = tf.Session()
result = sess.run(product)
print(result)
sess.close()

#用with的方式启动
with tf.Session() as sess:
    result = sess.run(product)
    print(result)


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

init = tf.global_variables_initializer()

with tf.Session() as sess:
   sess.run(init)#实例化变量
   print(sess.run(sub))
   print(sess.run(add))



state = tf.Variable(0,name="counter")
new_value = tf.add(state,1)
update = tf.assign(state,new_value)
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 and feed

import tensorflow as tf
#Fetch
input1 = tf.constant(3.0)
input2 = tf.constant(2.0)
input3 = tf.constant(5.0)

add = tf.add(input2,input3)
mul = tf.multiply(input1,add)
with tf.Session() as sess:
   result = sess.run([mul,add])
   print(result)

#feed
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
output = tf.multiply(input1,input2)

with tf.Session() as sess:
    print(sess.run(output, feed_dict={input1:[7.],input2:[2.]}))







#tensorflow exp
import tensorflow as tf
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

#构建一个线性模型
b = tf.Variable(0.)
k = tf.Variable(0.)
y = k*x_data + b

#二次代价函数
loss = tf.reduce_mean(tf.square(y_data - y))
optimizer = tf.train.GradientDescentOptimizer(0.2)
train = optimizer.minimize(loss)
#实例化变量
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]))