第二周：tensorflow学习与应用

本节内容：

• 创建图，启动图
• 变量
• Fetch and Feed
• tensorflow简单示例

 

 

 

 

import tensorflow as tf
​
m1 = tf.constant([[3,3]])
m2 = tf.constant([[2],[3]])
product = tf.matmul(m1,m2)
print(product)

Tensor("MatMul_1:0", shape=(1, 1), dtype=int32)

sess = tf.Session()
result = sess.run(product)
print(result)
sess.close()

#[[15]]

with tf.Session() as sess:
    result = sess.run(product)
    print(result)

#[[15]]

 

 

import tensorflow as tf


x = tf.Variable([1,2])
a = tf.constant([3,3])
sub = tf.subtract(x,a)
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))



#[-2 -1]
#[-1  1]

 

 

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:
    print(sess.run(init))
    print(sess.run(state))
    for _ in range(5):
        sess.run(update)
        print(sess.run(state))
    

###


None
0
1
2
3
4
5
###

 

Fetch:在会话里面同时执行多个op,得到运行的结果

import tensorflow as tf

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:
    print(sess.run([mul,add]))


#[21.0, 7.0]

 

 

 Feed：便于为操作赋值

import tensorflow as tf
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
out = tf.multiply(input1,input2)
with tf.Session() as sess:
    print(sess.run(out,feed_dict={input1:[2.0],input2:[8.0]}))



#[16.]

 

 

 

import tensorflow as tf
import numpy as np

x_data = np.random.rand(100)
y_data = x_data*0.1 + 0.2

b = tf.Variable(0.)
k = tf.Variable(0.)
y = x_data*k + b

loss = tf.reduce_mean(tf.square(y_data-y))
optimizer = tf.train.GradientDescentOptimizer(0.2)#0.2的学习率
train = optimizer.minimize(loss)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for i in range(201):
        sess.run(train)
        if i%20 == 0:
            print(i,sess.run([k,b]))
            
###

0 [0.046911635, 0.097727574]
20 [0.09816256, 0.20087942]
40 [0.0989946, 0.20048131]
60 [0.09944985, 0.20026337]
80 [0.09969897, 0.20014411]
100 [0.099835284, 0.20007886]
120 [0.09990986, 0.20004314]
140 [0.09995068, 0.2000236]
160 [0.099973015, 0.20001292]
180 [0.09998523, 0.20000707]
200 [0.09999191, 0.20000388]
###