TensorFlow 1

分类： 1，protocol Buffer 处理结构化数据工具；（xml，json）

　　　2，Bazel 自动化构建工具，编译；

tensor 张量；张量就是多维数组；

flow 流；

两个阶段：1 定义计算图中所有的计算；

　　　　 2，执行计算；

张量tensor：

　　1，多维数组；

　　2，零阶张量表示标量（scalar），就是一个数；

　　3，一阶张量表示为向量（vector），是一维数组；

　　。。。。。

　　第n阶 --------------------------------n 维数组；

一个张量主要保存三个属性； name 名字、 shape 维度、 type 类型；

张量的使用：

　　用途分类：1，对中间计算结果的引用；

　　　　　　2，当计算图构造完成之后，张量可以用来获得计算结果，也就是得到真实数字；

1, 练习

[root@shenzhen ~]# cat /server/tensorflow/tensor1.py

#!/usr/local/bin/python3

#coding:utf-8

import tensorflow as tf

import numpy as np

#create data

x_data = np.random.rand(100).astype(np.float32)

y_data = x_data*0.1 + 0.3

###create tensorflow structure start###

Weights = tf.Variable(tf.random_uniform([1],-1.0,1.0))

biases = tf.Variable(tf.zeros([1]))

y = Weights*x_data + biases

loss = tf.reduce_mean(tf.square(y - y_data))

optimizer = tf.train.GradientDescentOptimizer(0.5)

train = optimizer.minimize(loss)

#init = tf.initialize_all_variables()

init = tf.global_variables_initializer()

###create tensorflow structure start###

sess = tf.Session()

sess.run(init)  #very important

for step in range(201):

    sess.run(train)

    if step % 20 == 0:

        print(step, sess.run(Weights), sess.run(biases))

[root@shenzhen tensorflow]# python3 tensor1.py

2018-08-20 20:58:14.585672: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

0 [0.5822645] [0.00799593]

20 [0.27060172] [0.19762385]

40 [0.16020724] [0.26387033]

60 [0.12124781] [0.28724945]

80 [0.10749861] [0.2955002]

100 [0.10264634] [0.29841197]

120 [0.10093395] [0.29943955]

140 [0.10032961] [0.2998022]

160 [0.10011631] [0.2999302]

180 [0.10004105] [0.2999754]

200 [0.10001447] [0.29999134]

2，Session 会话：运行模型

[root@shenzhen tensorflow]# python3 tensor2.py

2018-08-21 20:02:22.863676: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

[[12]]

[root@shenzhen tensorflow]# vim tensor2.py

[root@shenzhen tensorflow]# python3 tensor2.py

2018-08-21 20:05:11.457492: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

[[12]]

[root@shenzhen tensorflow]# cat tensor2.py

#!/usr/local/bin/python3

#coding:utf-8

import tensorflow as tf

matrix1 = tf.constant([[3,3]])

matrix2 = tf.constant([[2],

                       [2]])

product = tf.matmul(matrix1,matrix2) #matrix multiply np.dot(m1,m2)

#method1

#sess = tf.Session()

#result = sess.run(product)

#print(result)

#sess.close()

#method2

with tf.Session() as sess:

    result2 = sess.run(product)

    print(result2)

3， tensorflow 变量

创建变量；

[root@shenzhen tensorflow]# python3 tensor3.py

2018-08-21 20:48:45.724496: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

1

2

3

[root@shenzhen tensorflow]# cat tensor3.py

#!/usr/local/bin/python3

#coding:utf-8

import tensorflow as tf

state = tf.Variable(0, name='counter')

#print(state.name)

one = tf.constant(1)

new_value = tf.add(state, one)

update = tf.assign(state, new_value)

init = tf.global_variables_initializer() #must have if define variable

with tf.Session() as sess:

    sess.run(init)

    for _ in range(3):

        sess.run(update)

        print(sess.run(state))

tensorflow 传入值：

[root@shenzhen tensorflow]# python3 tensor4.py

2018-08-22 19:39:44.495986: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

[21.]

[root@shenzhen tensorflow]# cat tensor4.py

#!/usr/local/bin/python3

#coding:utf-8

import tensorflow as tf

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:[3.]}))

tensorflow1

框架tensorflow1

TensorFlow 1