#!/usr/bin/pyton

'''

A simple example(linear regression) to show the complete struct that how to run a tensorflow

create_data -> create_tensorflow_struct->start session
create date: 2017/10/20

'''

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 begin##

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)

#when define variables, initialize must be called

#init = tf.initialize_all_variables()

### create tensorflow structure end ###

sess = tf.Session()

#note: initialize_local_variables no more support in new version

if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:

    init = tf.initialize_all_variables()

else:

    init = tf.global_variables_initializer()

sess.run(init)

for step in range(201):

    sess.run(train)

    if step % 20 == 0:

        #session controls all opertions and varilables

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

sess.close()

#!/usr/bin/python

'''

A example to show how to call session

create date: 2017/10/20

'''

import tensorflow as tf 

#1. 定义一个操作

m1 = tf.constant([[2, 2]])

m2 = tf.constant([[3],

                    [3]])

dot_opeartion = tf.matmul(m1, m2)

#2. 调用session实现

# 图画好以后，需要通过session来控制执行，让图来运行

# 另外每一个图中的操作都需要通过session来控制

# print result

#method1 use session

sess = tf.Session()

result = sess.run(dot_opeartion)

print(result)

sess.close()

#method2 use session

with tf.Session() as sess:

    result_ = sess.run(dot_opeartion)

    print(result_) 

##output
[[12]]
[[12]]

#!/usr/bin/python

'''

A example to show how to call placehoder(类似于占位符)

create date: 2017/10/20

'''

import tensorflow as tf 

#1. 声明placehoder:待传入值

x1 = tf.placeholder(dtype=tf.float32, shape=None)

y1 = tf.placeholder(dtype=tf.float32, shape=None)

z1 = x1 + y1

x2 = tf.placeholder(dtype=tf.float32, shape=None)

y2 = tf.placeholder(dtype=tf.float32, shape=None)

z2 = tf.matmul(x2, y2)

#2. 调用session，传入值

with tf.Session() as sess:

    #when only one operation to run

    #feed_dict: input the values into placeholder

    z1_value = sess.run(z1, feed_dict={x1: 1, y1:2})

    # when run multiple operaions

    #run the two opeartions together

    z1_value, z2_value = sess.run(

        [z1, z2],

        feed_dict={

            x1:1, y1:2,

            x2:[[2],[2]], y2:[[3,3]]

        }

    )

    print(z1_value)

    print(z2_value)

#!/usr/bin/python

'''

A example to show how to call variables

create date: 2017/10/20

'''

import tensorflow as tf 

# 1.stuct

#our first variable in the "global_variable" set

var = tf.Variable(0)

add_operation = tf.add(var,1)

#把add_operation值给var

update_operation = tf.assign(var, add_operation)

# once define variables, you have to initialize them by doing this

init = tf.global_variables_initializer()

# 2. call session

with tf.Session() as sess:

    sess.run(init)

    for count in range(3):

        sess.run(update_operation)

        print(sess.run(var))