tensorflow1--会话，变量，fetch and feed,线性回归示例

import tensorflow as tf
import numpy as np
# 1-1 初识tensorflow会话
###########################
#   with定义会话
# a = tf.constant([[1,2]])
# b = tf.constant([[3],[5]])
# c = tf.multiply(a,b)
# d = tf.matmul(a,b)
# with tf.Session() as sess:
#     a1 = sess.run(a)
#     b1 = sess.run(b)
#     d1 = sess.run(d)
#     print(a1)
#     print(b1)
#     print(d1)
#   常规定义会话
# sess = tf.Session()
# a1 = sess.run(a)
# b1 = sess.run(b)
# c1 = sess.run(c)
# d1 = sess.run(d)
# print(a1)
# print(b1)
# print(c1)
# print(d1)
# sess.close()

# 2-2 变量 (必须初始化)
###########################
# a = tf.Variable([1,2])
# b = tf.constant([5,8])
# c = tf.subtract(a,b)        #减法
# d = tf.add(a,b)             #加法
# #变量自加1，赋值
# e = tf.Variable(5)
# newe = tf.add(e,1)
# update = tf.assign(e,newe)
#
# init = tf.global_variables_initializer()        #有变量必须初始化全局变量
# with tf.Session() as sess:
#     sess.run(init)
#     # print(sess.run(a))
#     # print(sess.run(b))
#     e1 = sess.run(e)
#     for i in range(5):
#         sess.run(update)
#         print(sess.run(e))

# 2-3   Fetch and Feed
########################
#   Fetch
# a = tf.constant(1)
# b = tf.constant(2)
# c = tf.add(a,b)
# d = tf.multiply(b,c)
# with tf.Session() as sess:
#     result = sess.run([d,c])
#     print(result)

#   Feed
# a = tf.placeholder(tf.float32)
# b = tf.placeholder(tf.float32)
# output = tf.multiply(a,b)
# with tf.Session() as sess:
#     result = sess.run(output,feed_dict={a:25,b:5})
#     print(result)

# 2-4 简单实例
######################
#模拟训练数据集
x_data = np.random.rand(100)
y_data = 5 * x_data + 3
#训练参数
k = tf.Variable(0.)
b = tf.Variable(0.)
y = k * x_data + b
#代价函数
loss = tf.reduce_mean(tf.square(y - y_data))
#梯度下降
optimizer = tf.train.GradientDescentOptimizer(0.3)
#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 i in range(200):
        sess.run(train)
        if i % 20 == 0:
            print(i,sess.run([k,b]))