# -*- coding: utf-8 -*-

"""

Created on Sat May 26 18:42:08 2018

@author: Administrator

"""

import tensorflow as tf

import numpy as np

BATCH_SIZE=8

seed=23455

#基于seed产生随机数

rdm=np.random.RandomState(seed)

#初始化特征值为32个样本*2个特征值

#初始化标签

X=rdm.rand(32,2)

Y_=[[x1+x2+(rdm.rand()/10.0-0.05)] for (x1,x2) in X]

#定义输入，参数和输出和传播过程

x=tf.placeholder(tf.float32,shape=(None,2))

y_=tf.placeholder(tf.float32,shape=(None,1))

w1=tf.Variable(tf.random_normal([2,1],stddev=1,seed=1))

y=tf.matmul(x,w1)

#定义损失函数以及反向传播方法

loss_mse=tf.reduce_mean(tf.square(y_-y))

train_step=tf.train.GradientDescentOptimizer(0.01).minimize(loss_mse)

#会话训练

with tf.Session() as sess:

    init_op=tf.global_variables_initializer()

    sess.run(init_op)

    STEPS=20000

    for i in range(STEPS):

        start=(i*BATCH_SIZE)%32

        end=(i*BATCH_SIZE)%32+BATCH_SIZE

        #每次训练抽取start到end的数据

        sess.run(train_step,feed_dict={x:X[start:end],y_:Y_[start:end]})

        #每500次打印一次参数

        if i%500==0:

            print("在%d次迭代后,参数为"%(i))

            print(sess.run(w1))

    #输出训练后的参数

    print("\n")

    print("FINAL w1 is:",sess.run(w1))