# -*- coding: utf-8 -*-
import tensorflow as tf
from numpy.random import RandomState

batch_size = 8 #训练数据batch的大小

#定义神经网络的参数
w1 = tf.Variable(tf.random_normal([2,3],stddev = 1,seed = 1))
w2 = tf.Variable(tf.random_normal([3,1],stddev = 1,seed = 1))

x = tf.placeholder(tf.float32,shape=(None,2),name ='x-input')
y_ = tf.placeholder(tf.float32,shape=(None,1),name ='y-input') 

#定义神经网络前向传播的过程
a = tf.matmul(x,w1)
y = tf.matmul(a,w2)

#定义损失函数和反向传播的算法
cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y,1e-10,1.0)))
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

#通过随机数生成一个模拟数据集
rdm = RandomState(1)
dataset_size = 128
X = rdm.rand(dataset_size,2)
Y = [[int(x1+x2 < 1)] for (x1,x2) in X]

with tf.Session() as sess:
    init_op = tf.initialize_all_variables()
    sess.run(init_op)#初始化变量
    print (sess.run(w1))
    print (sess.run(w2))
   
    #设定训练的轮数
    STEPS = 5000
    for i in range(STEPS):
        #每次选取batch_size个样本进行训练
        start = (i * batch_size) % dataset_size
        end = min(start + batch_size,dataset_size)
        
        #通过选取的样本训练神经网络并更新参数
        sess.run(train_step,feed_dict = {x:X[start:end],y_:Y[start:end]})
        if i % 1000 == 0:
            #每隔一段时间计算在所有数据上的交叉熵并输出
            total_cross_entropy = sess.run(
                cross_entropy,feed_dict = {x:X,y_:Y})  
            print("After %d training step(s),cross entropy on all data is %g"
                 %(i,total_cross_entropy))
            
    print (sess.run(w1))
    print (sess.run(w2))
                   
        

