#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 23 20:01:26 2017

@author: lyx
"""

import tensorflow as tf
from numpy.random import RandomState

batch_size=6

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')

a=tf.matmul(x,w1)
y=tf.matmul(a,w2)

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)

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)
print (sess.run(w1))
print (sess.run(w2))     
'''
sess=tf.Session()
init_op=tf.initialize_all_variables()
sess.run(init_op)
   
print (sess.run(w1))
print (sess.run(w2))   
sess.close()
'''

STEPS=5000
for i in range(STEPS):
    start=(i*batch_size)%dataset_size
    end=min(start+batch_size,dataset_size)
    
    sess.run(train_step,
            feed_dict={x: X[start:end],y_: Y[start:end]})
    if i%1000==0:
        total_cross_entropy=sess.run(cross_entropy,feed_dict={x: X,y_: Y})
        print("After %d training step(s),cross entropy is %g"%(i,total_cross_entropy))
        
        
print(sess.run(w1))
print(sess.run(w2))        
          
    
    
    
    
    
    
    
    
    
    
    





    
