#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 15 10:54:53 2018
@author: myhaspl
@email:[email protected]
糖尿病預測（多層）
csv格式：懷孕次數、葡萄糖、血壓、面板厚度，胰島素,bmi,糖尿病血統函式,年齡,結果
"""

import tensorflow as tf
import os
 
trainCount=10000
inputNodeCount=8
validateCount=50
sampleCount=200
testCount=10
outputNodeCount=
 
1

g=tf.Graph()
with g.as_default():
    
       
    def getWeights(shape,wname):
        weights=tf.Variable(tf.truncated_normal(shape,stddev=0.1),name=wname)
        return weights
 
    def getBias(shape,bname):
        biases=tf.Variable(tf.constant(0.1,shape=shape),name=bname)
        return biases
        
    def
 
 inferenceInput(x):
        layer1=tf.nn.relu(tf.add(tf.matmul(x,w1),b1))    
        result=tf.add(tf.matmul(layer1,w2),b2)
        return result
    
    def inference(x):
        yp=inferenceInput(x)
        return tf.sigmoid(yp)
    
    def loss():
        yp=inferenceInput(x)
        return tf.
 
reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y,logits=yp))
    
    def train(learningRate,trainLoss,trainStep):
        trainOp=tf.train.AdamOptimizer(learningRate).minimize(trainLoss,global_step=trainStep)
        return trainOp
    
    def evaluate(x):
        return tf.cast(inference(x)>0.5,tf.float32)
    
    def accuracy(x,y,count):
        yp=evaluate(x)
        return tf.reduce_mean(tf.cast(tf.equal(yp,y),tf.float32))
    
    def inputFromFile(fileName,skipLines=1):
        #生成檔名佇列
        fileNameQueue=tf.train.string_input_producer([fileName])
        #生成記錄鍵值對
        reader=tf.TextLineReader(skip_header_lines=skipLines)
        key,value=reader.read(fileNameQueue)
        return value

    def getTestData(fileName,skipLines=1,n=10):
        #生成檔名佇列
        testFileNameQueue=tf.train.string_input_producer([fileName])
        #生成記錄鍵值對
        testReader=tf.TextLineReader(skip_header_lines=skipLines)
        testKey,testValue=testReader.read(testFileNameQueue)
        testRecordDefaults=[[1.],[1.],[1.],[1.],[1.],[1.],[1.],[1.],[1.]]
        testDecoded=tf.decode_csv(testValue,record_defaults=testRecordDefaults)
        pregnancies,glucose,bloodPressure,skinThickness,insulin,bmi,diabetespedigreefunction,age,outcome=tf.train.shuffle_batch(testDecoded,batch_size=n,capacity=1000,min_after_dequeue=1)    
        testFeatures=tf.transpose(tf.stack([pregnancies,glucose,bloodPressure,skinThickness,insulin,bmi,diabetespedigreefunction,age]))
        testY=tf.transpose([outcome])
        return (testFeatures,testY)
    
    def getNextBatch(n,values):
        recordDefaults=[[1.],[1.],[1.],[1.],[1.],[1.],[1.],[1.],[1.]]
        decoded=tf.decode_csv(values,record_defaults=recordDefaults)
        pregnancies,glucose,bloodPressure,skinThickness,insulin,bmi,diabetespedigreefunction,age,outcome=tf.train.shuffle_batch(decoded,batch_size=n,capacity=1000,min_after_dequeue=1)    
        features=tf.transpose(tf.stack([pregnancies,glucose,bloodPressure,skinThickness,insulin,bmi,diabetespedigreefunction,age]))
        y=tf.transpose([outcome])
        return (features,y) 
    
    with tf.name_scope("inputSample"): 
        samples=inputFromFile("s3://myhaspl/tf_learn/diabetes.csv",1)
        inputDs=getNextBatch(sampleCount,samples)  
        with tf.name_scope("validateSamples"):
            validateInputs=getNextBatch(validateCount,samples)

   
    with tf.name_scope("testSamples"):  
        testInputs=getTestData("s3://myhaspl/tf_learn/diabetes_test.csv")
       
    with tf.name_scope("inputDatas"):
        x=tf.placeholder(dtype=tf.float32,shape=[None,inputNodeCount],name="input_x")
        y=tf.placeholder(dtype=tf.float32,shape=[None,outputNodeCount],name="input_y")
     
    with tf.name_scope("Variable"):
        w1=getWeights([inputNodeCount,12],"w1")
        b1=getBias((),"b1")       
        w2=getWeights([12,outputNodeCount],"w2")
        b2=getBias((),"b2")       
        trainStep=tf.Variable(0,dtype=tf.int32,name="tcount",trainable=False)      
    
    with tf.name_scope("train"):
        trainLoss=loss()    
        trainOp=train(0.005,trainLoss,trainStep)
        init=tf.global_variables_initializer()         
 
   
with tf.Session(graph=g) as sess:    
    sess.run(init)
    
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    
    while trainStep.eval()<trainCount: 
        
        sampleX,sampleY=sess.run(inputDs)
        sess.run(trainOp,feed_dict={x:sampleX,y:sampleY})
        nowStep=sess.run(trainStep)
        if nowStep%500==0:
            validate_acc=sess.run(accuracy(sampleX,sampleY,sampleCount))
            print "%d次後=>正確率%g"%(nowStep,validate_acc)
        if nowStep>trainCount:
            break

    testInputX,testInputY=sess.run(testInputs)
    print "測試樣本正確率%g"%sess.run(accuracy(testInputX,testInputY,testCount))
    print testInputX,testInputY
    print sess.run(evaluate(testInputX))
    
    coord.request_stop()
    coord.join(threads) 

500次後=>正確率0.67
1000次後=>正確率0.75
1500次後=>正確率0.81
2000次後=>正確率0.75
2500次後=>正確率0.775
3000次後=>正確率0.765
3500次後=>正確率0.84
4000次後=>正確率0.85
4500次後=>正確率0.77
5000次後=>正確率0.78
5500次後=>正確率0.775
6000次後=>正確率0.835
6500次後=>正確率0.84
7000次後=>正確率0.785
7500次後=>正確率0.805
8000次後=>正確率0.765
8500次後=>正確率0.83
9000次後=>正確率0.835
9500次後=>正確率0.78
10000次後=>正確率0.775
測試樣本正確率0.7
[[1.00e+01 1.01e+02 7.60e+01 4.80e+01 1.80e+02 3.29e+01 1.71e-01 6.30e+01]
 [3.00e+00 7.80e+01 5.00e+01 3.20e+01 8.80e+01 3.10e+01 2.48e-01 2.60e+01]
 [2.00e+00 1.22e+02 7.00e+01 2.70e+01 0.00e+00 3.68e+01 3.40e-01 2.70e+01]
 [2.00e+00 8.80e+01 5.80e+01 2.60e+01 1.60e+01 2.84e+01 7.66e-01 2.20e+01]
 [1.00e+01 1.01e+02 7.60e+01 4.80e+01 1.80e+02 3.29e+01 1.71e-01 6.30e+01]
 [2.00e+00 1.22e+02 7.00e+01 2.70e+01 0.00e+00 3.68e+01 3.40e-01 2.70e+01]
 [1.00e+00 8.90e+01 6.60e+01 2.30e+01 9.40e+01 2.81e+01 1.67e-01 2.10e+01]
 [6.00e+00 1.48e+02 7.20e+01 3.50e+01 0.00e+00 3.36e+01 6.27e-01 5.00e+01]
 [1.00e+00 9.30e+01 7.00e+01 3.10e+01 0.00e+00 3.04e+01 3.15e-01 2.30e+01]
 [2.00e+00 1.22e+02 7.00e+01 2.70e+01 0.00e+00 3.68e+01 3.40e-01 2.70e+01]] [[0.]
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[[1.]
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