阿里云AI-深度学习糖尿病预测

Posted 2021-01-24

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 15 10:54:53 2018
@author: myhaspl
@email:[email protected]
糖尿病预测（多层）
csv格式：怀孕次数、葡萄糖、血压、皮肤厚度，胰岛素,bmi,糖尿病血统函数,年龄,结果
"""

import tensorflow as tf

trainCount=10000
inputNodeCount=8
validateCount=50
sampleCount=200
testCount=10
outputNodeCount=1
ossPath="oss://myhaspl-ai.oss-cn-beijing-internal.aliyuncs.com/"
localPath="./"
dataPath=ossPath

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):
        #生成文件名队列
        fileNameQueue=tf.train.string_input_producer([fileName])
        #生成记录键值对
        reader=tf.TextLineReader(skip_header_lines=skipLines)
        key,value=reader.read(fileNameQueue)
        return value

    def getTestData(fileName,skipLines=1,n=10):
        #生成文件名队列
        testFileNameQueue=tf.train.string_input_producer([fileName])
        #生成记录键值对
        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(dataPath+"diabetes.csv",1)
        inputDs=getNextBatch(sampleCount,samples)  
        with tf.name_scope("validateSamples"):
            validateInputs=getNextBatch(validateCount,samples)

    with tf.name_scope("testSamples"):  
        testInputs=getTestData(dataPath+"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)
    logStr=""
    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))
            logStr=logStr+"%d次后=>正确率%g"%(nowStep,validate_acc)+"
"
            print ".",
        if nowStep>trainCount:
            break

    testInputX,testInputY=sess.run(testInputs)
    logStr=logStr+"测试样本正确率%g"%sess.run(accuracy(testInputX,testInputY,testCount))
    logStr=logStr+str((testInputX,testInputY))
    logStr=logStr+str(sess.run(evaluate(testInputX)))
    with tf.gfile.GFile(dataPath+"ai_log.txt","wb") as f:
        f.write(logStr)    
    coord.request_stop()
    coord.join(threads) 

500次后=>正确率0.7
1000次后=>正确率0.8
1500次后=>正确率0.83
2000次后=>正确率0.78
2500次后=>正确率0.775
3000次后=>正确率0.76
3500次后=>正确率0.885
4000次后=>正确率0.85
4500次后=>正确率0.785
5000次后=>正确率0.79
5500次后=>正确率0.795
6000次后=>正确率0.87
6500次后=>正确率0.85
7000次后=>正确率0.79
7500次后=>正确率0.805
8000次后=>正确率0.775
8500次后=>正确率0.87
9000次后=>正确率0.84
9500次后=>正确率0.815
10000次后=>正确率0.805
测试样本正确率1(array([[1.00e+00, 8.90e+01, 6.60e+01, 2.30e+01, 9.40e+01, 2.81e+01,
        1.67e-01, 2.10e+01],
       [8.00e+00, 1.83e+02, 6.40e+01, 0.00e+00, 0.00e+00, 2.33e+01,
        6.72e-01, 3.20e+01],
       [1.00e+00, 1.26e+02, 6.00e+01, 0.00e+00, 0.00e+00, 3.01e+01,
        3.49e-01, 4.70e+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],
       [8.00e+00, 1.83e+02, 6.40e+01, 0.00e+00, 0.00e+00, 2.33e+01,
        6.72e-01, 3.20e+01],
       [5.00e+00, 1.16e+02, 7.40e+01, 0.00e+00, 0.00e+00, 2.56e+01,
        2.01e-01, 3.00e+01],
       [8.00e+00, 1.83e+02, 6.40e+01, 0.00e+00, 0.00e+00, 2.33e+01,
        6.72e-01, 3.20e+01],
       [1.00e+00, 8.50e+01, 6.60e+01, 2.90e+01, 0.00e+00, 2.66e+01,
        3.51e-01, 3.10e+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],
       [9.00e+00, 8.90e+01, 6.20e+01, 0.00e+00, 0.00e+00, 2.25e+01,
        1.42e-01, 3.30e+01]], dtype=float32), array([[0.],
       [1.],
       [1.],
       [0.],
       [1.],
       [0.],
       [1.],
       [0.],
       [1.],
       [0.]], dtype=float32))[[0.]
 [1.]
 [1.]
 [0.]
 [1.]
 [0.]
 [1.]
 [0.]
 [1.]
 [0.]]