阿裏雲AI-深度學習糖尿病預測
阿新 • • 發佈：2018-12-04
iyu dde std 鍵值 鍵值對 bat ext 2.3 cor
#!/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、皮膚厚度，胰島素,bmi,糖尿病血統函數,年齡,結果
"""
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):
#生成文件名隊列
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):
#生成文件名隊列
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(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次後=>正確率%g"%(nowStep,validate_acc)+"\n"
print ".",
if nowStep>trainCount:
break
testInputX,testInputY=sess.run(testInputs)
logStr=logStr+"測試樣本正確率%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次後=>正確率0.7 1000次後=>正確率0.8 1500次後=>正確率0.83 2000次後=>正確率0.78 2500次後=>正確率0.775 3000次後=>正確率0.76 3500次後=>正確率0.885 4000次後=>正確率0.85 4500次後=>正確率0.785 5000次後=>正確率0.79 5500次後=>正確率0.795 6000次後=>正確率0.87 6500次後=>正確率0.85 7000次後=>正確率0.79 7500次後=>正確率0.805 8000次後=>正確率0.775 8500次後=>正確率0.87 9000次後=>正確率0.84 9500次後=>正確率0.815 10000次後=>正確率0.805 測試樣本正確率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.]]
阿裏雲AI-深度學習糖尿病預測