tensorflow-sigmoid預測

#!/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 """ import tensorflow as tf import os trainCount=10000 inputNodeCount=8 validateCount=20 sampleCount=100 testCount=7 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): result=tf.add(tf.matmul(x,w1),b1) 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.AdadeltaOptimizer(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 inputs(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 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("inputSamples"): samples=inputs(os.getcwd()+"/diabetes.csv",1) inputX,inputY=getNextBatch(sampleCount,samples) with tf.name_scope("validateSamples"): validateInputX,validateInputY=getNextBatch(validateCount,samples) with tf.name_scope("testSamples"): testSamples=inputs(os.getcwd()+"/diabetes_test.csv",1) testInputX,testInputY=getNextBatch(testCount,testSamples) 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=tf.Variable(tf.truncated_normal([inputNodeCount,1],name="w1",stddev=0.1)) b1=tf.Variable(0.1,dtype=tf.float32,name="b1") trainStep=tf.Variable(0,dtype=tf.int32,name="tcount",trainable=False) with tf.Session(graph=g) as sess: trainLoss=loss() accuracyOp=accuracy(validateInputX,validateInputY,validateCount) trainOp=train(0.025,trainLoss,trainStep) init=tf.global_variables_initializer() sess.run(init) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) while trainStep.eval()<trainCount: sampleX=sess.run(inputX) sampleY=sess.run(inputY) sess.run(trainOp,feed_dict={x:sampleX,y:sampleY}) nowStep=sess.run(trainStep) if nowStep%500==0: validate_acc=sess.run(accuracyOp) print "%d次後=>正確率%g"%(nowStep,validate_acc) if nowStep>trainCount: break print "測試樣本正確率%g"%sess.run(accuracy(testInputX,testInputY,testCount)) print sess.run(inputY) print sess.run(evaluate(inputX)) coord.request_stop() coord.join(threads)