标签：ida scope min std tde 单样本 lob diff dom
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 15 10:54:53 2018
@author: myhaspl
@email:myhaspl@myhaspl.com
非线性回归y=a*x^3+b*x^3+c
单样本
"""
import tensorflow as tf
import numpy as np
trainCount=350
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 inference(x):
result=tf.add(tf.matmul(tf.pow(x,3),w),b)
return result
def loss(x,y):
yp=inference(x)
return tf.multiply(tf.reduce_sum(tf.squared_difference(y,yp)),0.5)
def train(learningRate,trainLoss,trainStep):
trainOp=tf.train.GradientDescentOptimizer(learningRate).minimize(trainLoss,global_step=trainStep)
return trainOp
def evaluate(x):
return inference(x)
def accuracy(x,y):
yp=inference(x)
return tf.subtract(1.0,tf.reduce_mean(tf.divide(tf.abs(yp-y),y)))
def inputs(n):
sampleX=np.array(np.random.rand(n,2),dtype=np.float32)
sampleb1=5.
samplew=np.array([0.5,0.9],dtype=np.float32)
sampleY=np.matmul(pow(sampleX,3),samplew)+sampleb1
return (sampleX,sampleY)
with tf.name_scope("variables"):
w=getWeights([2,1],"w")
b=getBias((),"b")
trainStep=tf.Variable(0,dtype=tf.int32,name="step")
with tf.name_scope("inputDatas"):
x=tf.placeholder(dtype=tf.float32,shape=[None,2],name="input_x")
y=tf.placeholder(dtype=tf.float32,shape=[None],name="input_y")
init=tf.global_variables_initializer()
with tf.Session(graph=g) as sess:
sess.run(init)
sampleX,sampleY=inputs(100)
sampleCount=sampleX.shape[0]
testX,testY=inputs(5)
testCount=testX.shape[0]
trainLoss=loss(x,y)
accuracyOp=accuracy(sampleX,sampleY)
inputX=sampleX
inputY=sampleY
print inputX.shape
print inputY.shape
trainOp=train(0.25,trainLoss,trainStep)
while trainStep.eval()<trainCount:
for i in xrange(sampleCount):
inputX=np.array([sampleX[i]],dtype=np.float32)
inputY=np.array([sampleY[i]],dtype=np.float32)
sess.run(trainOp,feed_dict={x:inputX,y:inputY})
nowStep=sess.run(trainStep)
if nowStep%50==0:
validate_acc=sess.run(accuracyOp)
print "%d次后=>正确率%g"%(nowStep,validate_acc)
if nowStep>trainCount:
break
print "w:",sess.run(w)
print "b:",sess.run(b)
print "测试样本正确率%g"%sess.run(accuracy(testX,testY))(100, 2)
(100,)
50次后=>正确率0.941076
100次后=>正确率0.942413
150次后=>正确率0.943086
200次后=>正确率0.943109
250次后=>正确率0.943165
300次后=>正确率0.943153
350次后=>正确率0.943156
w: [[0.5005716]
[0.8993188]]
b: 5.000005
测试样本正确率0.950526
标签：ida scope min std tde 单样本 lob diff dom
原文地址：http://blog.51cto.com/13959448/2321784
