tensorflow-非线性回归

#!/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
单样本
"""
import tensorflow as tf
import numpy as np

trainCount=50
g=tf.Graph()
with g.as_default():

with tf.name_scope("variables"):
w=tf.Variable(tf.zeros([2,1]),name="w",dtype=tf.float32)
b=tf.Variable(0.,dtype=tf.float32,name="b")

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")

def inference(x):
result=tf.add(tf.matmul(tf.pow(x,2),w),b)
return result

def loss(x,y):
yp=inference(x)
return tf.reduce_sum(tf.squared_difference(y,yp))

def train(learningRate,trainLoss):
trainOp=tf.train.GradientDescentOptimizer(learningRate).minimize(trainLoss)
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)
b2=np.array(np.random.rand(n),dtype=np.float32)
sampleY=np.matmul(pow(sampleX,2),samplew)+sampleb1+b2
return (sampleX,sampleY)

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)
trainOp=train(0.25,trainLoss)
accuracyOp=accuracy(sampleX,sampleY)
inputX=sampleX
inputY=sampleY
print inputX.shape
print inputY.shape
for trainStep in xrange(trainCount):
if trainStep%5==0:
validate_acc=sess.run(accuracyOp)
print "%d次后=>正确率%g"%(trainStep,validate_acc)
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})
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,)
0次后=>正确率0
5次后=>正确率0.927204
10次后=>正确率0.927204
15次后=>正确率0.927204
20次后=>正确率0.927204
25次后=>正确率0.927204
30次后=>正确率0.927204
35次后=>正确率0.927204
40次后=>正确率0.927204
45次后=>正确率0.927204
w: [[0.4828106 ]
[0.82115054]]
b: 5.412575
测试样本正确率0.956847
单样本训练