tensorflow-線性回歸
阿新 • • 發佈：2019-02-08
multiply odin feed 線性回歸 zip utf res vpd ace
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Feb 3 20:28:26 2019
@author: myhaspl
sample:
Area,Value,Room,Living,School,Year,Floor
60,135,2,1,1,1944,6
87,450,3,1,1,1952,4
87,450,3,1,1,1952,4
87,500,3,1,1,1952,4
...
"""
import pandas as pd
import numpy as np
import tensorflow as tf
import pylab as plt
trainData=pd.read_csv("bjhouse.csv")
train=trainData[:1000]
trainX=train[‘Area‘].values.reshape(-1,1)
trainY=train[‘Value‘].values.reshape(-1,1)
sampleCount=trainX.shape[0]
learning_rate=0.8
trainingEpochs=500
dispCount=10
sx=tf.placeholder(tf.float32)
sy=tf.placeholder(tf.float32)
w=tf.Variable(np.random.randn(),name="w",dtype=tf.float32)
b=tf.Variable(np.random.randn(),name="b",dtype=tf.float32)
y_=tf.multiply(sx,w)+b
cost=tf.reduce_sum(tf.pow(y_-sy,2)/(2*sampleCount))
trainStep=tf.train.AdamOptimizer(learning_rate).minimize(cost)
init=tf.global_variables_initializer()
sess=tf.Session()
sess.run(init)
for epoch in range(trainingEpochs):
trainSample=zip(trainX,trainY)
for (x,y) in trainSample:
sess.run(trainStep,feed_dict={sx:x,sy:y})
if (epoch+1)% dispCount ==0:
trainCost=sess.run(cost,feed_dict={sx:trainX,sy:trainY})
print("epoch{},cost:{:.04f}".format(epoch+1,trainCost))
print("train finished")
print("w:{},b:{},cost:{}".format(sess.run(w),sess.run(b),sess.run(cost,feed_dict={sx:trainX,sy:trainY})))
plt.rcParams[‘font.sans-serif‘] = [‘SimHei‘] # 替換sans-serif字體
plt.rcParams[‘axes.unicode_minus‘] = False # 解決坐標軸負數的負號顯示問題
plt.plot(trainX,trainY,"go",label="原數據")
plt.plot(trainX,trainX*sess.run(w),label="擬合數據")
plt.legend()
plt.show()
epoch10,cost:3925.0693 epoch20,cost:3926.9109 epoch30,cost:3927.0454 epoch40,cost:3927.0542 epoch50,cost:3927.0552 epoch60,cost:3927.0554 epoch70,cost:3927.0554 epoch80,cost:3927.0554 epoch90,cost:3927.0554 epoch100,cost:3927.0554 epoch110,cost:3927.0554 epoch120,cost:3927.0554 epoch130,cost:3927.0554 epoch140,cost:3927.0554 epoch150,cost:3927.0554 epoch160,cost:3927.0554 epoch170,cost:3927.0554 epoch180,cost:3927.0554 epoch190,cost:3927.0554 epoch200,cost:3927.0554 epoch210,cost:3927.0554 epoch220,cost:3927.0554 epoch230,cost:3927.0554 epoch240,cost:3927.0554 epoch250,cost:3927.0554 epoch260,cost:3927.0554 epoch270,cost:3927.0554 epoch280,cost:3927.0554 epoch290,cost:3927.0554 epoch300,cost:3927.0554 epoch310,cost:3927.0554 epoch320,cost:3927.0554 epoch330,cost:3927.0554 epoch340,cost:3927.0554 epoch350,cost:3927.0554 epoch360,cost:3927.0554 epoch370,cost:3927.0554 epoch380,cost:3927.0554 epoch390,cost:3927.0554 epoch400,cost:3927.0554 epoch410,cost:3927.0554 epoch420,cost:3927.0554 epoch430,cost:3927.0554 epoch440,cost:3927.0554 epoch450,cost:3927.0554 epoch460,cost:3927.0554 epoch470,cost:3927.0554 epoch480,cost:3927.0554 epoch490,cost:3927.0554 epoch500,cost:3927.0554 train finished w:4.52463436126709,b:21.11363410949707,cost:3927.055419921875
tensorflow-線性回歸