TF之LiR：基于tensorflow实现机器学习之线性回归算法

输出结果

代码设计

# -*- coding: utf-8 -*-

#TF之LiR：基于tensorflow实现机器学习之线性回归算法

import tensorflow as tf

import numpy

import matplotlib.pyplot as plt

rng =numpy.random

#参数设定

learning_rate=0.01

training_epochs=10000

display_step=50        #每隔50次迭代输出一次

#训练数据

train_X=numpy.asarray([……])

train_Y=numpy.asarray([……])

n_samples=train_X.shape[0]

print("train_X：",train_X)

print("train_Y：",train_Y)  

#设置placeholder

X=tf.placeholder("float")

Y=tf.placeholder("float")

#设置模型的权重和偏置,因为是不断更新的所以采用Variable定义

W=tf.Variable(rng.randn(),name="weight")

b=tf.Variable(rng.randn(),name="bias")

#设置线性回归方程LiR：w*x+b

pred=tf.add(tf.multiply(X,W),b)

cost=tf.reduce_sum(tf.pow(pred-Y,2))/(2*n_samples)  #设置cost为均方差即reduce_sum函数

optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #梯度下降，minimize函数默认下自动修正w和b

init=tf.global_variables_initializer() #在session运算时初始化所有变量

#开始训练

with tf.Session() as sess:

   sess.run(init)                        #运行一下初始化的变量

   for epoch in range(training_epochs):  #输入所有训练数据

       for(x,y) in zip(train_X,train_Y):

           sess.run(optimizer,feed_dict={X:x,Y:y})

         

           #打印出每次迭代的log日志，每隔50个打印一次

           if (epoch+1) % display_step ==0:

               c=sess.run(cost,feed_dict={X:train_X,Y:train_Y})

               print("迭代次数Epoch:","%04d" % (epoch+1),"下降值cost=","{:.9f}".format(c),

                     "W=",sess.run(W),"b=",sess.run(b))

   print("Optimizer Finished!")

   training_cost=sess.run(cost,feed_dict={X:train_X,Y:train_Y})

   print("Training cost=",training_cost,"W=",sess.run(W),"b=",sess.run(b))

   #绘图

   plt.rcParams['font.sans-serif']=['SimHei']

   plt.subplot(121)

   plt.plot(train_X, train_Y, 'ro', label='Original data')

   plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')

   plt.legend()

   plt.title("TF之LiR：Original data")

 

 

   #测试样本

   test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])

   test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831,2.92, 3.24, 1.35, 1.03])

   print("Testing... (Mean square loss Comparison)")

   testing_cost = sess.run(tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),

                           feed_dict={X:test_X,Y:test_Y}) # same function as cost above

   print("Testing cost=", testing_cost)

   print("Absolute mean square loss difference:", abs( training_cost - testing_cost))

   #绘图

   plt.subplot(122)

   plt.plot(test_X, test_Y, 'bo', label='Testing data')

   plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')

   plt.legend()

   plt.title("TF之LiR：Testing data")

   plt.show()