tensorflow线性回归

一些tensorflow的基本操作，训练模式

官方地址->github

# -*- coding: utf-8 -*-
"""
Created on Fri Jan  5 15:39:35 2018
Linear Regressipn
线性回归
tensorflow
@author: Administrator
"""

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
rng = np.random

#参数定义
learning_rate = 0.01     #学习率
training_epochs = 1000  #循环次数，训练次数
display_step = 50       #每隔50次输出一次结果

#训练的数据集
train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                      7.042,10.791,5.313,7.997,5.654,9.27,3.1])#输入
train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                     2.827,3.465,1.65,2.904,2.42,2.94,1.3])#输出
n_samples = train_X.shape[0]

# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")

#设置模型的权重W 和偏置量 b
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(),name='baise')

#定义一个线性结构
pred = tf.add(tf.multiply(X,W),b)

#损失函数
cost = tf.reduce_sum(tf.pow(pred - Y,2))/(2*n_samples)
#梯度下降
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

#初始化变量
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    
    #训练数据
    for epoch in range(training_epochs):
        for(x,y) in zip(train_X, train_Y):
            sess.run(optimizer, feed_dict = {X:x,Y:y})
        
        if (epoch+1) % display_step == 0:
            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), "W=", sess.run(W), "b=", sess.run(b))

    print("Optimization 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), '\n')

    #Graphic display
    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.show()