机器学习系列-tensorflow-03-线性回归Linear Regression
利用tensorflow实现数据的线性回归
导入相关库
import tensorflow as tf import numpy import matplotlib.pyplot as plt rng = numpy.random
参数设置
learning_rate = 0.01 training_epochs = 1000 display_step = 50
训练数据
train_x = numpy.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]) train_y = numpy.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]) n_samples = train_x.shape[0]
tf图输入
x = tf.placeholder("float") y = tf.placeholder("float")
设置权重和偏置
w = tf.variable(rng.randn(), name="weight") b = tf.variable(rng.randn(), name="bias")
构建线性模型
pred = tf.add(tf.multiply(x, w), b)
均方误差
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()
开始训练
with tf.session() as sess: sess.run(init) # 适合所有训练数据 for epoch in range(training_epochs): for (x, y) in zip(train_x, train_y): sess.run(optimizer, feed_dict={x: x, y: y}) # 显示每个纪元步骤的日志 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') # 画图显示 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()
结果展示
epoch: 0050 cost= 0.183995649 w= 0.43250677 b= -0.5143978
epoch: 0100 cost= 0.171630666 w= 0.42162812 b= -0.43613702
epoch: 0150 cost= 0.160693780 w= 0.41139638 b= -0.36253116
epoch: 0200 cost= 0.151019916 w= 0.40177315 b= -0.2933027
epoch: 0250 cost= 0.142463341 w= 0.39272234 b= -0.22819161
epoch: 0300 cost= 0.134895071 w= 0.3842099 b= -0.16695316
epoch: 0350 cost= 0.128200993 w= 0.37620357 b= -0.10935676
epoch: 0400 cost= 0.122280121 w= 0.36867347 b= -0.055185713
epoch: 0450 cost= 0.117043234 w= 0.36159125 b= -0.004236537
epoch: 0500 cost= 0.112411365 w= 0.3549302 b= 0.04368245
epoch: 0550 cost= 0.108314596 w= 0.34866524 b= 0.08875148
epoch: 0600 cost= 0.104691163 w= 0.34277305 b= 0.13114017
epoch: 0650 cost= 0.101486407 w= 0.33723122 b= 0.17100765
epoch: 0700 cost= 0.098651998 w= 0.33201888 b= 0.20850417
epoch: 0750 cost= 0.096145160 w= 0.32711673 b= 0.24377018
epoch: 0800 cost= 0.093927994 w= 0.32250607 b= 0.27693948
epoch: 0850 cost= 0.091967128 w= 0.31816947 b= 0.308136
epoch: 0900 cost= 0.090232961 w= 0.31409115 b= 0.33747625
epoch: 0950 cost= 0.088699281 w= 0.31025505 b= 0.36507198
epoch: 1000 cost= 0.087342896 w= 0.30664718 b= 0.39102668
optimization finished!
training cost= 0.087342896 w= 0.30664718 b= 0.39102668
参考:
author: aymeric damien
project: https://github.com/aymericdamien/tensorflow-examples/
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