本文实例为大家分享了tensorflow实现线性回归的具体代码,供大家参考,具体内容如下
一、随机生成1000个点,分布在y=0.1x+0.3直线周围,并画出来
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import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
num_points = 1000
vectors_set = []
for i in range (num_points):
x1 = np.random.normal( 0.0 , 0.55 )
/ / 设置一定范围的浮动
y1 = x1 * 0.1 + 0.3 + np.random.normal( 0.0 , 0.03 )
vectors_set.append([x1,y1])
x_data = [v[ 0 ] for v in vectors_set]
y_data = [v[ 1 ] for v in vectors_set]
plt.scatter(x_data,y_data,c = 'r' )
plt.show()
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二、构造线性回归函数
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#生成一维的w矩阵,取值为[-1,1]之间的随机数
w = tf.Variable(tf.random_uniform([ 1 ], - 1.0 , 1.0 ),name = 'W' )
#生成一维的b矩阵,初始值为0
b = tf.Variable(tf.zeros([ 1 ]),name = 'b' )
y = w * x_data + b
#均方误差
loss = tf.reduce_mean(tf.square(y - y_data),name = 'loss' )
#梯度下降
optimizer = tf.train.GradientDescentOptimizer( 0.5 )
#最小化loss
train = optimizer.minimize(loss,name = 'train' )
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
#print("W",sess.run(w),"b=",sess.run(b),"loss=",sess.run(loss))
for step in range ( 20 ):
sess.run(train)
print ( "W=" ,sess.run(w), "b=" ,sess.run(b), "loss=" ,sess.run(loss))
/ / 显示拟合后的直线
plt.scatter(x_data,y_data,c = 'r' )
plt.plot(x_data,sess.run(w) * x_data + sess.run(b))
plt.show()
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三、部分训练结果如下:
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W = [ 0.10559751 ] b = [ 0.29925063 ] loss = 0.000887708
W = [ 0.10417549 ] b = [ 0.29926425 ] loss = 0.000884275
W = [ 0.10318361 ] b = [ 0.29927373 ] loss = 0.000882605
W = [ 0.10249177 ] b = [ 0.29928035 ] loss = 0.000881792
W = [ 0.10200921 ] b = [ 0.29928496 ] loss = 0.000881397
W = [ 0.10167261 ] b = [ 0.29928818 ] loss = 0.000881205
W = [ 0.10143784 ] b = [ 0.29929042 ] loss = 0.000881111
W = [ 0.10127408 ] b = [ 0.29929197 ] loss = 0.000881066
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拟合后的直线如图所示:
结论:最终w趋近于0.1,b趋近于0.3,满足提前设定的数据分布
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持服务器之家。
原文链接:https://blog.csdn.net/Missayaaa/article/details/80053060