tensorflow（二）利用tensorflow實現線性迴歸

一、隨機生成1000個點，分佈在y=0.1x+0.3直線周圍，並畫出來

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)
    //設置一定範圍的浮動
    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()

二、構造線性迴歸函數

#生成一維的w矩陣，取值爲[-1，1]之間的隨機數
w = tf.Variable(tf.random_uniform([1],-1.0,1.0),name='W')
#生成一維的b矩陣，初始值爲0
b = tf.Variable(tf.zeros([1]),name='b')
y = w*x_data+b

#均方誤差
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))

//顯示擬合後的直線
plt.scatter(x_data,y_data,c='r')
plt.plot(x_data,sess.run(w)*x_data+sess.run(b))
plt.show()

三、部分訓練結果如下：

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

擬合後的直線如圖所示：

結論：最終w趨近於0.1，b趨近於0.3，滿足提前設定的數據分佈