tensorflow(2)——线性回归

学习《Tensorflow入门教程》记录 

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

# 随机生成1000个点，围绕在y=0.1x+0.3的直线周围
num_points = 1000
vectors_set = []
for i in range(num_points):
    x1 = np.random.normal(0.0, 0.55)
    y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)
    vectors_set.append([x1, y1])

# 生成一些样本
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()

结果如下所示： 

 对上面的数据集进行训练：

# 生成1维的W矩阵，取值是[-1,1]之间的随机数
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W')
# 生成1维的b矩阵，初始值是0
b = tf.Variable(tf.zeros([1]), name='b')
# 经过计算得出预估值y
y = W * x_data + b

# 以预估值y和实际值y_data之间的均方误差作为损失
loss = tf.reduce_mean(tf.square(y - y_data), name='loss')
# 采用梯度下降法来优化参数
optimizer = tf.train.GradientDescentOptimizer(0.5)
# 训练的过程就是最小化这个误差值
train = optimizer.minimize(loss, name='train')

sess = tf.Session()

init = tf.global_variables_initializer()
sess.run(init)

# 初始化的W和b是多少
print ("W =", sess.run(W), "b =", "seess.run(b),s =", sess.run(loss))
# 执行20次训练
for step in range(20):
    sess.run(train)
    # 输出训练好的W和b
    print ("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss))

训练的结果为： 
W = [-0.68253565] b = seess.run(b),s = 0.28643712
W = [-0.44002083] b = [0.32005146] loss = 0.08808484
W = [-0.2786493] b = [0.31371173] loss = 0.04372422
W = [-0.16553827] b = [0.30949324] loss = 0.021932669
W = [-0.08626061] b = [0.30653635] loss = 0.011227837
W = [-0.03069621] b = [0.3044639] loss = 0.005969218
W = [0.00824796] b = [0.30301136] loss = 0.003385987
W = [0.03554329] b = [0.3019933] loss = 0.0021170068
W = [0.05467413] b = [0.30127975] loss = 0.0014936357
W = [0.06808263] b = [0.30077967] loss = 0.0011874122
W = [0.07748042] b = [0.30042914] loss = 0.0010369839
W = [0.08406717] b = [0.30018348] loss = 0.0009630878
W = [0.08868372] b = [0.30001128] loss = 0.00092678727
W = [0.09191938] b = [0.2998906] loss = 0.000908955
W = [0.09418721] b = [0.299806] loss = 0.0009001951
W = [0.09577668] b = [0.29974672] loss = 0.00089589204
W = [0.09689073] b = [0.29970518] loss = 0.00089377817
W = [0.09767154] b = [0.29967606] loss = 0.0008927398
W = [0.0982188] b = [0.29965565] loss = 0.0008922296
W = [0.09860236] b = [0.29964134] loss = 0.000891979
W = [0.09887119] b = [0.2996313] loss = 0.00089185586

由结果可知，损失越来越小，W和b越来越逼近实际值。