实验目的：
方程：y = Wx + b
通过大量的（x, y）坐标值，模型可以计算出接近W和b的值
实验步骤：
第一步：生成线程回归方程模型所需要的数据
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, 0.50) # 正态分布 0.0:均值，0.50：标准差 y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03) vectors_set.append([x1, y1]) # 生成一些样本数据 x_data = [v[0] for v in vectors_set] y_data = [v[1] for v in vectors_set]
# print(x_data) 会有1000个数据
plt.scatter(x_data, y_data, c='r') plt.show()
第二步：建立线性回归模型，将生成的数据（x_data，y_data）喂给模型，并产生结果。
# 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') # 记过计算得出预估值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() # 初始化sess init = tf.global_variables_initializer() sess.run(init) # 初始化的W和b是多少 print("W = ", sess.run(W), "b = ", sess.run(b), "loss = ", sess.run(loss)) # 执行20次训练 for step in range(20): sess.run(train) # 输出寻来你好的W和b print("W = ", sess.run(W), "b = ", sess.run(b), "loss = ", sess.run(loss))
结果：
W = [0.6756458] b = [0.] loss = 0.16456214 W = [0.53388023] b = [0.2748111] loss = 0.050819542 W = [0.4182969] b = [0.28113642] loss = 0.027618099 W = [0.33377516] b = [0.28629357] loss = 0.015202045 W = [0.2719439] b = [0.2900648] loss = 0.0085575525 W = [0.22671175] b = [0.29282364] loss = 0.00500173 W = [0.19362253] b = [0.29484183] loss = 0.0030988192 W = [0.16941638] b = [0.2963182] loss = 0.0020804694 W = [0.15170857] b = [0.29739827] loss = 0.0015354961 W = [0.13875458] b = [0.29818836] loss = 0.0012438523 W = [0.12927818] b = [0.29876634] loss = 0.0010877779 W = [0.12234581] b = [0.29918915] loss = 0.0010042539 W = [0.11727448] b = [0.29949847] loss = 0.0009595558 W = [0.11356459] b = [0.29972476] loss = 0.0009356354 W = [0.11085065] b = [0.29989028] loss = 0.00092283427 W = [0.10886529] b = [0.30001137] loss = 0.0009159839 W = [0.10741292] b = [0.30009997] loss = 0.0009123176 W = [0.10635044] b = [0.30016476] loss = 0.00091035594 W = [0.1055732] b = [0.30021217] loss = 0.0009093059 W = [0.10500462] b = [0.30024683] loss = 0.00090874406 W = [0.10458867] b = [0.30027223] loss = 0.00090844336
我们可以看到W不断趋近于0.1，b不断趋近于0.3，loss不断变小。
说明模型是可用的。
