构造线性回归模型2
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() #生成一维的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') #生成一维的b矩阵，初始值是0 b = tf.Variable(tf.zeros([1]),name='b') #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') #梯度下降优化参数 optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.5) #训练的过程就是最小化loss train = optimizer.minimize(loss,name='train') sess = tf.compat.v1.Session() init = tf.compat.v1.global_variables_initializer() sess.run(init) #执行20次训练 for step in range(20): sess.run(train) print("W=",sess.run(W),"b=",sess.run(b),"loss=",sess.run(loss))
