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  • tensorflow学习笔记3

    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))

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  • 原文地址:https://www.cnblogs.com/xrj-/p/14455994.html
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