TensorFlow 模拟线性回归代码样例

    import numpy as np    import tensorflow as tf    import matplotlib.pyplot as plt    #1 构造待模拟线性回归的点    num_points = 10000    vector_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)        vector_set.append([x1, y1])    x_data = [v[0] for v in vector_set]    y_data = [v[1] for v in vector_set]    #2 定义计算框架    ##2.1 定义拟合函数    w = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name="w")    b = tf.Variable(tf.zeros([1]), name="b")    y = w * x_data + b    ##2.2 定义loss函数    loss = tf.reduce_mean(tf.square(y - y_data) * 1.0, name="lose")    ##2.3 定义优化器梯度下降法    optimizer = tf.train.GradientDescentOptimizer(0.5)    ##2.4 定义训练目标    train = optimizer.minimize(loss, name='train')    #3 迭代计算进行训练    init = tf.global_variables_initializer()    with tf.Session() as sess:        sess.run(init)        print("w=", sess.run(w), ", b=", sess.run(b), ", loss=", sess.run(loss))        for _ in range(100):            sess.run(train)            print("w=", sess.run(w), ", b=", sess.run(b), ", loss=", sess.run(loss))        ##这里增加了一个步骤，将拟合出的函数显示到坐标图上        plt.scatter(x_data, y_data, c="r")        plt.plot(x_data, sess.run(w) * x_data + sess.run(b))    plt.show()

以上代码出处为以下课程，本人在完成该课程学习时，在本地环境运行了以上代码并在注释中增加了自己的理解。
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