本文共 1472 字，大约阅读时间需要 4 分钟。

主要是得注意输入的数据格式和placeholder的用法，当然了，还有weights和bias的初始化

import tensorflow as tfimport numpy as npimport matplotlib.pyplot as pltimport osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'x = np.linspace(-10, 10, 50)[:, np.newaxis]noise = np.random.normal(0, 0.5, x.shape)y=x*0.5+noise  #加上一个0~1内的随机数。用来模拟噪声w=tf.Variable(tf.random_normal([1,1]))b=tf.Variable(tf.zeros([1,1]))#定义权重x_input=tf.placeholder(tf.float32, [None, 1])y_output=tf.placeholder(tf.float32, [None, 1])# y_predict=w*x+b### plt.figure()# plt.scatter(x,y,c='green',s=50,marker='o')#散点图# plt.plot(x, y, color="red")   #画一条直线# plt.show()#5个样本作为一个batchdef get_next_batch():    train_indices=np.random.choice(range(len(x)),50)    train_x=x[train_indices]    train_y=y[train_indices]    return train_x,train_ydef output():    y_predict=tf.add(tf.matmul(x_input,w),b)    return y_predictsess=tf.Session()y_predict=output()loss=(tf.reduce_mean(tf.reduce_sum(tf.square(y_output-y_predict))))/50train=tf.train.GradientDescentOptimizer(0.005).minimize(loss)init=tf.global_variables_initializer()sess.run(init)print(sess.run(w))print('_______')for i in range(100):  #500次迭代    train_x,train_y=get_next_batch()    sess.run(train,feed_dict={x_input:train_x,y_output:train_y})    if i%30==0:        result_w=sess.run(w)        result_b=sess.run(b)        y_predict=result_w[0][0]*x+result_b        plt.figure()        plt.scatter(x,y,c='green',s=50,marker='o')#散点图        plt.plot(x, y_predict, color="red")   #画一条直线        plt.show()

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