tensorflow实现非线性拟合

 import tensorflow as tf import numpy as np  x_data = np.random.rand(100) y_data = x_data*0.6 + 0.8  # 定义变量 k = tf.Variable(tf.zeros([1, 1])) b = tf.Variable(tf.zeros([1, 1])) y = k * x_data + b  # 定义二次代价函数 loss = tf.reduce_mean(tf.square(y - y_data)) # 定义梯度下降优化器 optimizer = tf.train.GradientDescentOptimizer(0.2) # 最小化训练 train_step = optimizer.minimize(loss)  with tf.Session() as sess:     sess.run(tf.global_variables_initializer())     for i in range(201):         sess.run(train_step)         if i % 10 == 0:             print("step: "+str(i))      print(sess.run([k, b])) 

运行结果图如下：

文章来源: https://blog.csdn.net/any1234567890/article/details/92635591