我有如下代码。如果数据点的数量更改为 30 以上的任何数字（例如 40），那么我会得到 为什么nan值？final_slope , final_intercept

对于 25 个示例，它运行良好。我在 Windows 机器上的 tensorflow 上使用 cpu 版本。

可以通过更改第 4 行的数字来更改数据点的数量 n= 40

import numpy as np
import tensorflow as tf

n= 40
x_data = np.linspace(0,10,n) + np.random.uniform(-1.5,1.5,n)
y_label = np.linspace(0,10,n) + np.random.uniform(-1.5,1.5,n)

import matplotlib.pyplot as plt
#%matplotlib inline
plt.plot(x_data,y_label,'*')

m = tf.Variable(0.39)
b = tf.Variable(0.2)

error = 0

for x,y in zip(x_data,y_label):

    y_hat = m*x + b  #Our predicted value

    error += (y-y_hat)**2 # The cost we want to minimize (we'll need to use an optimization function for the minimization!)


optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
train = optimizer.minimize(error)

init = tf.global_variables_initializer()

with tf.Session() as sess:

    sess.run(init)

    epochs = 1000

    for i in range(epochs):

        sess.run(train)


    # Fetch Back Results
    final_slope , final_intercept = sess.run([m,b])

print (final_slope , final_intercept)