python之Tensorflow 上的多元回归

我是 StackOverflow 的新手，所以如果我说了一些愚蠢的话，请原谅我。我在 tensorflow 库中编写这个多元线性回归，但由于某种原因它不起作用，损失只是增加然后变成无。

# coding: utf-8 
 
import numpy as np 
import tensorflow as tf 
import matplotlib.pyplot as plt 
import seaborn as sns 
get_ipython().magic('matplotlib inline') 
 
from sklearn.datasets import load_boston     
 
data=load_boston() 
 
X_data = data.data 
y_data = data.target 
m = len(X_data) 
n = len(X_data[0]) 
 
X = tf.placeholder(tf.float32,[m,n]) 
y = tf.placeholder(tf.float32,[m,1]) 
 
W = tf.Variable(tf.ones([n,1])) 
b = tf.Variable(tf.ones([1])) 
 
y_ = tf.matmul(X,W)+b 
 
loss = tf.reduce_mean(tf.square( y - y_)) 
 
optimizer = tf.train.GradientDescentOptimizer(0.01) 
train = optimizer.minimize(loss) 
 
with tf.Session() as sess: 
    init = tf.global_variables_initializer() 
    sess.run(init) 
    vals = [] 
    for i in range(100): 
        val = sess.run([train,loss],feed_dict={X:X_data , y:y_data[:,None]}) 
        vals.append(val) 
 
print(vals) 

输出是:

[[None, 823712.88], 
 [None, 3.2238912e+13], 
 [None, 1.2631078e+21], 
 [None, 4.9488092e+28], 
 [None, 1.9389255e+36], 
 [None, inf], 
 [None, inf], 
 [None, inf], 
 [None, inf], 
 [None, inf], 
 [None, inf], 
 [None, nan], 
 [None, nan], 
 [None, nan], 
 [None, nan], 
 [None, nan], 
... 
 [None, nan], 
 [None, nan]] 

我找不到哪里出错了...帮忙吗？有人吗？

请您参考如下方法:

看来你的学习率太高了。如果你将学习率降低到 1e-6 这样的值，它就会收敛。