Tensorflow 我的张量流梯度下降发散

这是我的密码。我的sat.csv包含sat的阅读、写作和数学成绩数据。你可以猜到，这些功能之间的差异并没有那么大

这是sat.csv的一部分

import tensorflow as tf
import pandas as pd
import numpy as np

def normalize(data):
    return data - np.min(data) / np.max(data) - np.min(data)

df = pd.read_csv('sat.csv', skipinitialspace=True)
x_reading = df['reading_score']
x_math = df['math_score']
x_reading, x_math = np.array(x_reading[df.reading_score != 's']), np.array(x_math[df.math_score != 's'])

x_data = normalize(np.float32(np.array([x_reading, x_math])))

y_writing = df[['writing_score']]
y_data = normalize(np.float32(np.array(y_writing[df.writing_score != 's'])))

W = tf.Variable(tf.random_uniform([1, 2], -.5, .5)) #float32
b = tf.Variable(tf.ones([1]))
y = tf.matmul(W, x_data) + b

loss = tf.reduce_mean(tf.square(y - y_data.T))
optimizer = tf.train.GradientDescentOptimizer(0.005)
train = optimizer.minimize(loss)

init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)

    for step in range(1000):
        sess.run(train)
        print step, sess.run(W), sess.run(b), sess.run(loss)

我只使用了数学、写作和阅读成绩。我上面代码的目标是，如果我给出数学和阅读分数，就预测写作分数

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我从未见过Tensorflow的梯度下降模型与如此简单的数据相背离。有什么问题吗？

以下是一些您可以尝试的选项：

• 使输入和输出数据正常化
• 为权重设置较小的初始值
• 使用较低的学习率
• 将您的损失除以您拥有的样本数量（不将数据放在占位符中已经很少见了）

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让我知道这些选项中有什么（如果有的话）有用，祝你好运

谢谢你的帮助！但是，在你的建议下，我尽了一切努力，但似乎没有任何改进。我以前在没有TF的情况下编写的线性回归代码与上面的代码有相同的问题。线性回归开始把我吓坏了！！哦，我刚把学习率改为0.000025，它正在工作！！！非常感谢。但另一个问题是，我的模型学习速度太慢。

DBN,SCHOOL NAME,Num of Test Takers,reading_score,math_score,writing_score
01M292,HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES,29,355,404,363
01M448,UNIVERSITY NEIGHBORHOOD HIGH SCHOOL,91,383,423,366
01M450,EAST SIDE COMMUNITY SCHOOL,70,377,402,370
01M458,FORSYTH SATELLITE ACADEMY,7,414,401,359
01M509,MARTA VALLE HIGH SCHOOL,44,390,433,384
01M515,LOWER EAST SIDE PREPARATORY HIGH SCHOOL,112,332,557,316
01M539,"NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND MATH HIGH SCHOOL",159,522,574,525
01M650,CASCADES HIGH SCHOOL,18,417,418,411
01M696,BARD HIGH SCHOOL EARLY COLLEGE,130,624,604,628
02M047,47 THE AMERICAN SIGN LANGUAGE AND ENGLISH SECONDARY SCHOOL,16,395,400,387