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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)
这是我的代码。我的sat.csv包含SAT阅读,写作和数学成绩的数据。你可以猜到,这些功能之间的差异并不大。My TensorFlow渐变下降
这是sat.csv的一部分。
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
我只使用数学,写作和阅读成绩。我上面的代码的目标是预测写作分数,如果我给数学和阅读分数。
我从未见过Tensorflow的梯度下降模型与这种简单的数据不同。什么错了?
感谢您的帮助!但是,尽管我已经尝试了您的建议,但似乎没有任何改进。之前我没有使用TF的线性回归代码与上面的代码有相同的问题。线性回归开始吓到我了! –
哦,我刚刚将学习速率更改为0.0000252,并且正在运行!非常感谢。但另一个问题是我的模型学得如此之慢。 –