个人中心
文章发布
退出
作者热门文章
- html - 出于某种原因，IE8 对我的 Sass 文件中继承的 html5 CSS 不友好？
- JMeter 在响应断言中使用 span 标签的问题
- html - 在 :hover and :active? 上具有不同效果的 CSS 动画
- html - 相对于居中的 html 内容固定的 CSS 重复背景？
27
4
我是 tensorflow 和机器学习的初学者。我想通过 tensorflow 尝试一个简单的线性回归示例。
但是3700个epoch之后损失就无法减少了。不知道怎么了？
显然，我们得到了W = 3.52，b = 2.8865。所以y = 3.52*x + 2.8865。测试数据x = 11, y = 41.6065时。但这是错误的。因为训练数据x = 10, y = 48.712。
下面发布了代码和损失。
#Goal: predict the house price in 2017 by linear regression method
#Step: 1. load the original data
# 2. define the placeholder and variable
# 3. linear regression method
# 4. launch the graph
from __future__ import print_function
import os
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
# 1. load the original data
price = np.asarray([6.757, 12.358, 10.091, 11.618, 14.064,
16.926, 17.673, 22.271, 26.905, 34.742, 48.712])
year = np.asarray([0,1,2,3,4,5,6,7,8,9,10])
n_samples = price.shape[0]
# 2. define the placeholder and variable
x = tf.placeholder("float")
y_ = tf.placeholder("float")
W = tf.Variable(np.random.randn())
b = tf.Variable(np.random.randn())
# 3. linear regression method
y = tf.add(tf.multiply(x, W), b)
loss = tf.reduce_mean(tf.square(y - y_))/(2*n_samples)
training_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
# 4. launch the graph
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(10000):
for (year_epoch, price_epoch) in zip(year, price):
sess.run(training_step, feed_dict = {x: year_epoch, y_: price_epoch})
if (epoch+1) % 50 == 0:
loss_np = sess.run(loss, feed_dict={x: year, y_: price})
print("Epoch: ", '%04d' % (epoch+1), "loss = ", "{:.9f}".format(loss_np), "W = ", sess.run(W), "b = ", sess.run(b))
# print "Training finish"
training_loss = sess.run(loss, feed_dict = {x: year, y_: price})
print("Training cost = ", training_loss, "W = ", sess.run(W), "b = ", sess.run(b), '\n')
损失为:
Epoch: 0050 loss = 1.231071353 W = 3.88227 b = 0.289058
Epoch: 0100 loss = 1.207471132 W = 3.83516 b = 0.630129
Epoch: 0150 loss = 1.189429402 W = 3.79423 b = 0.926415
Epoch: 0200 loss = 1.175611973 W = 3.75868 b = 1.1838
Epoch: 0250 loss = 1.165009260 W = 3.72779 b = 1.40738
Epoch: 0300 loss = 1.156855702 W = 3.70096 b = 1.60161
Epoch: 0350 loss = 1.150570631 W = 3.67766 b = 1.77033
Epoch: 0400 loss = 1.145712137 W = 3.65741 b = 1.9169
Epoch: 0450 loss = 1.141945601 W = 3.63982 b = 2.04422
Epoch: 0500 loss = 1.139016271 W = 3.62455 b = 2.15483
Epoch: 0550 loss = 1.136731029 W = 3.61127 b = 2.25091
Epoch: 0600 loss = 1.134940267 W = 3.59974 b = 2.33437
Epoch: 0650 loss = 1.133531928 W = 3.58973 b = 2.40688
Epoch: 0700 loss = 1.132419944 W = 3.58103 b = 2.46986
Epoch: 0750 loss = 1.131537557 W = 3.57347 b = 2.52458
Epoch: 0800 loss = 1.130834818 W = 3.5669 b = 2.57211
Epoch: 0850 loss = 1.130271792 W = 3.5612 b = 2.6134
Epoch: 0900 loss = 1.129818439 W = 3.55625 b = 2.64927
Epoch: 0950 loss = 1.129452229 W = 3.55194 b = 2.68042
Epoch: 1000 loss = 1.129154325 W = 3.5482 b = 2.70749
Epoch: 1050 loss = 1.128911495 W = 3.54496 b = 2.731
Epoch: 1100 loss = 1.128711581 W = 3.54213 b = 2.75143
Epoch: 1150 loss = 1.128546953 W = 3.53968 b = 2.76917
Epoch: 1200 loss = 1.128411174 W = 3.53755 b = 2.78458
Epoch: 1250 loss = 1.128297567 W = 3.53571 b = 2.79797
Epoch: 1300 loss = 1.128202677 W = 3.5341 b = 2.8096
Epoch: 1350 loss = 1.128123403 W = 3.5327 b = 2.81971
Epoch: 1400 loss = 1.128056765 W = 3.53149 b = 2.82849
Epoch: 1450 loss = 1.128000259 W = 3.53044 b = 2.83611
Epoch: 1500 loss = 1.127952814 W = 3.52952 b = 2.84274
Epoch: 1550 loss = 1.127912283 W = 3.52873 b = 2.84849
Epoch: 1600 loss = 1.127877355 W = 3.52804 b = 2.85349
Epoch: 1650 loss = 1.127847791 W = 3.52744 b = 2.85783
Epoch: 1700 loss = 1.127822518 W = 3.52692 b = 2.8616
Epoch: 1750 loss = 1.127801418 W = 3.52646 b = 2.86488
Epoch: 1800 loss = 1.127782702 W = 3.52607 b = 2.86773
Epoch: 1850 loss = 1.127766728 W = 3.52573 b = 2.8702
Epoch: 1900 loss = 1.127753139 W = 3.52543 b = 2.87234
Epoch: 1950 loss = 1.127740979 W = 3.52517 b = 2.87421
Epoch: 2000 loss = 1.127731323 W = 3.52495 b = 2.87584
Epoch: 2050 loss = 1.127722263 W = 3.52475 b = 2.87725
Epoch: 2100 loss = 1.127714872 W = 3.52459 b = 2.87847
Epoch: 2150 loss = 1.127707958 W = 3.52444 b = 2.87953
Epoch: 2200 loss = 1.127702117 W = 3.52431 b = 2.88045
Epoch: 2250 loss = 1.127697825 W = 3.5242 b = 2.88126
Epoch: 2300 loss = 1.127693415 W = 3.52411 b = 2.88195
Epoch: 2350 loss = 1.127689362 W = 3.52402 b = 2.88255
Epoch: 2400 loss = 1.127686620 W = 3.52395 b = 2.88307
Epoch: 2450 loss = 1.127683759 W = 3.52389 b = 2.88352
Epoch: 2500 loss = 1.127680898 W = 3.52383 b = 2.88391
Epoch: 2550 loss = 1.127679348 W = 3.52379 b = 2.88425
Epoch: 2600 loss = 1.127677798 W = 3.52374 b = 2.88456
Epoch: 2650 loss = 1.127675653 W = 3.52371 b = 2.88483
Epoch: 2700 loss = 1.127674222 W = 3.52368 b = 2.88507
Epoch: 2750 loss = 1.127673268 W = 3.52365 b = 2.88526
Epoch: 2800 loss = 1.127672315 W = 3.52362 b = 2.88543
Epoch: 2850 loss = 1.127671123 W = 3.5236 b = 2.88559
Epoch: 2900 loss = 1.127670288 W = 3.52358 b = 2.88572
Epoch: 2950 loss = 1.127670050 W = 3.52357 b = 2.88583
Epoch: 3000 loss = 1.127669215 W = 3.52356 b = 2.88592
Epoch: 3050 loss = 1.127668500 W = 3.52355 b = 2.88599
Epoch: 3100 loss = 1.127668381 W = 3.52354 b = 2.88606
Epoch: 3150 loss = 1.127667665 W = 3.52353 b = 2.88615
Epoch: 3200 loss = 1.127667546 W = 3.52352 b = 2.88621
Epoch: 3250 loss = 1.127667069 W = 3.52351 b = 2.88626
Epoch: 3300 loss = 1.127666950 W = 3.5235 b = 2.8863
Epoch: 3350 loss = 1.127666354 W = 3.5235 b = 2.88633
Epoch: 3400 loss = 1.127666593 W = 3.5235 b = 2.88637
Epoch: 3450 loss = 1.127666593 W = 3.52349 b = 2.8864
Epoch: 3500 loss = 1.127666235 W = 3.52349 b = 2.88644
Epoch: 3550 loss = 1.127665997 W = 3.52348 b = 2.88646
Epoch: 3600 loss = 1.127665639 W = 3.52348 b = 2.88648
Epoch: 3650 loss = 1.127665639 W = 3.52348 b = 2.88649
Epoch: 3700 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 3750 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 3800 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 3850 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 3900 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 3950 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4000 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4050 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4100 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4150 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4200 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4250 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4300 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4350 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4400 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4450 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4500 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4550 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4600 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4650 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4700 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4750 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4800 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4850 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4900 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 4950 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5000 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5050 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5100 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5150 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5200 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5250 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5300 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5350 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5400 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5450 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5500 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5550 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5600 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5650 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5700 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5750 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5800 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5850 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5900 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 5950 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6000 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6050 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6100 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6150 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6200 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6250 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6300 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6350 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6400 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6450 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6500 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6550 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6600 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6650 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6700 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6750 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6800 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6850 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6900 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 6950 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7000 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7050 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7100 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7150 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7200 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7250 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7300 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7350 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7400 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7450 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7500 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7550 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7600 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7650 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7700 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7750 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7800 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7850 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7900 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 7950 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8000 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8050 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8100 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8150 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8200 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8250 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8300 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8350 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8400 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8450 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8500 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8550 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8600 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8650 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8700 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8750 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8800 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8850 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8900 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 8950 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9000 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9050 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9100 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9150 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9200 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9250 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9300 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9350 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9400 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9450 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9500 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9550 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9600 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9650 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9700 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9750 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9800 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9850 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9900 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 9950 loss = 1.127665997 W = 3.52348 b = 2.8865
Epoch: 10000 loss = 1.127665997 W = 3.52348 b = 2.8865
Training cost = 1.12767 W = 3.52348 b = 2.8865
最佳答案
您假设预测输出呈直线的假设是不正确的。检查年份和价格的情节如何。 
因此，您所采取的线性假设将通过满足尽可能多的输入点来尝试最好地拟合直线，以降低成本。因此，当您测试范围之外的点时，它将在直线上预测最适合您提供的输入集的优化值。
现在，您提到了两个问题。
<强>1。成本没有下降:尝试降低学习率。您的成本肯定会下降。
<强>2。你的year = 11的输出是错误的:原因我已经在上面提到过。你需要做的是改变假设。包括一个平方项，然后检查它。示例:y = ax^2 + bx + c。您将更好地拟合这个假设方程。
关于machine-learning - tensorflow 的简单线性回归，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/46622270/
27
4
0
[在此处输入图像描述][1]我正在努力弄清楚回归是否是我需要走的路线，以便解决我当前使用 Python 的挑战。这是我的场景: 我有一个 195 行 x 25 列的 Pandas Dataframe
我想训练回归模型(不是分类)，其输出是连续数字。 假设我有输入变量 X，其范围在 -70 到 70 之间。我有输出变量 Y，其范围在 -5 到 5 之间。X 有 39 个特征，Y 有 16 个特征，每
我想使用神经网络逼近 sinc 函数。这是我的代码: import tensorflow as tf from keras.layers import Dense from keras.models
我对 postgres 表做了一些更改，我想将其恢复到以前的状态。没有数据库的备份。有办法吗？比如，postgres 会自动拍摄快照并将其存储在某个地方，还是原始数据会永远丢失？ 最佳答案 默认情况下
我有大约 100 个 7x7 因变量矩阵(所以有 49 个因变量)。我的自变量是时间。我正在做一个物理项目，我应该通过求解 ODE 得到一个矩阵函数(矩阵的每个元素都是时间的函数)。我使用了 nump
我之前曾被告知——出于完全合理的原因——当结果变量为二元变量时(即是/否、真/假、赢/输等)，不应运行 OLS 回归。但是，我经常阅读经济学/其他社会科学方面的论文，其中研究人员对二元变量运行 OLS
您好，我正在使用生命线包进行 Cox 回归。我想检查非二元分类变量的影响。有内置的方法吗？或者我应该将每个类别因子转换为一个数字？或者，在生命线中使用 kmf fitter，是否可以对每个因素执行此操
作为后续 this question ，我拟合了具有定量和定性解释变量之间相互作用的多元 Logistic 回归。 MWE如下: Type |z|) (Intercept) -0.65518
我想在单个动物园对象中的多对数据系列上使用 lm 执行滚动回归。 虽然我能够通过以下代码对动物园对象中的一对数据系列执行滚动回归: FunLm seat time(seat) seat fm
是否有一种简单的方法可以在 R 中拟合多元回归，其中因变量根据 Skellam distribution 分布？ (两个泊松分布计数之间的差异)？比如: myskellam <- glm(A ~ B
包含各种特征和回归目标(称为 qval)的数据集用于训练 XGBoost 回归器。该值 qval 介于 0 和 1 之间，应具有以下分布: 到目前为止，还不错。但是，当我使用 xgb.save_mod
这有效: felm(y ~ x1 + x2 | fe1 + fe2 | 0 | , data = data) 我想要: fixedeffects = "fe1 + fe2" felm(y ~ x1
这有效: felm(y ~ x1 + x2 | fe1 + fe2 | 0 | , data = data) 我想要: fixedeffects = "fe1 + fe2" felm(y ~ x1
关闭。这个问题不符合Stack Overflow guidelines .它目前不接受答案。 我们不允许提问寻求书籍、工具、软件库等的推荐。您可以编辑问题，以便用事实和引用来回答。 关闭 7 年前。
我刚刚开始使用 R 进行统计分析，而且我还在学习。我在 R 中创建循环时遇到问题。我有以下案例，我想知道是否有人可以帮助我。对我来说，这似乎是不可能的，但对你们中的一些人来说，这只是小菜一碟。我有不同
是否可以在 sklearn 中使用或不使用(即仅使用截距)预测器来运行回归(例如逻辑回归)？这似乎是一个相当标准的类型分析，也许这些信息已经在输出中可用。 我发现的唯一相关的东西是sklearn.sv
假设我对一些倾斜的数据分布执行 DNN 回归任务。现在我使用平均绝对误差作为损失函数。 机器学习中的所有典型方法都是最小化平均损失，但对于倾斜来说这是不恰当的。从实际角度来看，最好尽量减少中值损失。我
我正在对公寓特征进行线性回归分析，然后预测公寓的价格。目前，我已经收集了我所在城市 13000 套公寓的特征。我有 23-25 个特征，我不确定在公寓价格预测中拥有如此多的特征是否正常。 我有以下功能
我是 ML 新手，对 catboost 有疑问。所以，我想预测函数值(例如 cos | sin 等)。我回顾了一切，但我的预测始终是直线 是否可能，如果可能，我该如何解决我的问题 我很高兴收到任何评论
我目前已经为二进制类实现了概率(至少我这么认为)。现在我想扩展这种回归方法，并尝试将其用于波士顿数据集。不幸的是，我的算法似乎被卡住了，我当前运行的代码如下所示: from sklearn impor
我是一名优秀的程序员,十分优秀！