机器学习中的线性回归是一种用于解决回归问题的基础模型。简单来说，回归问题是指给定一些输入变量，我们需要预测一个连续值的问题。例如，预测房价、气温、销售额等。线性回归模型通过拟合一条直线，来表示输入变量与输出变量之间的关系。
线性回归模型的基本形式如下：
y = w1 x1 + w2 x2 +... + wn * xn + b
其中，y 是我们的输出变量（在这里是一个连续值），x1, x2,..., xn 是我们的输入变量，w1, w2,..., wn 是我们需要学习的权重，b 是偏差。
为了使用线性回归模型，我们需要完成以下步骤：
- 收集数据：收集一组包含输入变量和输出变量的数据集。这些数据可以是实验室测量值、历史数据或现实世界中的观测值。
- 数据预处理：处理数据中的异常值、缺失值，并将连续变量标准化（例如归一化）以提高模型性能。
- 划分数据集：将数据集划分为训练集和测试集。训练集用于训练模型，而测试集用于评估模型性能。
- 训练模型：使用训练集数据，通过最小化代价函数（如均方误差，MSE）来调整模型参数（权重和偏差）。
- 评估模型：使用测试集数据，评估模型的性能。如果性能不佳，可以调整学习率、迭代次数等超参数，或尝试使用其他模型。
- 应用模型：将训练好的模型应用于新数据，进行预测。
总之，线性回归是一种简单且易于使用的机器学习模型，可以用于解决回归问题。通过收集数据、预处理数据、划分数据集、训练模型、评估模型和应用模型等步骤，我们可以使用线性回归模型来预测连续值。
Using Queues
If you have a lot of training data, you probably don't want to load it all into memory at once. The QueueRunner in TensorFlow is a tool to efficiently employ a queue data-structure in a multi-threaded way.
import tensorflow as tf
import numpy as np
We will be running multiple threads, so let's figure out the number of CPUs:
import multiprocessing
NUM_THREADS = multiprocessing.cpu_count()
Generate some fake data to work with:
xs = np.random.randn(100, 3)
ys = np.random.randint(0, 2, size=100)
Here's a couple concrete examples of our data:
xs_and_ys = zip(xs, ys)
for _ in range(5):
x, y = next(xs_and_ys)
print('Input {} ---> Output {}'.format(x, y))
Input [ 1.46034759 0.71462742 0.73288402] ---> Output 0
Input [ 1.1537654 -0.09128405 0.08036941] ---> Output 1
Input [-0.61164559 -0.19188485 0.06064167] ---> Output 0
Input [ 0.1007337 0.34815357 0.24346031] ---> Output 0
Input [-1.25581117 1.44738085 1.15035257] ---> Output 0
Define a queue:
queue = tf.FIFOQueue(capacity=1000, dtypes=[tf.float32, tf.int32])
Set up the enqueue and dequeue ops:
enqueue_op = queue.enqueue_many([xs, ys])
x_op, y_op = queue.dequeue()
Define a QueueRunner:
qr = tf.train.QueueRunner(queue, [enqueue_op] * 4)
Now that all variables and ops have been defined, let's get started with a session:
sess = tf.InteractiveSession()
Create threads for the QueueRunner:
coord = tf.train.Coordinator()
enqueue_threads = qr.create_threads(sess, coord=coord, start=True)
Test out dequeueing:
for _ in range(100):
if coord.should_stop():
break
x, y = sess.run([x_op, y_op])
print(x, y)
coord.request_stop()
coord.join(enqueue_threads)
[ 1.46034753 0.71462744 0.73288405] 0
[ 1.15376544 -0.09128405 0.08036941] 1
[-0.61164558 -0.19188486 0.06064167] 0
[ 0.1007337 0.34815356 0.24346031] 0
[-1.25581121 1.4473809 1.1503526 ] 0
[ 0.60369009 -0.87942719 -1.37121975] 1
[ 1.30641925 1.55316997 1.01789773] 0
[ 0.0575242 0.59463078 0.47600508] 1
[-1.22782397 -0.86792755 1.37459588] 1
[-0.27896652 0.51645088 1.36873603] 0
[-0.34542757 0.79360306 0.32000065] 0
[-0.46792462 -0.31817994 0.91739392] 0
[ 0.24787657 0.83848852 1.16125166] 0
[-0.46220389 -0.09412029 -0.9981451 ] 1
[ 0.06739734 -1.08405316 -0.3582162 ] 1
[-1.2644819 -0.27479929 1.15882337] 1
[-0.68015367 -0.10199564 1.4274267 ] 0
[-0.48884565 -0.39484504 0.1496018 ] 1
[ 1.48414564 -0.43943462 -0.12646018] 0
[ 0.49450573 0.42091215 -0.17693481] 0
[ 0.02265234 0.99832052 0.26808155] 1
[-0.94086462 1.67000341 0.92434174] 1
[-0.50961769 -0.39044595 -0.5737586 ] 0
[-0.95702702 0.61196166 -0.86487901] 1
[-0.6125344 -0.30916786 -1.06602347] 1
[-1.91383719 0.26860073 0.50380921] 1
[-0.14638679 0.11614402 1.36613548] 1
[-0.56817967 1.4221288 0.99365205] 0
[-0.04597072 0.43875724 -0.4809106 ] 0
[-0.2000681 -0.2384561 0.06599616] 0
[ 0.5862993 0.85386461 0.82285357] 1
[ 1.64371336 -0.46838599 0.22755136] 0
[ 0.21683638 -0.96399426 1.78278649] 1
[ 0.03778305 2.49208736 0.07467758] 0
[-1.48958826 -0.11699235 0.98281074] 1
[-0.27623582 -0.41658697 -0.89554274] 0
[-1.64742625 1.83507264 -0.76936585] 0
[-1.5386405 0.14272654 0.17047048] 1
[ 0.63654041 1.75451732 -1.14198494] 0
[-0.57061732 0.11121389 1.39394116] 1
[ 1.94736981 -0.36588097 0.54801333] 1
[-0.56976408 -1.36990237 -0.9922803 ] 1
[-2.47653961 1.19603479 -0.3038739 ] 0
[-0.76740891 -0.49611184 0.47167206] 0
[ 1.62004089 0.13268068 0.28845155] 0
[-0.91749012 -0.30151108 -0.08271972] 0
[-0.21053326 -0.16114895 -0.52424961] 1
[ 0.19968066 0.2387522 2.0314014 ] 0
[-0.29072183 0.53720349 -0.38972732] 0
[-0.85891634 -0.26684314 -1.91741192] 1
[-2.07077003 1.97488022 -0.92741841] 0
[ 2.37270904 2.19385314 -0.29643178] 0
[-0.18054648 -0.1651988 1.70858753] 1
[-0.27851281 -0.13095042 0.30613536] 1
[-0.13653868 -0.14431253 1.3018136 ] 1
[-1.79938364 0.26698261 -0.3283855 ] 0
[-0.43491617 -0.8737886 -0.48871836] 1
[-0.27275884 0.08004636 -0.34334385] 0
[-0.06538768 -0.47280514 -1.82918119] 0
[ 1.72329473 0.6359638 1.53474641] 0
[ 0.88200653 0.87051851 0.17676826] 1
[-2.22127795 -0.39812142 0.69118947] 0
[-0.90146214 0.23153968 -1.07890677] 0
[-0.66513097 -0.74897975 -1.9886812 ] 0
[ 0.95217085 -0.1361241 -0.81558466] 1
[ 0.97319698 0.10349847 1.78010297] 0
[ 0.54321396 1.10134006 -1.03641176] 1
[ 0.46445891 0.56387979 0.10383373] 0
[ 0.22231635 -1.20880091 0.20125042] 1
[ 0.56338882 -0.76195502 -0.33035895] 0
[ 0.13885871 0.62347603 0.32560909] 0
[-0.63413048 0.19185983 1.65251637] 1
[ 0.81965917 -0.14427175 -0.9943186 ] 0
[ 1.98786604 -1.38118052 -0.34296793] 0
[-0.49028778 -0.30242845 0.81718981] 0
[ 0.48434621 -1.3200016 -0.32307461] 0
[-0.91041267 -0.34315997 0.71205115] 0
[ 0.61457998 -0.85814965 0.6939835 ] 0
[-0.40195578 -1.11846507 -0.19713871] 1
[-0.47889531 -0.75685191 1.68955612] 1
[ 1.51117146 -2.23529124 1.13895822] 0
[-0.00831293 -0.50950557 0.08648733] 1
[-0.47011089 1.04781067 -0.05893843] 1
[-0.34855339 -0.5695411 -0.12196264] 1
[-0.47251806 -0.49479187 0.27609721] 0
[-2.04546118 -0.16185458 1.42348552] 0
[-0.67136103 -0.16650072 0.3609505 ] 0
[ 1.22566068 1.18665588 -1.87292075] 0
[-0.80474126 -0.1114784 0.00531922] 1
[ 0.62691861 -3.26328206 -0.39003551] 0
[-0.77470082 -1.23692167 -1.55790484] 0
[-0.49005547 -0.19645052 -0.21566501] 1
[-0.44095206 -0.13273652 -0.59810853] 0
[-0.9750855 -0.46043435 0.06064714] 1
[-0.181191 -0.12452056 0.23064452] 1
[-0.34818363 -1.13179028 1.20628965] 0
[-1.58196092 -1.3506341 -2.05767131] 1
[-1.66225421 -0.43541616 1.55258 ] 0
[-0.12949325 -0.15456693 0.04389611] 0
[ 0.24592777 0.11407969 -0.31221709] 1
