Vowpal Wabbit原始预测（-r）和预测之间的区别（-p）

Question

我想分类二进制数据。在数据文件中，类[0,1]被转换为[-1,1]。数据有21个功能。所有功能都是分类。我正在使用神经网络进行培训。培训的命令是：

 vw -d train.vw --cache_file data --passes 5 -q sd -q ad -q do -q fd --binary -f model --nn 22 

我创建原始预测文件：

vw -d test.vw -t -i neuralmodel -r raw.txt 

和正常预测文件：

vw -d test.vw -t -i neuralmodel -p out.txt 

第一原始文件的五行分别是：

0:-0.861075,-0.696812 1:-0.841357,-0.686527 2:0.796014,0.661809 3:1.06953,0.789289 4:-1.23823,-0.844951 5:0.886767,0.709793 6:2.02206,0.965555 7:-2.40753,-0.983917 8:-1.09056,-0.797075 9:1.22141,0.84007 10:2.69466,0.990912 11:2.64134,0.989894 12:-2.33309,-0.981359 13:-1.61462,-0.923839 14:1.54888,0.913601 15:3.26275,0.995055 16:2.17991,0.974762 17:0.750114,0.635229 18:2.91698,0.994164 19:1.15909,0.820746 20:-0.485593,-0.450708 21:2.00432,0.964333 -0.496912 
    0:-1.36519,-0.877588 1:-2.83699,-0.993155 2:-0.257558,-0.251996 3:-2.12969,-0.97213 4:-2.29878,-0.980048 5:2.70791,0.991148 6:1.31337,0.865131 7:-2.00127,-0.964116 8:-2.14167,-0.972782 9:2.50633,0.986782 10:-1.09253,-0.797788 11:2.29477,0.97989 12:-1.67385,-0.932057 13:-0.740598,-0.629493 14:0.829695,0.680313 15:3.31954,0.995055 16:3.44069,0.995055 17:2.48612,0.986241 18:1.32241,0.867388 19:1.97189,0.961987 20:1.19584,0.832381 21:1.65151,0.929067 -0.588528 
    0:0.908454,0.72039 1:-2.48134,-0.986108 2:-0.557337,-0.505996 3:-2.15072,-0.973263 4:-1.77706,-0.944375 5:0.202272,0.199557 6:2.37479,0.982839 7:-1.97478,-0.962201 8:-1.78124,-0.944825 9:1.94016,0.959547 10:-1.67845,-0.932657 11:2.54895,0.987855 12:-1.60502,-0.92242 13:-2.32369,-0.981008 14:1.59895,0.921511 15:2.02658,0.96586 16:2.55443,0.987987 17:3.47049,0.995055 18:1.92482,0.958313 19:1.47773,0.901044 20:-3.60913,-0.995055 21:3.56413,0.995055 -0.809399 
    0:-2.11677,-0.971411 1:-1.32759,-0.868656 2:2.59003,0.988807 3:-0.198721,-0.196146 4:-2.51631,-0.987041 5:0.258549,0.252956 6:1.60134,0.921871 7:-2.28731,-0.97959 8:-2.89953,-0.993958 9:-0.0972349,-0.0969177 10:3.1409,0.995055 11:1.62083,0.924746 12:-2.30097,-0.980134 13:-2.05674,-0.967824 14:1.6744,0.932135 15:1.85612,0.952319 16:2.7231,0.991412 17:1.97199,0.961995 18:3.47125,0.995055 19:0.603527,0.539567 20:1.25539,0.84979 21:2.15267,0.973368 -0.494474 
    0:-2.21583,-0.97649 1:-2.16823,-0.974171 2:2.00711,0.964528 3:-1.84079,-0.95087 4:-1.27159,-0.854227 5:-0.0841799,-0.0839635 6:2.24566,0.977836 7:-2.19458,-0.975482 8:-2.42779,-0.98455 9:0.39883,0.378965 10:1.32133,0.86712 11:1.87572,0.95411 12:-2.22585,-0.976951 13:-2.04512,-0.96708 14:1.52652,0.909827 15:1.98228,0.962755 16:2.37265,0.982766 17:1.73726,0.939908 18:2.315,0.980679 19:-0.08135,-0.081154 20:1.39248,0.883717 21:1.5889,0.919981 -0.389856 

前五行（正常）预测文件是：

-0.496912 
    -0.588528 
    -0.809399 
    -0.494474 
    -0.389856 

我已经用原始输出计算了这个（正常）输出。我注意到五个原始行中每个行的（最后或结尾）浮点值与上面相同。

我希望了解原始输出和正常输出。每条线有22对值是与22个神经元有关吗？如何将输出解释为[-1,1]以及为什么需要一个sigmoid函数来将上述任意一个转换为概率。将不胜感激的帮助。

Answer 1

对于二元分类，您应该使用合适的损失函数（--loss_function=logistic或--loss_function=hinge）。 --binary交换机只是确保报告的损失是0/1损失（但您不能直接优化0/1损失，默认损失功能是--loss_function=squared）。

我建议尝试​​作为调整VW参数时的最后一步。通常，它只改善一点点结果，并且隐藏层中的最佳单位数量非常小（--nn 1，--nn 2或--nn 3）。您还可以尝试在输入和输出层之间添加一个与--inpass的直接连接。 注意​​总是使用的tanh作为隐藏层的S形函数和仅一个隐藏层是可能的（它是在硬编码nn.cc）。

如果你想获得的概率（从实数[0,1]），使用vw -d test.vw -t -i neuralmodel --link=logistic -p probabilities.txt。如果您希望输出为[-1,1]的实数，请使用--link=glf1。

如果没有--link和--binary,--pred输出是内部预测（当使用逻辑或铰链损失函数时，范围为[-50,50]）。

至于--nn --raw问题，您的猜测是正确的： 22对数字对应于22个神经元，最后一个数字是最终（内部）预测。我的猜测是每对对应隐藏层上每个单元的偏置和输出。