Question

这是神经网络模式识别。我使用了vec数据集1 * 54149和1 * 54149目标，我试图训练我的神经网络进行二进制分类（1和0）。我想获得最佳？

clear all;   
clc;   
load vec; load target;     
inputs = double(vec);    
targets = double(target);      

% Create a Pattern Recognition Network    
hiddenLayerSize = 1;    
%net = patternnet(hiddenLayerSize);    
net = patternnet(hiddenLayerSize);    

% Choose Input and Output Pre/Post-Processing Functions    
% For a list of all processing functions type: help nnprocess     
net.inputs{1}.processFcns = {'removeconstantrows','mapstd'};     
net.outputs{2}.processFcns = {'removeconstantrows','mapstd'};     


% Setup Division of Data for Training, Validation, Testing     
% For a list of all data division functions type: help nndivide      
net.divideFcn = 'dividerand';     
net.divideMode = 'sample';  % Divide up every sample     
net.divideParam.trainRatio = 50/100;     
net.divideParam.valRatio = 25/100;      
net.divideParam.testRatio = 25/100;     

% For a list of all training functions type: help nntrain      
net.trainFcn = 'trainrp';        

% Choose a Performance Function      
% For a list of all performance functions type: help nnperformance      
net.performFcn = 'mse';         

% Choose Plot Functions      
% For a list of all plot functions type: help nnplot      
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...    
  'plotregression', 'plotfit'};       


% Train the Network      
[net,tr] = train(net,inputs,targets);      

% Test the Network      
outputs = net(inputs);       
errors = gsubtract(targets,outputs);       
performance = perform(net,targets,outputs);        
[tpr,fpr,thresholds] = roc(targets,outputs);        

% Recalculate Training, Validation and Test Performance      
trainTargets = targets .* tr.trainMask{1};      
valTargets = targets  .* tr.valMask{1};      
testTargets = targets  .* tr.testMask{1};     
trainPerformance = perform(net,trainTargets,outputs);      
valPerformance = perform(net,valTargets,outputs);       
testPerformance = perform(net,testTargets,outputs);         


% View the Network      
view(net)       

 %Plots      
% Uncomment these lines to enable various plots.      
figure, plotperform(tr)      
figure, plottrainstate(tr)        
figure, plotconfusion(targets,outputs)     
figure, ploterrhist(errors)      
figure, plotregression(targets,outputs)     
figure, plotroc(targets,outputs)

所以请有人可以帮助我。提前谢谢你

Answer 1

您的hiddenLayerSize为1非常小。这只允许对线性可分的数据进行分类（即绘制时可以轻松在两个类之间画线的数据。）

尝试使用10的hiddenLayerSize并根据需要上升以获得更好的结果。最好从小做起，看看是否可以使用简单的解决方案。使用54149可能需要更高，但这取决于问题的复杂性。

当您训练数据时，将分为训练，验证和测试集。培训将自动停止泛化，验证数据已达到顶峰。测试数据的准确性（不用于训练或停止）将很好地衡量网络可以推广到类似新数据的程度。

如何通过神经网络获得更好的测试误差/准确度？

1 个答案: