how to get best test error/accuracy with neural networks pattern recognition ?

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casper
casper 2014-3-25
回答： Vrushabh Bhangod 2018-10-21
This is Neural Network Pattern Recognition.I used a vec dataset 1*54149 and 1*54149 target and I'm trying to train my neural network to do binary classification (1 and 0).i want get best ? So please someone can help me ?. thank you in advance
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)

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采纳的回答

Greg Heath
Greg Heath 2014-3-26
Obvious:
1. plot(x,t,'.') to estimate how much training data is really needed to adequately characterize the classes AND to identify and remove or modify outliers
2. Then the short answer is to increase the number of hidden nodes, H, AND for each value of H, loop over multiple (10?) designs with different random initial weights. For examples search using
greg Hmax Ntrials
( where Hmax << Hub = -1 + ceil( (Ntrn-1)/3) ~ round(Ntrn/3) )
ADDITIONAL COMMENTS:
3. TRAINSCG is preferred for classification unless the necessary minimum value of Ntrn is huge. Then TRAINRP is preferred.
4. It may be worthwhile (OR JUST INTERESTING) to
a. Compare the default properties of both
net = trainscg % No semicolon
net = trainrp
b. See how large Ntrn can be before TRAINRP has to be used.
5. Delete or comment all statements that specify values that are already defaults (Different for SCG and RP)
6. If Ntrn is sufficiently large, Nval and Ntst will probably not add any new information.
Hope this helps.
Thank you for formally accepting my answer
Greg
  2 个评论
casper
casper 2014-3-27
编辑：casper 2014-3-27
hi sir. thank you for your answer
but, i didn't understood how can i use :
greg Hmax Ntrials
( where Hmax << Hub = -1 + ceil( (Ntrn-1)/3) ~ round(Ntrn/3) )
please sir i want more detail, because i'm new in neural network.
thank you for your help
Greg Heath
Greg Heath 2014-3-28
NEWSGROUP and ANSWERS designs:
I have posted jillions of double loop designs
for j = Hmin:dH:Hmax
...
for I = 1:Ntrials
...
end
end
Thee is no shortage of detail. Try the NEWSGROUP first.

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更多回答（1 个）

Vrushabh Bhangod
Vrushabh Bhangod 2018-10-21
SIR, I Want a Neural Network with Logsig as the activation function and validation 0, Training % 70%, testing % 30%, learning rate 0.1, momentum coefficient and I am unable to find the accuracy. Kindly answer the earliset

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