Question

我已经制作了用于鼠标手势识别的简单神经网络（输入是角度），并且我使用了nprtool（用于创建的函数patternnet）。我保存了网络的权重和偏见：

W1=net.IW{1,1};
W2=net.LW{2,1};
b1=net.b{1,1};
b2=net.b{2,1};

用于计算结果我使用了tansig(W2*(tansig(W1*in+b1))+b2); 其中in是输入。但结果很糟糕（每个数字大约等于0.99）。表彰net(in)的结果很好。我究竟做错了什么？对我来说非常重要的是为什么第一种方法是坏的（和我在C ++程序中做的一样）。我正在寻求帮助：）

[编辑] 下面是nprtool GUI生成的代码。也许对某人来说这会有所帮助，但我没有从这段代码中看到我的问题的任何解决方案。对于隐藏和输出层，神经元使用tansig激活函数（MATLAB网络中是否有任何参数？）。

% Solve a Pattern Recognition Problem with a Neural Network
% Script generated by NPRTOOL
% Created Tue May 22 22:05:57 CEST 2012
%
% This script assumes these variables are defined:
%
%   input - input data.
%   target - target data.    
inputs = input;
targets = target;

% Create a Pattern Recognition Network
hiddenLayerSize = 10;
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','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};


% Setup Division of Data for Training, Validation, Testing
% For a list of all data division functions type: help nndivide
net.divideFcn = 'dividerand';  % Divide data randomly
net.divideMode = 'sample';  % Divide up every sample
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

% For help on training function 'trainlm' type: help trainlm
% For a list of all training functions type: help nntrain
net.trainFcn = 'trainlm';  % Levenberg-Marquardt

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

% 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)

% 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)

Answer 1

从代码中可以看出，网络对目标的输入和后处理应用自动预处理 - 查找定义processFcns的行。这意味着训练的参数对于预处理的输入有效，并且网络的输出被后处理（具有与目标相同的参数）。因此，在您的行tansig(W2*(tansig(W1*in+b1))+b2);中，您无法使用原始输入。您必须预处理输入，将结果用作网络的输入，并使用用于后处理目标的相同参数对输出进行后处理。只有这样，您才能获得与调用net(in)相同的结果。

您可以在此处阅读更多内容：http://www.mathworks.com/help/toolbox/nnet/rn/f0-81221.html#f0-81692

MATLAB神经网络模式识别

1 个答案: