How to give target data in nntool or in nnstart?

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santhosh kumar buddepu
回答： Samay Sagar 2024-4-26
I have created dataset of 94 B-scan images (76 training + 18 testing) consists of 3 classes (metal pipe, steel box, plastic box). I have extracted statistical features from images the size of training features is 76*6. and I have assigned labels using traindb.Labels, I can able to load input data as training features from work space but Iam unable to load target data.
My goal is to classify the three objects using NN classifier. I have classified these objects using ECOC-SVM but I'm unable to load target data using nntool. I'm attaching my code and database for your reference. please help me.
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santhosh kumar buddepu
santhosh kumar buddepu 2021-12-8
my labels are 76*1 categorical (metal pipe-19, plastic box-28, steel box-29). I need to classify into 3 classes and my target should be 76*3 which is in the form of
1 1 1....1(19) 0 0 0... (remaining)
0 0 0..0(19) 1 1 1...1(28) 0 0...0(remaining)
0 0 0......0(47) 1 1 1...1(29)
how to make target like this? please help me

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回答（1 个）

Samay Sagar
Samay Sagar 2024-4-26
To load target data for use with a Neural Network classifier, you need to convert your categorical labels into a format that the neural network can understand. This usually means transforming the labels into a one-hot encoded format if you are using a network that outputs class probabilities for each class.
Here is how you can modify your labels:
% Assuming traininglabels is a 76x1 categorical array with categories:
% 'metal pipe', 'plastic box', 'steel box'
% Initialize the target matrix with zeros
numImages = numel(traininglabels); % Number of training images
categoriesList = categories(traininglabels); % Get the list of categories
numClasses = numel(categoriesList); % Number of unique classes
targetMatrix = zeros(numImages, numClasses);
% For each class, set the appropriate entries in the target matrix to 1
for classIndex = 1:numClasses
className = categoriesList{classIndex}; % Access the category name correctly
% Find all rows in traininglabels that belong to the current class
rowsOfClass = traininglabels == className;
% Set the corresponding entries in the target matrix to 1
targetMatrix(rowsOfClass, classIndex) = 1;
end
% Now, targetMatrix is your desired 76x3 target matrix
You can follow a similar approach to prepare your testing target data.
Hope this helps!

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