On performing regression tasks from spectral imaging data

Question

大樹 2023-7-20

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1998383-on-performing-regression-tasks-from-spectral-imaging-data

回答： Shreeya 2024-2-20

I would like to be informed about the modifications.

I would like to create a model using deep learning to predict concentration from spectra, using spectral mapping data and the concentration of the three components at each measurement point as the original data.

I have written the following code, but I get the following error

Error while using: trainNetwork

Invalid training data. The output size (3) of the last layer does not match the response size (100).

Error: layer_test (line 86)

net = trainNetwork(xTrain, yTrain, layers, options);

The original data for spectral mapping is 100pixel x 100pixel x 380 channels.

I do not understand why the response is 100 and would appreciate your advice, including whether such a task might be possible.

% read .dm3 files
[~, ximage1] = dmread('P4.dm3');
% set the data ranges for processing
xdim = 100;
ydim = 100;
zdim = 380;
row = xdim*ydim;
% Expand SI data to 2D matrices
xdat1 = squeeze(ximage1(1:xdim, 1:ydim, 1:zdim));
xdat1_reshape = reshape(xdat1, [row,zdim]);
% Load labels (C1, C2, C3)
C1 = readmatrix('C1.txt');
C2 = readmatrix('C2.txt');
C3 = readmatrix('C3.txt');
% Extract the desired range from C1, C2, C3
C1 = C1(1:xdim, 1:ydim);
C1_reshape = reshape(C1,[row,1]); 
C2 = C2(1:xdim, 1:ydim);
C2_reshape = reshape(C2,[row,1]);
C3 = C3(1:xdim, 1:ydim);
C3_reshape = reshape(C3,[row,1]);
C = cat(3, C1, C2, C3); % 出
% network
layers = [
    sequenceInputLayer([100, 100, 380]) 
    convolution2dLayer(3, 16, 'Padding', 'same')
    reluLayer()
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(3, 32, 'Padding', 'same')
    reluLayer()
    maxPooling2dLayer(2, 'Stride', 2)
    flattenLayer() % to 1d
    lstmLayer(20, 'OutputMode', 'last') % LSTM）
    fullyConnectedLayer(3) 
    regressionLayer('Name', 'output') 
];
% option setting
options = trainingOptions('adam', 'MaxEpochs', 10, 'MiniBatchSize', 32);
% data prep.
xTrain = xdat1;
yTrain = C;
net = trainNetwork(xTrain, yTrain, layers, options);

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Answer 1

Shreeya 2024-2-20

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1998383-on-performing-regression-tasks-from-spectral-imaging-data#answer_1412408

From the error message, it seems that the output size of the neural network is inconsistent with the expected response size. You can approach this problem by changing the number of neurons in the last layer. Also, refer to this link for more help:

https://www.mathworks.com/matlabcentral/answers/480565-invalid-training-data-the-output-size-of-the-last-layer-does-not-match-the-response-size