I'm having trouble with convolution1dLayer

Question

nagihan yagmur 2023-3-26

1
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1935274-i-m-having-trouble-with-convolution1dlayer

评论： Matt J 2023-4-6

layers = [
    featureInputLayer(24)
    convolution1dLayer(5, 32, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    maxPooling1dLayer(2, 'Stride', 2)
    convolution1dLayer(5, 64, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    maxPooling1dLayer(2, 'Stride', 2)
    convolution1dLayer(5, 128, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    maxPooling1dLayer(2, 'Stride', 2)
    dropoutLayer(0.5)
    fullyConnectedLayer(5)
    softmaxLayer
    classificationLayer];
options = trainingOptions('adam', ...
    'MaxEpochs', 20, ...
    'MiniBatchSize', 128, ...
    'ValidationData', {XVal, YVal}, ...
    'ValidationFrequency', 50, ...
    'Shuffle', 'every-epoch', ...
    'Verbose', false, ...
    'Plots', 'training-progress');
%Xtrain = 5000x24 Ytrain = 5000x1 Xtest = 5000x24 Ytest=50000x1
net = trainNetwork(XTrain, YTrain, layers, options);
% Doğruluk oranını hesapla
YPred = classify(net, XTest);
accuracy = sum(YPred == YTest) / numel(YTest);
fprintf('Doğruluk oranı: %0.2f%%\n', 100*accuracy);

I have a dataset of 15300 records with 24 features. (size 15300x24) My output dataset consists of 5 classes (15300x1). I am trying to classify with cnn. When I write the Layer, I encounter the following error:

Caused by:

Layer 2: Input data must have one spatial dimension only, one temporal dimension only, or one of each.

Instead, it has 0 spatial dimensions and 0 temporal dimensions.

I haven't been able to solve it.

2 个评论
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Walter Roberson 2023-3-26

Your XTrain is empty, somehow.

nagihan yagmur 2023-3-26

编辑：Walter Roberson 2023-3-26

在 MATLAB Online 中打开

clc;
clear all;
load veri_seti.mat
X = MyData.Inp;
Y = categorical(MyData.Out); 
numClasses = 5;
layers = [    featureInputLayer(24,'Name','inputs')                 
    convolution1dLayer(128,3,'Stride',2)    
    reluLayer()    maxPooling1dLayer(2,'Stride',2)    
    batchNormalizationLayer()    
    convolution1dLayer(64,3,'Stride',1)   
    reluLayer()   
    maxPooling1dLayer(2,'Stride',2)    
    batchNormalizationLayer()   
    dropoutLayer(0.2)    
    convolution1dLayer(32,3,'Stride',1)    
    reluLayer()    
    batchNormalizationLayer()    
    convolution1dLayer(16,3,'Stride',1)   
    reluLayer()    
    batchNormalizationLayer()    
    dropoutLayer(0.2)    
    convolution1dLayer(8,3,'Stride',1)    
    reluLayer()    
    maxPooling1dLayer(2,'Stride',2)    
    globalMaxPooling1dLayer()   
    dropoutLayer(0.2)    
    batchNormalizationLayer()    
    fullyConnectedLayer(1024)    
    fullyConnectedLayer(1024)    
    softmaxLayer()    
    classificationLayer()];
% Öğrenme oranı ve diğer hiperparametreler
miniBatchSize = 128;
maxEpochs = 30;
initialLearningRate = 0.001;
learnRateDropFactor = 0.1;
learnRateDropPeriod = 10;
% Options nesnesi
options = trainingOptions('adam', ...
    'MiniBatchSize', miniBatchSize, ...
    'MaxEpochs', maxEpochs, ...
    'InitialLearnRate', initialLearningRate, ...
    'LearnRateSchedule', 'piecewise', ...
    'LearnRateDropFactor', learnRateDropFactor, ...
    'LearnRateDropPeriod', learnRateDropPeriod, ...
    'Shuffle', 'every-epoch', ...
    'Verbose', false, ...
    'Plots', 'training-progress');

Error using vertcat

Dimensions of arrays being concatenated are not consistent.

Error in example (line 9)

layers = [ featureInputLayer(24,'Name','inputs')

I'm constantly between these two errors

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Answer 1

Matt J 2023-4-4

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1935274-i-m-having-trouble-with-convolution1dlayer#answer_1208954

编辑：Matt J 2023-4-6

在 MATLAB Online 中打开

Tech Support has suggested 2 workarounds to me. The simplest IMO is to recast the training as a 2D image classification problem, where one of the dimensions of the image is a singleton. This requires the use of an imageInputLayer as well as converting convolutional and pooling layers to 2D form, also specifying one of the dimensions as a singleton.

load veri_seti
XTrain = reshape(MyData.Inp',24,1,1,[]);  %Dimensions: 24x1x1xBatch
YTrain = reshape( categorical(MyData.Out),[],1); %Dimensions: Batchx1
layers = [ imageInputLayer([24,1],'Name','inputs')        %<---Use imageInputLayer         
    convolution2dLayer([5,1], 32, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer([2,1], 'Stride', [2,1])
    convolution2dLayer([5,1], 64, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer([2,1], 'Stride', [2,1])
    convolution2dLayer([5,1], 128, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer([2,1], 'Stride', [2,1])
    dropoutLayer(0.5)
    flattenLayer
    fullyConnectedLayer(5)
    softmaxLayer
    classificationLayer];
%analyzeNetwork(layers);
options = trainingOptions('adam', ...
    'MaxEpochs', 3, ...
    'MiniBatchSize', 128, ...
    'Verbose', false, ...
    'Plots', 'training-progress','ExecutionEnvironment','cpu');
net = trainNetwork(XTrain, YTrain(:), layers,options);

3 个评论
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nagihan yagmur 2023-4-6

I am very very grateful

Matt J 2023-4-6

I'm glad, but please Accept-click the answer to indicate that it worked.

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Answer 2

Walter Roberson 2023-3-26

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1935274-i-m-having-trouble-with-convolution1dlayer#answer_1201359

移动：Walter Roberson 2023-3-26

在 MATLAB Online 中打开

layers = [    featureInputLayer(24,'Name','inputs')                 
    convolution1dLayer(128,3,'Stride',2)    
    reluLayer()    maxPooling1dLayer(2,'Stride',2)    

Notice you have two layers on the same line.