How to Train 1d CNN on Custom dataset in matrix form in MATLAB

Question

Med Future 2022-2-17

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1651895-how-to-train-1d-cnn-on-custom-dataset-in-matrix-form-in-matlab

评论： yanqi liu 2022-2-18

myFile.txt

Hi everyone, i hope you are doing well.

In my previous question : https://www.mathworks.com/matlabcentral/answers/1649260-how-to-train-cnn-on-custom-dataset-in-matrix-form

yanqi liu answer the question with 2D CNN, But i wanted to train 1D CNN

i have the following dataset myFile.txt includes 102x5,in which first 4 coloums are the Number of Observation and the last column are the Discrete labels/Classes for the dataset. I want to train 1D-CNN on this dataset

sz = size(dataset);
dataset = dataset(randperm(sz(1)),:);
traindata=dataset(:,1:4);
trainlabel=categorical(dataset(:,5));
classes = unique(trainlabel)
numClasses = numel(unique(trainlabel))
PD = 0.80 ;  
Ptrain = []; Ttrain = [];
Ptest = []; Ttest = [];
for i = 1 : length(classes)
    indi = find(trainlabel==classes(i));
    indi = indi(randperm(length(indi)));
    indj = round(length(indi)*PD);
    Ptrain = [Ptrain; traindata(indi(1:indj),:)]; Ttrain = [Ttrain; trainlabel(indi(1:indj),:)]; 
    Ptest = [Ptest; traindata(indi(1+indj:end),:)]; Ttest = [Ttest; trainlabel(indi(1+indj:end),:)]; 
end
Ptrain=(reshape(Ptrain', [4,1,1,size(Ptrain,1)]));
Ptest=(reshape(Ptest', [4,1,1,size(Ptest,1)]));
layers = [imageInputLayer([4 1 1])
    convolution2dLayer([3 1],3,'Stride',1)
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
    dropoutLayer
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];
options = trainingOptions('adam', ...
    'MaxEpochs',3000, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false, ...
    'ValidationData',{Ptest,Ttest},...
    'ExecutionEnvironment', 'cpu', ...
    'ValidationPatience',Inf);
net = trainNetwork(Ptrain,Ttrain,layers,options);

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Med Future 2022-2-17

@yanqi liu https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.convolution1dlayer.html

here is the 1D CNN layer

Med Future 2022-2-17

编辑：Med Future 2022-2-17

@yanqi liu where i can change in convolution2dLayer to set dimension 1

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Answer 1

yanqi liu 2022-2-17

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1651895-how-to-train-1d-cnn-on-custom-dataset-in-matrix-form-in-matlab#answer_898335

编辑：yanqi liu 2022-2-17

在 MATLAB Online 中打开

yes，sir，if 2021b has convolution1dLayer，so we can make the cnn as follows，then we can try train it

layers = [sequenceInputLayer(4)

convolution1dLayer(3,32,Padding="causal")

reluLayer

globalMaxPooling1dLayer

dropoutLayer

fullyConnectedLayer(5)

softmaxLayer

classificationLayer];

layers

layers =

8×1 Layer array with layers: 1 '' Sequence Input Sequence input with 4 dimensions 2 '' Convolution 32 3 convolutions with stride 1 and padding 'causal' 3 '' ReLU ReLU 4 '' 1-D Global Max Pooling 1-D global max pooling 5 '' Dropout 50% dropout 6 '' Fully Connected 5 fully connected layer 7 '' Softmax softmax 8 '' Classification Output crossentropyex

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yanqi liu 2022-2-17

在 MATLAB Online 中打开

dataset = [0.008,2.5699e+09,-60.397,5.0922e-06,1

0.0084998,2.5698e+09,-59.954,5.0719e-06,1

0.0089996,2.5701e+09,-59.638,5.0947e-06,1

0.0094995,2.5696e+09,-59.246,5.0906e-06,1

0.0099995,2.5701e+09,-58.858,5.0083e-06,1

0.010499,2.5696e+09,-58.538,4.9623e-06,1

0.010999,2.5702e+09,-58.004,4.96e-06,1

0.011499,2.5705e+09,-57.557,4.99e-06,1

0.03,2.5802e+09,-58.372,9.9782e-06,2

0.03055,2.5803e+09,-58.253,9.9844e-06,2

0.0311,2.5801e+09,-58.22,9.9826e-06,2

0.03165,2.5799e+09,-58.096,1.0018e-05,2

0.094902,2.5798e+09,-48.896,1.0022e-05,2

0.095451,2.5805e+09,-48.955,1.0003e-05,2

0.096001,2.5804e+09,-48.721,1.0002e-05,2

0.096551,2.5796e+09,-48.758,1.0011e-05,2

0.097101,2.5799e+09,-48.639,1.0021e-05,2

0.097652,2.5797e+09,-48.812,1.002e-05,2

0.098202,2.5797e+09,-48.622,1.0005e-05,2

0.098752,2.5801e+09,-48.592,1.0009e-05,2

0.099302,2.5796e+09,-48.571,9.989e-06,2

0.099852,2.5804e+09,-48.707,1.0001e-05,2

0.1004,2.5799e+09,-48.712,1.0013e-05,2

0.10095,2.5797e+09,-48.67,1.0008e-05,2

0.1015,2.5804e+09,-48.555,1.0002e-05,2

0.1,2.5904e+09,-56.358,1.4981e-05,3

0.10057,2.59e+09,-55.861,1.4994e-05,3

0.10114,2.5902e+09,-55.625,1.4918e-05,3

0.10171,2.5902e+09,-55.158,1.5037e-05,3

0.10228,2.5901e+09,-54.942,1.5006e-05,3

0.10285,2.5904e+09,-54.477,1.5099e-05,3

0.10342,2.5899e+09,-54.001,1.5055e-05,3

0.10399,2.5896e+09,-53.86,1.5047e-05,3

0.10456,2.5901e+09,-53.599,1.4958e-05,3

0.10513,2.5902e+09,-53.212,1.5004e-05,3

0.1057,2.5896e+09,-52.693,1.5076e-05,3

0.10627,2.59e+09,-52.356,1.4937e-05,3

0.10684,2.5903e+09,-52.188,1.4938e-05,3

0.10741,2.5902e+09,-51.769,1.4918e-05,3

0.10798,2.5897e+09,-51.339,1.4932e-05,3

0.13,2.6001e+09,-58.171,4.9896e-05,4

0.1461,2.6004e+09,-53.86,5.0058e-05,4

0.14725,2.5996e+09,-53.42,4.9917e-05,4

0.1484,2.6002e+09,-53.152,5.0033e-05,4

0.14955,2.6e+09,-52.855,5.0033e-05,4

0.1507,2.5996e+09,-52.568,5.0067e-05,4

0.15185,2.6002e+09,-52.298,5.0028e-05,4

0.153,2.5999e+09,-52.044,5.0002e-05,4

0.15415,2.5996e+09,-51.865,4.992e-05,4

0.1553,2.5998e+09,-51.591,4.997e-05,4

0.15645,2.5996e+09,-51.336,5.0093e-05,4

0.1576,2.6002e+09,-51.19,5.0095e-05,4

0.15875,2.6005e+09,-50.951,4.9887e-05,4

0.1599,2.5995e+09,-50.693,4.9926e-05,4

0.16105,2.6e+09,-50.461,5.012e-05,4

0.1622,2.6004e+09,-50.302,5.0023e-05,4

0.16335,2.6001e+09,-50.048,4.9884e-05,4

0.1645,2.5997e+09,-49.878,5.0005e-05,4

0.16565,2.5997e+09,-49.791,5.0064e-05,4

0.1668,2.6e+09,-49.524,5.0041e-05,4

0.16795,2.6005e+09,-49.388,5.0036e-05,4

0.1691,2.6004e+09,-49.174,5.0053e-05,4

0.17025,2.5995e+09,-48.996,5.0016e-05,4

0.1714,2.5996e+09,-48.934,4.9977e-05,4

0.17255,2.6e+09,-48.782,5.0048e-05,4

0.1737,2.6005e+09,-48.727,5.0117e-05,4

0.17485,2.5997e+09,-48.641,4.9965e-05,4

0.176,2.6003e+09,-48.484,4.9884e-05,4

0.17715,2.5998e+09,-48.39,4.989e-05,4

0.1783,2.6003e+09,-48.42,4.9925e-05,4

0.17945,2.6005e+09,-48.393,4.9896e-05,4

0.1806,2.5997e+09,-48.316,4.9955e-05,4

0.18175,2.6e+09,-48.215,4.99e-05,4

0.1829,2.5997e+09,-48.125,5.0049e-05,4

0.18405,2.5997e+09,-48.28,4.9908e-05,4

0.1852,2.6004e+09,-48.275,5.0074e-05,4

0.18635,2.6001e+09,-48.135,5.0007e-05,4

0.1875,2.6004e+09,-48.314,4.9951e-05,4

0.18865,2.6e+09,-48.185,4.9916e-05,4

0.1898,2.5995e+09,-48.271,5.0097e-05,4

0.19095,2.5996e+09,-48.395,4.9928e-05,4

0.1921,2.6004e+09,-48.396,4.9988e-05,4

0.19325,2.5997e+09,-48.455,5.0102e-05,4

0.1944,2.6e+09,-48.575,5.0096e-05,4

0.19555,2.5998e+09,-48.663,5.0079e-05,4

0.25,2.6037e+09,-75.123,2.0454e-06,5

0.25075,2.6042e+09,-73.315,1.999e-06,5

0.2515,2.6044e+09,-71.653,1.9823e-06,5

0.25225,2.6038e+09,-69.994,2.0639e-06,5

0.253,2.604e+09,-68.391,1.9963e-06,5

0.25375,2.6043e+09,-67.257,1.9299e-06,5

0.2545,2.6039e+09,-66.164,2.0163e-06,5

0.25525,2.6036e+09,-65.639,1.9588e-06,5

0.256,2.6038e+09,-65.321,1.913e-06,5

0.25675,2.6045e+09,-65.28,2.0615e-06,5

0.2575,2.6039e+09,-65.538,1.9969e-06,5

0.25825,2.6041e+09,-66.246,1.9181e-06,5

0.259,2.6041e+09,-67.172,2.0295e-06,5

0.25975,2.6041e+09,-68.554,2.0292e-06,5

0.2605,2.6043e+09,-69.869,2.0366e-06,5

0.26125,2.6043e+09,-71.51,1.9323e-06,5

0.262,2.6036e+09,-73.437,1.9219e-06,5];

sz = size(dataset);

dataset = dataset(randperm(sz(1)),:);

traindata=dataset(:,1:4);

trainlabel=categorical(dataset(:,5));

classes = unique(trainlabel);

numClasses = numel(unique(trainlabel));

PD = 0.80 ;

Ptrain = []; Ttrain = [];

Ptest = []; Ttest = [];

for i = 1 : length(classes)

indi = find(trainlabel==classes(i));

indi = indi(randperm(length(indi)));

indj = round(length(indi)*PD);

Ptrain = [Ptrain; traindata(indi(1:indj),:)]; Ttrain = [Ttrain; trainlabel(indi(1:indj),:)];

Ptest = [Ptest; traindata(indi(1+indj:end),:)]; Ttest = [Ttest; trainlabel(indi(1+indj:end),:)];

end

Ptrain2 = []; Ttrain2 = [];

Ptest2 = []; Ttest2 = [];

Ttrain2=categorical(Ttrain);

Ttest2=categorical(Ttest);

for i = 1 : size(Ptrain,1)

Ptrain2{i} = Ptrain(i,:)';

%Ttrain2{i} = Ttrain(i);

end

for i = 1 : size(Ptest,1)

Ptest2{i} = Ptest(i,:)';

%Ttest2{i} = Ttest(i);

end

layers = [sequenceInputLayer(4)

convolution1dLayer(3,32,Padding="causal")

reluLayer

globalMaxPooling1dLayer

dropoutLayer

fullyConnectedLayer(5)

softmaxLayer

classificationLayer];

options = trainingOptions('adam', ...

'MaxEpochs',3000, ...

'Shuffle','every-epoch', ...

'Verbose',false, ...

'ValidationData',{Ptest2,Ttest2},...

'ExecutionEnvironment', 'auto', ...

'ValidationPatience',Inf);

[net,dinfo] = trainNetwork(Ptrain2,Ttrain2,layers,options)

net =

SeriesNetwork with properties: Layers: [8×1 nnet.cnn.layer.Layer] InputNames: {'sequenceinput'} OutputNames: {'classoutput'}

dinfo = struct with fields:

TrainingLoss: [13.8038 13.2205 14.1926 14.5815 14.5815 12.6373 13.8038 12.2484 13.2205 14.7759 14.3870 13.2205 13.9982 12.6373 12.6373 13.2205 13.9982 13.8038 13.9982 13.6094 13.0261 13.2205 13.2205 12.8317 13.9982 12.0540 12.8317 … ] TrainingAccuracy: [13.4146 17.0732 10.9756 8.5366 8.5366 20.7317 13.4146 23.1707 17.0732 7.3171 9.7561 17.0732 12.1951 20.7317 20.7317 17.0732 12.1951 13.4146 12.1951 14.6341 18.2927 17.0732 17.0732 19.5122 12.1951 24.3902 19.5122 13.4146 … ] ValidationLoss: [13.5510 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 8.7683 NaN NaN NaN … ] ValidationAccuracy: [15 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 45 NaN NaN NaN NaN NaN … ] BaseLearnRate: [0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 … ] FinalValidationLoss: 13.5510 FinalValidationAccuracy: 15 OutputNetworkIteration: 3000

yanqi liu 2022-2-17

在 MATLAB Online 中打开

dataset = [0.008,2.5699e+09,-60.397,5.0922e-06,1

0.0084998,2.5698e+09,-59.954,5.0719e-06,1

0.0089996,2.5701e+09,-59.638,5.0947e-06,1

0.0094995,2.5696e+09,-59.246,5.0906e-06,1

0.0099995,2.5701e+09,-58.858,5.0083e-06,1

0.010499,2.5696e+09,-58.538,4.9623e-06,1

0.010999,2.5702e+09,-58.004,4.96e-06,1

0.011499,2.5705e+09,-57.557,4.99e-06,1

0.03,2.5802e+09,-58.372,9.9782e-06,2

0.03055,2.5803e+09,-58.253,9.9844e-06,2

0.0311,2.5801e+09,-58.22,9.9826e-06,2

0.03165,2.5799e+09,-58.096,1.0018e-05,2

0.094902,2.5798e+09,-48.896,1.0022e-05,2

0.095451,2.5805e+09,-48.955,1.0003e-05,2

0.096001,2.5804e+09,-48.721,1.0002e-05,2

0.096551,2.5796e+09,-48.758,1.0011e-05,2

0.097101,2.5799e+09,-48.639,1.0021e-05,2

0.097652,2.5797e+09,-48.812,1.002e-05,2

0.098202,2.5797e+09,-48.622,1.0005e-05,2

0.098752,2.5801e+09,-48.592,1.0009e-05,2

0.099302,2.5796e+09,-48.571,9.989e-06,2

0.099852,2.5804e+09,-48.707,1.0001e-05,2

0.1004,2.5799e+09,-48.712,1.0013e-05,2

0.10095,2.5797e+09,-48.67,1.0008e-05,2

0.1015,2.5804e+09,-48.555,1.0002e-05,2

0.1,2.5904e+09,-56.358,1.4981e-05,3

0.10057,2.59e+09,-55.861,1.4994e-05,3

0.10114,2.5902e+09,-55.625,1.4918e-05,3

0.10171,2.5902e+09,-55.158,1.5037e-05,3

0.10228,2.5901e+09,-54.942,1.5006e-05,3

0.10285,2.5904e+09,-54.477,1.5099e-05,3

0.10342,2.5899e+09,-54.001,1.5055e-05,3

0.10399,2.5896e+09,-53.86,1.5047e-05,3

0.10456,2.5901e+09,-53.599,1.4958e-05,3

0.10513,2.5902e+09,-53.212,1.5004e-05,3

0.1057,2.5896e+09,-52.693,1.5076e-05,3

0.10627,2.59e+09,-52.356,1.4937e-05,3

0.10684,2.5903e+09,-52.188,1.4938e-05,3

0.10741,2.5902e+09,-51.769,1.4918e-05,3

0.10798,2.5897e+09,-51.339,1.4932e-05,3

0.13,2.6001e+09,-58.171,4.9896e-05,4

0.1461,2.6004e+09,-53.86,5.0058e-05,4

0.14725,2.5996e+09,-53.42,4.9917e-05,4

0.1484,2.6002e+09,-53.152,5.0033e-05,4

0.14955,2.6e+09,-52.855,5.0033e-05,4

0.1507,2.5996e+09,-52.568,5.0067e-05,4

0.15185,2.6002e+09,-52.298,5.0028e-05,4

0.153,2.5999e+09,-52.044,5.0002e-05,4

0.15415,2.5996e+09,-51.865,4.992e-05,4

0.1553,2.5998e+09,-51.591,4.997e-05,4

0.15645,2.5996e+09,-51.336,5.0093e-05,4

0.1576,2.6002e+09,-51.19,5.0095e-05,4

0.15875,2.6005e+09,-50.951,4.9887e-05,4

0.1599,2.5995e+09,-50.693,4.9926e-05,4

0.16105,2.6e+09,-50.461,5.012e-05,4

0.1622,2.6004e+09,-50.302,5.0023e-05,4

0.16335,2.6001e+09,-50.048,4.9884e-05,4

0.1645,2.5997e+09,-49.878,5.0005e-05,4

0.16565,2.5997e+09,-49.791,5.0064e-05,4

0.1668,2.6e+09,-49.524,5.0041e-05,4

0.16795,2.6005e+09,-49.388,5.0036e-05,4

0.1691,2.6004e+09,-49.174,5.0053e-05,4

0.17025,2.5995e+09,-48.996,5.0016e-05,4

0.1714,2.5996e+09,-48.934,4.9977e-05,4

0.17255,2.6e+09,-48.782,5.0048e-05,4

0.1737,2.6005e+09,-48.727,5.0117e-05,4

0.17485,2.5997e+09,-48.641,4.9965e-05,4

0.176,2.6003e+09,-48.484,4.9884e-05,4

0.17715,2.5998e+09,-48.39,4.989e-05,4

0.1783,2.6003e+09,-48.42,4.9925e-05,4

0.17945,2.6005e+09,-48.393,4.9896e-05,4

0.1806,2.5997e+09,-48.316,4.9955e-05,4

0.18175,2.6e+09,-48.215,4.99e-05,4

0.1829,2.5997e+09,-48.125,5.0049e-05,4

0.18405,2.5997e+09,-48.28,4.9908e-05,4

0.1852,2.6004e+09,-48.275,5.0074e-05,4

0.18635,2.6001e+09,-48.135,5.0007e-05,4

0.1875,2.6004e+09,-48.314,4.9951e-05,4

0.18865,2.6e+09,-48.185,4.9916e-05,4

0.1898,2.5995e+09,-48.271,5.0097e-05,4

0.19095,2.5996e+09,-48.395,4.9928e-05,4

0.1921,2.6004e+09,-48.396,4.9988e-05,4

0.19325,2.5997e+09,-48.455,5.0102e-05,4

0.1944,2.6e+09,-48.575,5.0096e-05,4

0.19555,2.5998e+09,-48.663,5.0079e-05,4

0.25,2.6037e+09,-75.123,2.0454e-06,5

0.25075,2.6042e+09,-73.315,1.999e-06,5

0.2515,2.6044e+09,-71.653,1.9823e-06,5

0.25225,2.6038e+09,-69.994,2.0639e-06,5

0.253,2.604e+09,-68.391,1.9963e-06,5

0.25375,2.6043e+09,-67.257,1.9299e-06,5

0.2545,2.6039e+09,-66.164,2.0163e-06,5

0.25525,2.6036e+09,-65.639,1.9588e-06,5

0.256,2.6038e+09,-65.321,1.913e-06,5

0.25675,2.6045e+09,-65.28,2.0615e-06,5

0.2575,2.6039e+09,-65.538,1.9969e-06,5

0.25825,2.6041e+09,-66.246,1.9181e-06,5

0.259,2.6041e+09,-67.172,2.0295e-06,5

0.25975,2.6041e+09,-68.554,2.0292e-06,5

0.2605,2.6043e+09,-69.869,2.0366e-06,5

0.26125,2.6043e+09,-71.51,1.9323e-06,5

0.262,2.6036e+09,-73.437,1.9219e-06,5];

sz = size(dataset);

dataset = dataset(randperm(sz(1)),:);

traindata=dataset(:,1:4);

trainlabel=categorical(dataset(:,5));

classes = unique(trainlabel);

numClasses = numel(unique(trainlabel));

PD = 0.80 ;

Ptrain = []; Ttrain = [];

Ptest = []; Ttest = [];

for i = 1 : length(classes)

indi = find(trainlabel==classes(i));

indi = indi(randperm(length(indi)));

indj = round(length(indi)*PD);

Ptrain = [Ptrain; traindata(indi(1:indj),:)]; Ttrain = [Ttrain; trainlabel(indi(1:indj),:)];

Ptest = [Ptest; traindata(indi(1+indj:end),:)]; Ttest = [Ttest; trainlabel(indi(1+indj:end),:)];

end

Ptrain2 = []; Ttrain2 = [];

Ptest2 = []; Ttest2 = [];

Ttrain2=categorical(Ttrain);

Ttest2=categorical(Ttest);

for i = 1 : size(Ptrain,1)

Ptrain2{i} = Ptrain(i,:)';

%Ttrain2{i} = Ttrain(i);

end

for i = 1 : size(Ptest,1)

Ptest2{i} = Ptest(i,:)';

%Ttest2{i} = Ttest(i);

end

layers = [sequenceInputLayer(4)

convolution1dLayer(3,100,Padding="causal")

reluLayer

globalMaxPooling1dLayer

dropoutLayer

fullyConnectedLayer(50)

dropoutLayer

fullyConnectedLayer(5)

softmaxLayer

classificationLayer];

options = trainingOptions('adam', ...

'MaxEpochs',1000, ...

'Shuffle','every-epoch', ...

'Verbose',false, ...

'ValidationData',{Ptest2,Ttest2},...

'ExecutionEnvironment', 'auto', ...

'ValidationPatience',Inf);

[net,dinfo] = trainNetwork(Ptrain2,Ttrain2,layers,options);

figure; plot(dinfo.TrainingAccuracy);

Med Future 2022-2-17

@yanqi liu this is not training curve

yanqi liu 2022-2-18

yes，sir，here on web，we can not see the plot curve，so we get the train status info and plot it

this picture is train acc curve by stats info structure

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