Error using trainNetwork Invalid training data. For cell array input, responses must be an N-by-1 cell array of sequences, where N is the number of sequences. The spatial and

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Mo'ath 2024-5-1

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2113946-error-using-trainnetwork-invalid-training-data-for-cell-array-input-responses-must-be-an-n-by-1-ce

评论： Mo'ath 2024-5-1

采纳的回答： Cris LaPierre

CycleAgeingData.mat

在 MATLAB Online 中打开

Error using trainNetwork

Invalid training data. For cell array input, responses must be an N-by-1 cell array of sequences, where N is the number of sequences. The spatial and channel dimensions of the sequences must be the same as the output size of the last layer (1).

% Edit - running code here
load CycleAgeingData.mat
numHiddenUnits = 50;
inputSize1 = size(Data{1},1)
inputSize1 = 7
% layers = [
%     sequenceInputLayer(numChannels)
%     lstmLayer(128)
%     fullyConnectedLayer(numChannels)
%     regressionLayer];
% 
layers = [ ...
    sequenceInputLayer(inputSize1)
    lstmLayer(50, 'OutputMode', 'sequence')
    fullyConnectedLayer(7)
    dropoutLayer(0.011547480894612765)
    fullyConnectedLayer(1)
    regressionLayer];
% layersLSTM = [ ...
%     sequenceInputLayer(inputSize1)
%     lstmLayer(numHiddenUnits)
%     fullyConnectedLayer(1)
%     regressionLayer
%     ];
% cell1x = num2cell(features', 1)';
% targets=cap6/cap6(1)
% cell1yB = num2cell(targets);
numChannels = size(Data{1},1)
numChannels = 7
numObservations = numel(Data);
idxTrain = 1:floor(0.7*numObservations);
idxval = floor(0.7*numObservations)+1:numObservations-2
idxval = 1x3
    11    12    13
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
idxTest = floor(0.7*numObservations)+4:numObservations;
dataTrain = Data(idxTrain);
dataVal = Data(idxval)
dataVal = 3x1 cell array
    {7x10 double}
    {7x32 double}
    {7x4  double}
dataTest = Data(idxTest);
%trainindx=(1:24)
trainindx = 1x24
     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18    19    20    21    22    23    24
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
%validindx=(25:29)
validindx = 1x5
    25    26    27    28    29
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
%testindx=(30:34)    
testindx = 1x5
    30    31    32    33    34
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
traincell2yB = target(idxTrain, :);
valcell2yB = target(idxval, :);
testcell2yB = target(idxTest, :);
options = trainingOptions('rmsprop', ...
    'MaxEpochs', 1500, ...
    'MiniBatchSize', 50, ...
    'InitialLearnRate', 0.00036008553147273947, ...
    'LearnRateSchedule', 'piecewise', ...
    'LearnRateDropPeriod', 125, ...
    'LearnRateDropFactor', 0.02, ...
    'Shuffle', 'every-epoch', ...
    'ValidationData', {dataVal, valcell2yB}, ...
    'ValidationFrequency', 50, ...
    'Verbose', 1, ...
    'Plots', 'training-progress');
% options = trainingOptions('rmsprop', ...
%     'InitialLearnRate', 0.001, ...
%     'MaxEpochs',500, ...
%     'MiniBatchSize',50, ...
%     'Plots','training-progress', 'ValidationData', {valcell1x, valcell1yB});
% options = trainingOptions('adam', ...
%     'InitialLearnRate', 0.001, ...
%     'MaxEpochs',500, ...
%     'MiniBatchSize',50, ...
%     'Plots','training-progress', 'ValidationData', {valcell1x, valcell1yB});
netLSTM1 = trainNetwork(dataTrain, traincell2yB, layers, options);
Error using trainNetwork (line 191)
Invalid training data. For cell array input, responses must be an N-by-1 cell array of sequences, where N is the number of sequences. The spatial and channel dimensions of the sequences must be the same as the output size of the last layer (1).

Here is my data

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Mo'ath 2024-5-1

@Cris LaPierre i think Each cell has the format M x L, where M is the number of features, that remains fixed for all the cells, and L is the variable length of the training data. and the i have use number of features as inputsize

Cris LaPierre 2024-5-1

Sorry, I now understand you are trying to perform sequence-to-sequence regression. That changes some things. You might find this example useful. Sequence to Sequence Regression using Deep Learning

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Answer 1

Cris LaPierre 2024-5-1

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2113946-error-using-trainnetwork-invalid-training-data-for-cell-array-input-responses-must-be-an-n-by-1-ce#answer_1450931

I believe the issue is because the sequence length is not the same in each sequence.

There are 2 reasons for this. First, your response vectors are Nx1, but need to be transponsed to 1xN so that the training and response sequences are the same length. Second, one of your sequences has a different response length.

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Cris LaPierre 2024-5-1

在 MATLAB Online 中打开

CycleAgeingData.mat

Here's a working example. I had to disable the plots option to run the code here.

load CycleAgeingData.mat
% Transpose response data
target = cellfun(@transpose,target,'UniformOutput',false);
% Hack to get code to run here. 
% Correct length difference
Data{5} = Data{5}(:,1:length(target{5}));
numHiddenUnits = 50;
numChannels = size(Data{1},1)
numChannels = 7
numResponses = size(target{1},1)
numResponses = 1
layers = [ ...
    sequenceInputLayer(numChannels)
    lstmLayer(numHiddenUnits, 'OutputMode', 'sequence')
    fullyConnectedLayer(7)
    dropoutLayer(0.011547480894612765)
    fullyConnectedLayer(numResponses)
    regressionLayer];
numObservations = numel(Data);
idxTrain = 1:floor(0.7*numObservations);
idxval = floor(0.7*numObservations)+1:numObservations-2
idxval = 1x3
    11    12    13
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
idxTest = floor(0.7*numObservations)+4:numObservations;
dataTrain = Data(idxTrain);
dataVal = Data(idxval)
dataVal = 3x1 cell array
    {7x10 double}
    {7x32 double}
    {7x4  double}
dataTest = Data(idxTest);
   
traincell2yB = target(idxTrain, :);
valcell2yB = target(idxval, :);
testcell2yB = target(idxTest, :);
options = trainingOptions('adam', ...
    'MaxEpochs', 1500, ...
    'MiniBatchSize', 50, ...
    'InitialLearnRate', 0.00036008553147273947, ...
    'LearnRateSchedule', 'piecewise', ...
    'LearnRateDropPeriod', 125, ...
    'LearnRateDropFactor', 0.02, ...
    'Shuffle', 'every-epoch', ...
    'ValidationData', {dataVal, valcell2yB}, ...
    'ValidationFrequency', 50, ...
    'Verbose', 1);
    % 'Plots', 'training-progress');
netLSTM1 = trainNetwork(dataTrain, traincell2yB, layers, options)
Training on single CPU.
|======================================================================================================================|
|  Epoch  |  Iteration  |  Time Elapsed  |  Mini-batch  |  Validation  |  Mini-batch  |  Validation  |  Base Learning  |
|         |             |   (hh:mm:ss)   |     RMSE     |     RMSE     |     Loss     |     Loss     |      Rate       |
|======================================================================================================================|
|       1 |           1 |       00:00:00 |         3.63 |         2.64 |       6.6028 |       3.4722 |          0.0004 |
|      50 |          50 |       00:00:00 |         2.11 |         1.67 |       2.2178 |       1.4003 |          0.0004 |
|     100 |         100 |       00:00:01 |         1.38 |         1.24 |       0.9512 |       0.7710 |          0.0004 |
|     150 |         150 |       00:00:01 |         1.23 |         1.18 |       0.7562 |       0.6937 |      7.2017e-06 |
|     200 |         200 |       00:00:02 |         1.22 |         1.18 |       0.7454 |       0.6929 |      7.2017e-06 |
|     250 |         250 |       00:00:02 |         1.20 |         1.18 |       0.7247 |       0.6919 |      7.2017e-06 |
|     300 |         300 |       00:00:03 |         1.21 |         1.18 |       0.7364 |       0.6919 |      1.4403e-07 |
|     350 |         350 |       00:00:03 |         1.21 |         1.18 |       0.7336 |       0.6919 |      1.4403e-07 |
|     400 |         400 |       00:00:03 |         1.21 |         1.18 |       0.7288 |       0.6919 |      2.8807e-09 |
|     450 |         450 |       00:00:04 |         1.19 |         1.18 |       0.7068 |       0.6919 |      2.8807e-09 |
|     500 |         500 |       00:00:04 |         1.19 |         1.18 |       0.7081 |       0.6919 |      2.8807e-09 |
|     550 |         550 |       00:00:05 |         1.19 |         1.18 |       0.7128 |       0.6919 |      5.7614e-11 |
|     600 |         600 |       00:00:05 |         1.23 |         1.18 |       0.7504 |       0.6919 |      5.7614e-11 |
|     650 |         650 |       00:00:06 |         1.19 |         1.18 |       0.7118 |       0.6919 |      1.1523e-12 |
|     700 |         700 |       00:00:06 |         1.20 |         1.18 |       0.7216 |       0.6919 |      1.1523e-12 |
|     750 |         750 |       00:00:06 |         1.20 |         1.18 |       0.7177 |       0.6919 |      1.1523e-12 |
|     800 |         800 |       00:00:07 |         1.22 |         1.18 |       0.7394 |       0.6919 |      2.3045e-14 |
|     850 |         850 |       00:00:07 |         1.21 |         1.18 |       0.7283 |       0.6919 |      2.3045e-14 |
|     900 |         900 |       00:00:07 |         1.20 |         1.18 |       0.7226 |       0.6919 |      4.6091e-16 |
|     950 |         950 |       00:00:08 |         1.20 |         1.18 |       0.7142 |       0.6919 |      4.6091e-16 |
|    1000 |        1000 |       00:00:08 |         1.20 |         1.18 |       0.7176 |       0.6919 |      4.6091e-16 |
|    1050 |        1050 |       00:00:09 |         1.18 |         1.18 |       0.7013 |       0.6919 |      9.2182e-18 |
|    1100 |        1100 |       00:00:09 |         1.21 |         1.18 |       0.7280 |       0.6919 |      9.2182e-18 |
|    1150 |        1150 |       00:00:10 |         1.20 |         1.18 |       0.7255 |       0.6919 |      1.8436e-19 |
|    1200 |        1200 |       00:00:10 |         1.19 |         1.18 |       0.7097 |       0.6919 |      1.8436e-19 |
|    1250 |        1250 |       00:00:10 |         1.20 |         1.18 |       0.7196 |       0.6919 |      1.8436e-19 |
|    1300 |        1300 |       00:00:11 |         1.19 |         1.18 |       0.7129 |       0.6919 |      3.6873e-21 |
|    1350 |        1350 |       00:00:11 |         1.19 |         1.18 |       0.7120 |       0.6919 |      3.6873e-21 |
|    1400 |        1400 |       00:00:12 |         1.21 |         1.18 |       0.7278 |       0.6919 |      7.3746e-23 |
|    1450 |        1450 |       00:00:12 |         1.19 |         1.18 |       0.7091 |       0.6919 |      7.3746e-23 |
|    1500 |        1500 |       00:00:12 |         1.20 |         1.18 |       0.7189 |       0.6919 |      7.3746e-23 |
|======================================================================================================================|
Training finished: Max epochs completed.
netLSTM1 = 
  SeriesNetwork with properties:

         Layers: [6x1 nnet.cnn.layer.Layer]
     InputNames: {'sequenceinput'}
    OutputNames: {'regressionoutput'}

Mo'ath 2024-5-1

@Cris LaPierre, i am try this solution and i get the same error but i am agrre with you i think the error come from the lengthsequance of the target output

Mo'ath 2024-5-1

@Cris LaPierre, thank you for yout time and support

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Error using trainNetwork Invalid training data. For cell array input, responses must be an N-by-1 cell array of sequences, where N is the number of sequences. The spatial and

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Error using trainNetwork Invalid training data. For cell array input, responses must be an N-by-1 cell array of sequences, where N is the number of sequences. The spatial and

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