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Error when using lstm with cnn

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Mohammed Firas
Mohammed Firas 2024-7-19，17:42
评论： Walter Roberson 2024-7-19，18:31
XTrain = single(DL_input_reshaped(:,1,1,Training_Ind));
YTrain = single(DL_output_reshaped(1,1,:,Training_Ind)); XValidation = single(DL_input_reshaped(:,1,1,Validation_Ind));
YValidation = single(DL_output_reshaped(1,1,:,Validation_Ind));
YValidation_un = single(DL_output_reshaped_un);
%% DL Model definition with adjusted pooling and convolution layers layers = [ imageInputLayer([size(XTrain,1), 1, 1],'Name','input','Normalization','none')
convolution2dLayer(3, 64, 'Padding', 'same', 'Name', 'conv1')
batchNormalizationLayer('Name', 'bn1')
reluLayer('Name', 'relu1')
maxPooling2dLayer([3,1], 'Stride', [3,1], 'Name', 'maxpool1')
convolution2dLayer(3, 128, 'Padding', 'same', 'Name', 'conv2')
batchNormalizationLayer('Name', 'bn2')
reluLayer('Name', 'relu2')
maxPooling2dLayer([3,1], 'Stride', [3,1], 'Name', 'maxpool2')
convolution2dLayer(3, 256, 'Padding', 'same', 'Name', 'conv3')
batchNormalizationLayer('Name', 'bn3')
reluLayer('Name', 'relu3')
maxPooling2dLayer([3,1], 'Stride', [3,1], 'Name', 'maxpool3')
flattenLayer('Name', 'flatten') % Flatten to 1D per sample
lstmLayer(200, 'OutputMode', 'last', 'Name', 'lstm1') % LSTM layer
fullyConnectedLayer(512, 'Name', 'fc1')
reluLayer('Name', 'relu4')
dropoutLayer(0.5, 'Name', 'dropout1')
fullyConnectedLayer(1024, 'Name', 'fc2')
reluLayer('Name', 'relu5')
dropoutLayer(0.5, 'Name', 'dropout2')
fullyConnectedLayer(2048, 'Name', 'fc3')
reluLayer('Name', 'relu6')
dropoutLayer(0.5, 'Name', 'dropout3')
fullyConnectedLayer(size(YTrain,3), 'Name', 'fc4')
regressionLayer('Name', 'output') ];
options = trainingOptions('rmsprop', ...
.
.
.
so this error is appear to me
((error useing trainNetwork Invalid training data.
The output size (1024) of the last layer does not match the response size (1).))
so the size or XTrain and YTrain is (features x 1 x 1 x minbatchsize)
  1 个评论
Walter Roberson
Walter Roberson 2024-7-19，18:31
XTrain = single(DL_input_reshaped(:,1,1,Training_Ind));
You are training with (something by 1 by 1 by something-else) data.
The networks probably expect (something by something-else) -- 2D data instead of 4D data.

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