Error when using lstm with cnn
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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 commentaire
Walter Roberson
le 19 Juil 2024 à 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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