Error when using lstm with cnn

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)

[Walter Roberson]

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.