I want to build seven inputs, one output network. (11 classes; 1, 2, ... , 11)
I used the filedatastore and tranformed datastore type.
My CNN model's input layer is 3D image arrays for each input.
One single mat file inculded Cell array (11x8); (7 double arrays (inputs) and categorical type (label))
{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[1 ]}
{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[2 ]}
{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[3 ]}
{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[4 ]}
{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[5 ]}
{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[6 ]}
{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[7 ]}
{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[8 ]}
{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[9 ]}
{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[10]}
{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[11]}
When I load the datastore, I used below code.
TrainDS=Simple_combined([File_path 'Train']);
function [trainingDatastore]=Simple_combined(folder)
tempdatastore = datastore(folder,'ReadFcn',@load,'IncludeSubfolders',1,'Type','file','FileExtensions','.mat');
trainingDatastore = transform(tempdatastore,@rearrangeData);
function out = rearrangeData(ds)
out = ds.CombinedCell;
end
end
I tried to train my model using this code.
[trainedNet, traininfo]= trainNetwork(TrainDS,lgraph,options);
The error message was this: Invalid training data. The output size (11) of the last layer does not match the number of classes (1).
I also debuged the code.
I found the "ResponseSize" and "OutputSizes" are different after "iCreateMISOTrainingDataDispatcher" in the "trainNetwork" function.
trainingDispatcher =
GeneralDatastoreDispatcher with properties:
ImageSize: {[10 9 640 1] [10 9 640 1] [10 9 640 1] [10 9 640 1] [10 9 640 1] [10 9 640 1] [10 9 640 1]}
ResponseSize: {[1 1 1 1]}
NumObservations: Inf
ResponseMetaData: [1×1 nnet.internal.cnn.response.ClassificationMetaData]
IsDone: 0
Precision: [1×1 nnet.internal.cnn.util.Precision]
EndOfEpoch: 'discardLast'
ShuffleOption: 'once'
MiniBatchSize: 10
DataLoader: [1×1 nnet.internal.cnn.DataLoader]
DataTranslator: [1×1 nnet.internal.cnn.util.DataTranslator]
CollateFunctions: [1×1 struct]
InputSizes: {[10 9 640 1] [10 9 640 1] [10 9 640 1] [10 9 640 1] [10 9 640 1] [10 9 640 1] [10 9 640 1]}
OutputSizes: {[1 1 1 11]}
UnpackCellInputOutput: 0
CanPreserveOrder: 0
CatAndSliceStrategy: [1×1 nnet.internal.cnn.util.NDArrayCatAndSliceStrategy]
RunInBackground: 0
What should I do? Please help me
