Hi,
I have trained a deep learning model on a datastore. The datastore is split into three subsets; train, test and validate.
[split_idx] = round(length(ds.Files)*(split/100));
train_idx = [1:split_idx(1)];
val_idx = [split_idx(1):split_idx(1)+split_idx(2)];
test_idx = [split_idx(1)+split_idx(2):split_idx(1)+split_idx(2)+split_idx(3)];
dstrain = subset(ds,train_idx);
dsval = subset(ds,val_idx);
dstest = subset(ds,test_idx);
After training the model I want to generate a confusion chart. However my datastore is a tall cell array, the first cell column is data and the second column is a lable. As brace indexing isn't allowed with tall data (myarray{..,..}{..,..}) I cannot come up with a simple way to extract the lablels from the tall array. Am I missing something obvious? I will attach a sample of the data.
net = trainNetwork(dstrain,layers,options);
YPred = classify(net,dstest,'MiniBatchSize',70);
Unfortunatly I received the data in a cell column format so can't just recompute to an easier format. After importing the sample data
should generate a sample; 4X1 tall cell where each row is an x*2 cell array, the first column being data and the second column being the ground truth label which I would like to extract as a variable for confusionchart().
Kind regards,
Christopher
