It seems that k-fold cross validation in convn net is not taken seriously due to huge running time of the neural network. I have a small data-set and I am interested in doing k-fold cross validation using the example given here. Is it possible? Thanks.
K fold cross validation using keras
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If you are using images with data generators, here's one way to do 10-fold cross-validation with Keras and scikit-learn. The strategy is to copy the files to
training
,validation
, andtest
subfolders according to each fold.In your predict() function, if you are using a data generator, the only way I could find to keep the predictions in the same order when testing was to use a
batch_size
of1
:With this code, I was able to do 10-fold cross-validation using data generators (so I did not have to keep all files in memory). This can be a lot of work if you have millions of images and the
batch_size = 1
could be a bottleneck if your test set is large, but for my project this worked well.