pytorch-template

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yeah please. Or any suggestions on how to modify the code if I want to use k-fold cross validation please?

Feb 23 '20 07:02 LumosWC

Sorry for late response. K-fold CV can be done by modifying BaseDataLoader.

Current implementation of _split_sampler use first n samples of dataset as validation set. Modify this function to take additional argument, fold_idx and to use specified part of dataset. To be more specific, this part should be changed to following.

valid_idx = idx_full[len_valid*fold_idx, len_valid*(fold_idx+1)]
train_idx = np.delete(idx_full, np.arange(0, len_valid))

Then, add the fold_idx to BaseDataLoader.__init__ and YourDataLoader.__init__, specify it in the config file, under data_loader.args.

Apr 16 '20 02:04 SunQpark

@SunQpark I think you mean

valid_idx = idx_full[len_valid * fold_idx : len_valid * (fold_idx + 1)]
train_idx = np.delete(idx_full, np.arange(len_valid * fold_idx, len_valid * (fold_idx + 1)))

May 27 '20 11:05 hsinlichu

I have implemented cross validation to my Pytorch-Template See #88

Mar 30 '21 15:03 deeperlearner