Training the final ResNet on the optimal hyperparamter combination seems to work, but there is an error afterwards when evaluating the model.
Training final model using optimal parameters
|00%|██████████| 58/58 [00:01<00:00, 29.46it/s]
-00%|██████████| 58/58 [00:01<00:00, 30.69it/s]
Calculating predictions on all train data.../s]
\00%|██████████| 59/59 [00:00<00:00, 71.76it/s]
Error in rbind(deparse.level, ...): numbers of columns of arguments do not match
Calculating covariate summary @ 2023-10-05 09:19:13
Training the Transformer throws an exception right on the first hyperparamter combination.
Running hyperparameter search for Transformer model
2023-10-05 05:29:55 [Main thread] INFO DeepPatientLevelPrediction Running hyperparameter combination no 1
2023-10-05 05:29:55 [Main thread] INFO DeepPatientLevelPrediction HyperParameters:
2023-10-05 05:29:55 [Main thread] INFO DeepPatientLevelPrediction numBlocks 2 | dimToken 256 | dimOut 1 | numHeads 4 | dimHidden 341 | attDropout 0.25 | ffnDropout 0.2 | resDropout 0.15 | estimator.weightDecay 0.001
2023-10-05 05:29:55 [Main thread] INFO DeepPatientLevelPrediction Max fold: 3
2023-10-05 05:29:56 [Main thread] ERROR PatientLevelPrediction 3 The size of tensor a (2) must match the size of tensor b (256) at non-singleton dimension 1
2023-10-05 05:29:56 [Main thread] INFO PatientLevelPrediction Calculating covariate summary @ 2023-10-05 05:29:56
Training the final
ResNet
on the optimal hyperparamter combination seems to work, but there is an error afterwards when evaluating the model.Training the
Transformer
throws an exception right on the first hyperparamter combination.