more output in mhcflurry/train_presentation_models_command.py
@@ -113,11 +113,22 @@ def main(args):
processing_predictor_without_flanks = Class1ProcessingPredictor.load(
args.processing_predictor_without_flanks)
+ print("Loaded affinity predictor", affinity_predictor)
+ print(
+ "Loaded processing_predictor_with_flanks",
+ processing_predictor_with_flanks)
+ print("Loaded processing_predictor_without_flanks",
+ processing_predictor_without_flanks)
+
predictor = Class1PresentationPredictor(
affinity_predictor=affinity_predictor,
processing_predictor_with_flanks=processing_predictor_with_flanks,
processing_predictor_without_flanks=processing_predictor_without_flanks)
+ # We want to predict using an optimized Class1AffinityPredictor but
+ # save the presentation models using an un-optimized Class1AffinityPredictor,
+ # since there seems to be issues saving and loading giant tensorflow
+ # graphs (which is what the optimization routine produces).
print("Before fit: saving affinity and processing predictors.")
predictor.save(
args.out_models_dir,
