diff --git a/trainer/src/Trainer.cpp b/trainer/src/Trainer.cpp index 9f41dfc2a8111ea6431f7473c4bba33dd0e88418..76bf1bda3207f4d1c69d2d1ccdc9dd50fced1664 100644 --- a/trainer/src/Trainer.cpp +++ b/trainer/src/Trainer.cpp @@ -400,10 +400,6 @@ void Trainer::doStepTrain() if (TI.lastActionWasPredicted[normalStateName]) { - if (ProgramParameters::debug) - { - fprintf(stderr, "Updating neural network \'%s\'\n", tm.getCurrentClassifier()->name.c_str()); - } if (newCost >= lastCost) { loss = tm.getCurrentClassifier()->trainOnExample(pendingFD[tm.getCurrentClassifier()->name], tm.getCurrentClassifier()->getActionIndex("EPSILON")); @@ -411,8 +407,14 @@ void Trainer::doStepTrain() else { loss = tm.getCurrentClassifier()->trainOnExample(pendingFD[tm.getCurrentClassifier()->name], tm.getCurrentClassifier()->getActionIndex(trainConfig.getCurrentStateHistory().top())); + + if (ProgramParameters::debug) + fprintf(stderr, "Updating neural network \'%s\', gold=\'%s\'\n", tm.getCurrentClassifier()->name.c_str(), trainConfig.getCurrentStateHistory().top().c_str()); } + if (ProgramParameters::debug) + fprintf(stderr, "Updating neural network \'%s\'\n", tm.getCurrentClassifier()->name.c_str()); + TI.addTrainLoss(tm.getCurrentClassifier()->name, loss); }