diff --git a/src/Model.py b/src/Model.py
index 1d12c81aaa490f34c3a49d9bdb7bf954ffed5643..e987acf2b0ae2a436e4b6dda8ec69f6b66b6910f 100644
--- a/src/Model.py
+++ b/src/Model.py
@@ -260,14 +260,14 @@ class Model:
 			ctcInput = evalRes[1]
 			evalList = self.lossPerElement
 			feedDict = {self.savedCtcInput : ctcInput, self.gtTexts : sparse, self.seqLen : [Model.maxTextLen] * numBatchElements, self.is_train: False}
-			#lossVals = self.sess.run(evalList, feedDict)
-			#probs = np.exp(-lossVals)
+			lossVals = self.sess.run(evalList, feedDict)
+			probs = np.exp(-lossVals)
 
 		# dump the output of the NN to CSV file(s)
 		if self.dump:
 			self.dumpNNOutput(evalRes[1])
 
-		return (texts)
+		return (texts, probs)
 
 
 	def save(self):
diff --git a/src/main.py b/src/main.py
index 764532068023b035920293900b09db68c6652303..1e712830526d579f001113c4e4a2b3a6de8318ca 100644
--- a/src/main.py
+++ b/src/main.py
@@ -40,7 +40,7 @@ def train(model, loader):
 
 		# validate
 		charErrorRate = validate(model, loader)
-		
+
 		# if best validation accuracy so far, save model parameters
 		if charErrorRate < bestCharErrorRate:
 			print('Character error rate improved, save model')
@@ -71,8 +71,8 @@ def validate(model, loader):
 		print('Batch:', iterInfo[0],'/', iterInfo[1])
 		batch = loader.getNext()
 		(recognized, _) = model.inferBatch(batch)
-		
-		print('Ground truth -> Recognized')	
+
+		print('Ground truth -> Recognized')
 		for i in range(len(recognized)):
 			numWordOK += 1 if batch.gtTexts[i] == recognized[i] else 0
 			numWordTotal += 1
@@ -80,7 +80,7 @@ def validate(model, loader):
 			numCharErr += dist
 			numCharTotal += len(batch.gtTexts[i])
 			print('[OK]' if dist==0 else '[ERR:%d]' % dist,'"' + batch.gtTexts[i] + '"', '->', '"' + recognized[i] + '"')
-	
+
 	# print validation result
 	charErrorRate = numCharErr / numCharTotal
 	wordAccuracy = numWordOK / numWordTotal
@@ -115,14 +115,14 @@ def main():
 	elif args.wordbeamsearch:
 		decoderType = DecoderType.WordBeamSearch
 
-	# train or validate on IAM dataset	
+	# train or validate on IAM dataset
 	if args.train or args.validate:
 		# load training data, create TF model
 		loader = DataLoader(FilePaths.fnTrain, Model.batchSize, Model.imgSize, Model.maxTextLen)
 
 		# save characters of model for inference mode
 		open(FilePaths.fnCharList, 'w').write(str().join(loader.charList))
-		
+
 		# save words contained in dataset into file
 		open(FilePaths.fnCorpus, 'w').write(str(' ').join(loader.trainWords + loader.validationWords))
 
@@ -143,4 +143,3 @@ def main():
 
 if __name__ == '__main__':
 	main()
-