diff --git a/quapy/method/neural.py b/quapy/method/neural.py
index 4decc74..bb59f97 100644
--- a/quapy/method/neural.py
+++ b/quapy/method/neural.py
@@ -87,15 +87,14 @@ class QuaNetTrainer(BaseQuantifier):
         train_posteriors = self.learner.predict_proba(train_data.instances)
 
         # turn instances' original representations into embeddings
-        valid_data.instances = self.learner.transform(valid_data.instances)
-        train_data.instances = self.learner.transform(train_data.instances)
+        valid_data_embed = LabelledCollection(self.learner.transform(valid_data.instances), valid_data.labels, self._classes_)
+        train_data_embed = LabelledCollection(self.learner.transform(train_data.instances), train_data.labels, self._classes_)
 
         self.quantifiers = {
             'cc': CC(self.learner).fit(None, fit_learner=False),
             'acc': ACC(self.learner).fit(None, fit_learner=False, val_split=valid_data),
             'pcc': PCC(self.learner).fit(None, fit_learner=False),
             'pacc': PACC(self.learner).fit(None, fit_learner=False, val_split=valid_data),
-            # 'emq': EMQ(self.learner).fit(classifier_data, fit_learner=False),
         }
         if classifier_data is not None:
             self.quantifiers['emq'] = EMQ(self.learner).fit(classifier_data, fit_learner=False)
@@ -110,9 +109,9 @@ class QuaNetTrainer(BaseQuantifier):
         nQ = len(self.quantifiers)
         nC = data.n_classes
         self.quanet = QuaNetModule(
-            doc_embedding_size=train_data.instances.shape[1],
+            doc_embedding_size=train_data_embed.instances.shape[1],
             n_classes=data.n_classes,
-            stats_size=nQ*nC, #+ 2*nC*nC,
+            stats_size=nQ*nC,
             order_by=0 if data.binary else None,
             **self.quanet_params
         ).to(self.device)
@@ -124,8 +123,8 @@ class QuaNetTrainer(BaseQuantifier):
         checkpoint = self.checkpoint
 
         for epoch_i in range(1, self.n_epochs):
-            self.epoch(train_data, train_posteriors, self.tr_iter, epoch_i, early_stop, train=True)
-            self.epoch(valid_data, valid_posteriors, self.va_iter, epoch_i, early_stop, train=False)
+            self.epoch(train_data_embed, train_posteriors, self.tr_iter, epoch_i, early_stop, train=True)
+            self.epoch(valid_data_embed, valid_posteriors, self.va_iter, epoch_i, early_stop, train=False)
 
             early_stop(self.status['va-loss'], epoch_i)
             if early_stop.IMPROVED: