diff --git a/higher/smart_aug/dataug.py b/higher/smart_aug/dataug.py
index 4df0e15..93cc598 100755
--- a/higher/smart_aug/dataug.py
+++ b/higher/smart_aug/dataug.py
@@ -127,7 +127,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
             Returns:
                 Tensor : Batch of tranformed data.
         """
-        self._samples = []
+        self._samples = torch.Tensor([])
         if self._data_augmentation:# and TF.random.random() < 0.5:
             device = x.device
             batch_size, h, w = x.shape[0], x.shape[2], x.shape[3]
@@ -145,12 +145,9 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
                 self._distrib = (mix_dist*prob+(1-mix_dist)*uniforme_dist)#.softmax(dim=1) #Mix distrib reel / uniforme avec mix_factor
 
             cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_tf), device=device)*self._distrib)
-            
-            for _ in range(self._N_seqTF):
-                
-                sample = cat_distrib.sample()
-                self._samples.append(sample)
+            self._samples=cat_distrib.sample([self._N_seqTF])
 
+            for sample in self._samples:
                 ## Transformations ##
                 x = self.apply_TF(x, sample)
         return x
@@ -448,7 +445,6 @@ class Data_augV7(nn.Module): #Proba sequentielles
 
             x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles)
             
-
             ## Echantillonage ##
             uniforme_dist = torch.ones(1,self._nb_TF_sets,device=device).softmax(dim=1)
 
@@ -707,14 +703,15 @@ class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh + rapide
 
             x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles)
             
-            for _ in range(self._N_seqTF):
-                ## Echantillonage ## == sampled_ops = np.random.choice(transforms, N)
-                uniforme_dist = torch.ones(1,self._nb_tf,device=device).softmax(dim=1)
-                cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_tf), device=device)*uniforme_dist)
-                sample = cat_distrib.sample()
+            ## Echantillonage ## == sampled_ops = np.random.choice(transforms, N)
+            uniforme_dist = torch.ones(1,self._nb_tf,device=device).softmax(dim=1)
+            cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_tf), device=device)*uniforme_dist)
+            self._samples=cat_distrib.sample([self._N_seqTF])
 
+            for sample in self._samples:
                 ## Transformations ##
                 x = self.apply_TF(x, sample)
+
         return x
 
     def apply_TF(self, x, sampled_TF):
@@ -964,7 +961,7 @@ class Augmented_model(nn.Module):
 
                 model.step(loss)
                 
-            Lacking epoch informations, this does not support LR scheduler and delayed meta-optimisation(Meta-optimizer: epoch_start>1).
+            Does not support LR scheduler.
             
             See ''run_simple_smartaug'' for a complete example.