diff --git a/higher/dataug.py b/higher/dataug.py
index 2e9b99c..99f0053 100644
--- a/higher/dataug.py
+++ b/higher/dataug.py
@@ -709,17 +709,38 @@ class Data_augV6(nn.Module): #Optimisation sequentielle
         self._fixed_mag = fixed_mag
         
         self._TF_set_size=3
-        #if self._TF_set_size>self._nb_tf:
-        #    print("Warning : TF sets size higher than number of TF. Reducing set size to %d"%self._nb_tf)
-        #    self._TF_set_size=self._nb_tf
-        assert self._nb_tf>=self._TF_set_size
-        self._TF_sets=[]
-        for i in range(1,self._nb_tf):
-            for j in range(i,self._nb_tf):
-                if i!=j:
-                    self._TF_sets+=[torch.tensor([0, i, j])]
-        #print(self._TF_sets)
-        #self._TF_sets=[torch.tensor([0, i, j]) for i in range(1,self._nb_tf)] #All VS Identity
+        self._fixed_TF=[0]
+        assert self._TF_set_size>=len(self._fixed_TF)
+
+        if self._TF_set_size>self._nb_tf:
+            print("Warning : TF sets size higher than number of TF. Reducing set size to %d"%self._nb_tf)
+            self._TF_set_size=self._nb_tf
+        
+        ## Genenerate TF sets ##
+        if self._TF_set_size==len(self._fixed_TF): 
+            print("Warning : using only fixed set of TF : ", self._fixed_TF)
+            self._TF_sets=torch.tensor([self._fixed_TF])
+        else: 
+            def generate_TF_sets(n_TF, set_size, idx_prefix=[]):
+                TF_sets=[]
+                print(set_size, idx_prefix)
+                if len(idx_prefix)!=0:
+                    if set_size>2:
+                        for i in range(idx_prefix[-1]+1, n_TF):
+                            TF_sets += generate_TF_sets(n_TF=n_TF, set_size=set_size-1, idx_prefix=idx_prefix+[i])
+                    else:
+                        #if i not in idx_prefix:
+                        TF_sets+=[torch.tensor(idx_prefix+[i]) for i in range(idx_prefix[-1]+1, n_TF)]
+                elif set_size>1:
+                     for i in range(0, n_TF):
+                        TF_sets += generate_TF_sets(n_TF=n_TF, set_size=set_size, idx_prefix=[i])
+                else:
+                    TF_sets+=[torch.tensor([i]) for i in range(0, n_TF)]
+                return TF_sets
+
+            self._TF_sets=generate_TF_sets(self._nb_tf, self._TF_set_size, self._fixed_TF)
+        
+        ## Plan TF learning schedule ##
         self._TF_schedule = [list(range(len(self._TF_sets))) for _ in range(self._N_seqTF)]
         for n_tf in range(self._N_seqTF) :
             TF.random.shuffle(self._TF_schedule[n_tf])
diff --git a/higher/res/Aug_mod(Data_augV6(Uniform-18TF(3)x1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.png b/higher/res/Aug_mod(Data_augV6(Uniform-18TF(3)x1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.png
index e8f0dab..99fe2e5 100644
Binary files a/higher/res/Aug_mod(Data_augV6(Uniform-18TF(3)x1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.png and b/higher/res/Aug_mod(Data_augV6(Uniform-18TF(3)x1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.png differ
diff --git a/higher/res/log/Aug_mod(Data_augV6(Uniform-18TF(3)x1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/log/Aug_mod(Data_augV6(Uniform-18TF(3)x1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
index aef94c6..6f5e137 100644
--- a/higher/res/log/Aug_mod(Data_augV6(Uniform-18TF(3)x1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json	
+++ b/higher/res/log/Aug_mod(Data_augV6(Uniform-18TF(3)x1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json	
@@ -1,10 +1,10 @@
 {
- "Accuracy": 69.5,
+ "Accuracy": 68.86,
  "Time": [
-  28.70131166007,
-  14.194138415030963
+  29.212177275560013,
+  14.7018381259085
  ],
- "Device": "GeForce GTX 1080 Ti",
+ "Device": "TITAN RTX",
  "Param_names": [
   "Identity",
   "FlipUD",
@@ -28,53 +28,17 @@
  "Log": [
   {
    "epoch": 1,
-   "train_loss": 2.240718126296997,
-   "val_loss": 2.2714805603027344,
-   "acc": 15.12,
-   "time": 37.600337649,
+   "train_loss": 2.258223295211792,
+   "val_loss": 2.26889705657959,
+   "acc": 15.83,
+   "time": 37.139667462,
    "param": [
     {
      "p": 0.3333333432674408,
      "m": 1.0
     },
     {
-     "p": 0.2515013813972473,
-     "m": 1.0
-    },
-    {
-     "p": 0.3731701672077179,
-     "m": 1.0
-    },
-    {
-     "p": 0.3666205108165741,
-     "m": 1.0
-    },
-    {
-     "p": 0.36203625798225403,
-     "m": 1.0
-    },
-    {
-     "p": 0.3580230474472046,
-     "m": 1.0
-    },
-    {
-     "p": 0.35442450642585754,
-     "m": 1.0
-    },
-    {
-     "p": 0.31597575545310974,
-     "m": 1.0
-    },
-    {
-     "p": 0.34682217240333557,
-     "m": 1.0
-    },
-    {
-     "p": 0.34268897771835327,
-     "m": 1.0
-    },
-    {
-     "p": 0.33822017908096313,
+     "p": 0.3198474943637848,
      "m": 1.0
     },
     {
@@ -82,7 +46,15 @@
      "m": 1.0
     },
     {
-     "p": 0.3333333432674408,
+     "p": 0.3284461200237274,
+     "m": 1.0
+    },
+    {
+     "p": 0.3000348210334778,
+     "m": 1.0
+    },
+    {
+     "p": 0.2958522439002991,
      "m": 1.0
     },
     {
@@ -90,13 +62,41 @@
      "m": 1.0
     },
     {
-     "p": 0.3333333432674408,
+     "p": 0.3436122238636017,
+     "m": 1.0
+    },
+    {
+     "p": 0.3468224108219147,
      "m": 1.0
     },
     {
      "p": 0.3333333432674408,
      "m": 1.0
     },
+    {
+     "p": 0.3311459720134735,
+     "m": 1.0
+    },
+    {
+     "p": 0.29349470138549805,
+     "m": 1.0
+    },
+    {
+     "p": 0.3333333432674408,
+     "m": 1.0
+    },
+    {
+     "p": 0.36038973927497864,
+     "m": 1.0
+    },
+    {
+     "p": 0.3333333432674408,
+     "m": 1.0
+    },
+    {
+     "p": 0.342389315366745,
+     "m": 1.0
+    },
     {
      "p": 0.3333333432674408,
      "m": 1.0
@@ -109,8019 +109,8019 @@
   },
   {
    "epoch": 2,
-   "train_loss": 2.140302896499634,
-   "val_loss": 2.0705080032348633,
-   "acc": 26.08,
-   "time": 36.63120242699999,
+   "train_loss": 2.095189094543457,
+   "val_loss": 1.9898102283477783,
+   "acc": 27.08,
+   "time": 36.603821763,
    "param": [
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      "p": 0.3333333432674408,
      "m": 1.0
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     {
-     "p": 0.25234025716781616,
+     "p": 0.2981089949607849,
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+     "p": 0.3333333432674408,
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-     "p": 0.3682156503200531,
+     "p": 0.2925703823566437,
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-     "p": 0.3802679777145386,
+     "p": 0.30946701765060425,
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+     "p": 0.26113054156303406,
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+     "p": 0.3058287799358368,
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+     "p": 0.36764150857925415,
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+     "p": 0.37921950221061707,
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+   "train_loss": 1.8762606382369995,
+   "val_loss": 1.8607264757156372,
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+   "train_loss": 0.30033618211746216,
+   "val_loss": 1.0332039594650269,
+   "acc": 68.8,
+   "time": 38.919610336000005,
    "param": [
     {
      "p": 0.3333333432674408,
      "m": 1.0
     },
     {
-     "p": 0.18617364764213562,
+     "p": 0.2690061330795288,
      "m": 1.0
     },
     {
-     "p": 0.2698425352573395,
+     "p": 0.35380783677101135,
      "m": 1.0
     },
     {
-     "p": 0.1872757226228714,
+     "p": 0.3451257348060608,
      "m": 1.0
     },
     {
-     "p": 0.16899600625038147,
+     "p": 0.3220878839492798,
      "m": 1.0
     },
     {
-     "p": 0.2524792551994324,
+     "p": 0.30160102248191833,
      "m": 1.0
     },
     {
-     "p": 0.19053803384304047,
+     "p": 0.2652004063129425,
      "m": 1.0
     },
     {
-     "p": 0.27088192105293274,
+     "p": 0.24113890528678894,
      "m": 1.0
     },
     {
-     "p": 0.14614441990852356,
+     "p": 0.3207394480705261,
      "m": 1.0
     },
     {
-     "p": 0.3114623427391052,
+     "p": 0.2500431537628174,
      "m": 1.0
     },
     {
-     "p": 0.11379595845937729,
+     "p": 0.27423471212387085,
      "m": 1.0
     },
     {
-     "p": 0.29156187176704407,
+     "p": 0.23856507241725922,
      "m": 1.0
     },
     {
-     "p": 0.3068397343158722,
+     "p": 0.24209439754486084,
      "m": 1.0
     },
     {
-     "p": 0.14596538245677948,
+     "p": 0.2459031492471695,
      "m": 1.0
     },
     {
-     "p": 0.05795559659600258,
+     "p": 0.1668601781129837,
      "m": 1.0
     },
     {
-     "p": 0.08655045926570892,
+     "p": 0.16665129363536835,
      "m": 1.0
     },
     {
-     "p": 0.07113166153430939,
+     "p": 0.09967554360628128,
      "m": 1.0
     },
     {
-     "p": 0.09549598395824432,
+     "p": 0.18962261080741882,
      "m": 1.0
     }
    ]
diff --git a/higher/test_dataug.py b/higher/test_dataug.py
index 2ecc305..b8e8ef3 100644
--- a/higher/test_dataug.py
+++ b/higher/test_dataug.py
@@ -10,17 +10,17 @@ tf_names = [
     'FlipLR',
     'Rotate',
     'TranslateX',
-    'TranslateY',
-    'ShearX',
-    'ShearY',
+    #'TranslateY',
+    #'ShearX',
+    #'ShearY',
 
     ## Color TF (Expect image in the range of [0, 1]) ##
-    'Contrast',
-    'Color',
-    'Brightness',
-    'Sharpness',
-    'Posterize',
-    'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
+    #'Contrast',
+    #'Color',
+    #'Brightness',
+    #'Sharpness',
+    #'Posterize',
+    #'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
 
     #Color TF (Common mag scale)
     #'+Contrast',
@@ -44,10 +44,10 @@ tf_names = [
     #'BadTranslateY',
     #'BadTranslateY_neg',
 
-    'BadColor',
-    'BadSharpness',
-    'BadContrast',
-    'BadBrightness',
+    #'BadColor',
+    #'BadSharpness',
+    #'BadContrast',
+    #'BadBrightness',
 
     #Non fonctionnel
     #'Auto_Contrast', #Pas opti pour des batch (Super lent)
@@ -91,11 +91,11 @@ if __name__ == "__main__":
     print('-'*9)
     '''
     #### Augmented Model ####
-    '''
+    #'''
     t0 = time.process_time()
     tf_dict = {k: TF.TF_dict[k] for k in tf_names}
     #tf_dict = TF.TF_dict
-    aug_model = Augmented_model(Data_augV6(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
+    aug_model = Augmented_model(Data_augV6(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
     #aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), WideResNet(num_classes=10, wrn_size=160)).to(device)
     #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), LeNet(3,10)).to(device)
     print(str(aug_model), 'on', device_name)
@@ -116,9 +116,9 @@ if __name__ == "__main__":
 
     print('Execution Time : %.00f '%(time.process_time() - t0))
     print('-'*9)
-    '''
-    #### TF tests ####
     #'''
+    #### TF tests ####
+    '''
     res_folder="res/brutus-tests/"
     epochs= 150
     inner_its = [1]
@@ -168,4 +168,4 @@ if __name__ == "__main__":
                             #plot_resV2(log, fig_name=res_folder+filename, param_names=tf_names)
                             print('-'*9)
 
-    #'''    
+    '''