diff --git a/higher/test_brutus.py b/higher/test_brutus.py
index 88de3ca..046df89 100755
--- a/higher/test_brutus.py
+++ b/higher/test_brutus.py
@@ -35,16 +35,29 @@ if __name__ == "__main__":
 
 
     n_inner_iter = 1
-    epochs = 200
+    epochs = 150
     dataug_epoch_start=0
+    optim_param={
+        'Meta':{
+            'optim':'Adam',
+            'lr':1e-2, #1e-2
+        },
+        'Inner':{
+            'optim': 'SGD',
+            'lr':1e-1, #1e-2
+            'momentum':0.9, #0.9
+        }
+    }
 
     #model = LeNet(3,10)
-    model = MobileNetV2(num_classes=10)
+    model = ResNet(num_classes=10)
+    #model = MobileNetV2(num_classes=10)
     #model = WideResNet(num_classes=10, wrn_size=32)
 
     tf_dict = {k: TF.TF_dict[k] for k in tf_names}
 
     ####
+    '''
     t0 = time.process_time()
 
     aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
@@ -60,9 +73,10 @@ if __name__ == "__main__":
     with open("res/log/%s.json" % filename, "w+") as f:
         json.dump(out, f, indent=True)
         print('Log :\"',f.name, '\" saved !')
-
+    '''
 
     ####
+    '''
     t0 = time.process_time()
 
     aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device)
@@ -82,11 +96,11 @@ if __name__ == "__main__":
     res_folder="res/brutus-tests/"
     epochs= 150
     inner_its = [1]
-    dist_mix = [1]
+    dist_mix = [0.0, 0.5, 0.8, 1.0]
     dataug_epoch_starts= [0]
     tf_dict = {k: TF.TF_dict[k] for k in tf_names}
     TF_nb = [len(tf_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
-    N_seq_TF= [2, 3, 4]
+    N_seq_TF= [2, 3]
     mag_setup = [(True,True), (False, False)]
     #prob_setup = [True, False]
     nb_run= 3
@@ -104,27 +118,34 @@ if __name__ == "__main__":
                     #for i in TF_nb:
                     for m_setup in mag_setup:
                         #for p_setup in prob_setup:
+                        p_setup=True
                         for run in range(nb_run):
-                            if n_inner_iter == 0 and (m_setup!=(True,True) or p_setup!=True): continue #Autres setup inutiles sans meta-opti
-                            if n_tf ==2 and m_setup==(True,True): continue #Deja resultats
+                            if n_inner_iter == 0 and (m_setup!=(True,True) and p_setup!=True): continue #Autres setup inutiles sans meta-opti
                             #keys = list(TF.TF_dict.keys())[0:i]
                             #ntf_dict = {k: TF.TF_dict[k] for k in keys}
 
-                            aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=n_tf, mix_dist=dist, fixed_prob=False, fixed_mag=m_setup[0], shared_mag=m_setup[1]), LeNet(3,10)).to(device)
-                            print(str(aug_model), 'on', device_name)
-                            #run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
-                            log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=20, loss_patience=None)
+                            aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=n_tf, mix_dist=dist, fixed_prob=p_setup, fixed_mag=m_setup[0], shared_mag=m_setup[1]), model).to(device)
+                            #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
 
+                            print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
+                            log= run_dist_dataugV2(model=aug_model,
+                                 epochs=epochs, 
+                                 inner_it=n_inner_iter, 
+                                 dataug_epoch_start=dataug_epoch_start, 
+                                 opt_param=optim_param,
+                                 print_freq=20, 
+                                 KLdiv=True, 
+                                 loss_patience=None)
+
+                            exec_time=time.process_time() - t0
                             ####
                             print('-'*9)
                             times = [x["time"] for x in log]
-                            out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times)), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
-                            print(str(aug_model),": acc", out["Accuracy"], "in :", out["Time"][0], "+/-", out["Time"][1])
-                            filename = "{}-{}epochs(dataug:{})-{}in_it-{}".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter,run)
-                            with open(res_folder+"log/%s.json" % filename, "w+") as f:
+                            out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), 'Optimizer': optim_param, "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
+                            print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
+                            filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
+                            with open("res/log/%s.json" % filename, "w+") as f:
                                 json.dump(out, f, indent=True)
                                 print('Log :\"',f.name, '\" saved !')
-
-                            #plot_resV2(log, fig_name=res_folder+filename, param_names=tf_names)
                             print('-'*9)
     '''