diff --git a/main.py b/main.py
index 2cd28d3050f8ab7ae463989b269f1b2bfc129394..54886332f556978232ca685f985f1d66fe7f70d6 100644
--- a/main.py
+++ b/main.py
@@ -254,10 +254,10 @@ def run_duo(args):
     plt.plot(train_loss)
     plt.plot(train_loss)
 
-    plt.savefig(f'output/Atraining_plot_model_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
+    plt.savefig(f'NewtonOutput/Atraining_plot_model_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
     #load and evaluate best model
     load_model(model, args.save_path)
-    make_prediction_duo(model,data_test, f'output/Amodel_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
+    make_prediction_duo(model,data_test, f'NewtonOutput/Amodel_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
     return best_loss,best_acc
 
 def make_prediction_duo(model, data, f_name):