From d47adb6752da3d4590dcc49ebc306b76a586e62f Mon Sep 17 00:00:00 2001
From: lcalmettes <leo.calmettes@etu.ec-lyon.fr>
Date: Mon, 12 May 2025 16:11:27 +0200
Subject: [PATCH] =?UTF-8?q?=09modifi=C3=A9=C2=A0:=20=20=20=20=20=20=20=20?=
=?UTF-8?q?=20main.py?=
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---
main.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/main.py b/main.py
index 2cd28d3..5488633 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):
--
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