fix : path

image_ref/main.py

@@ -84,7 +84,7 @@ def run_duo(args):
         model = model.cuda()
 
     #init accumulators
-    best_acc = 0
+    best_loss = 100
     train_acc=[]
     train_loss=[]
     val_acc=[]
@@ -101,18 +101,32 @@ def run_duo(args):
             loss, acc = test_duo(model,data_test_batch,loss_function,e)
             val_loss.append(loss)
             val_acc.append(acc)
-            if acc > best_acc :
+            if loss < best_loss :
                 save_model(model,args.save_path)
-                best_acc = acc
+                loss = acc
     # plot and save training figs
-    plt.plot(train_acc)
-    plt.plot(val_acc)
-    plt.plot(train_acc)
-    plt.plot(train_acc)
+    plt.clf()
+    plt.subplot(2, 1, 1)
+    plt.plot(train_acc, label='train')
+    plt.plot(val_acc, label='val')
+    plt.title('Train and validation accuracy')
+    plt.xlabel('epoch')
+    plt.ylabel('accuracy')
+    plt.legend(loc="upper left")
     plt.ylim(0, 1.05)
+
+    plt.subplot(2, 1, 2)
+    plt.plot(train_loss, label='train')
+    plt.plot(val_loss, label='val')
+    plt.title('Train and validation loss')
+    plt.xlabel('epoch')
+    plt.ylabel('loss')
+    plt.legend(loc="upper left")
+
+
     plt.show()
+    plt.savefig('output/training_plot_contrastive_noise_{}_lr_{}_model_{}.png'.format(args.noise_threshold,args.lr,args.model))
 
-    plt.savefig('../output/training_plot_contrastive_noise_{}_lr_{}_model_{}.png'.format(args.noise_threshold,args.lr,args.model))
     #load and evaluate best model
     load_model(model, args.save_path)
     make_prediction_duo(model,data_test_batch, 'output/confusion_matrix_contractive_noise_{}_lr_{}_model_{}.png'.format(args.noise_threshold,args.lr,args.model),