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diff --git a/src/machine_learning/neural_network/best_checkpoint.model b/src/machine_learning/neural_network/best_checkpoint.model
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diff --git a/src/machine_learning/neural_network/learning.py b/src/machine_learning/neural_network/learning.py
index acd8337..2a79944 100644
--- a/src/machine_learning/neural_network/learning.py
+++ b/src/machine_learning/neural_network/learning.py
@@ -10,6 +10,8 @@ from torch.autograd import Variable
 import torchvision
 import pathlib
 
+from net import Net
+
 
 temp_path = os.path.abspath('../../..')
 DIR = ''
@@ -33,16 +35,85 @@ transformer=transforms.Compose([
 #Dataloader
 
 #Path for training and testing directory
-train_path=train_dir
-test_path=test_dir
+train_path=r'D:\Documents\Studia\Semestr-4\sztuczna-inteligencja\super-saper\images\learning\training\training'
+test_path=r'D:\Documents\Studia\Semestr-4\sztuczna-inteligencja\super-saper\images\learning\test\test'
+
+print(train_path)
 
 train_loader=DataLoader(
-    torchvision.datasets.ImageFolder(train_path,transform=transformer),
+    torchvision.datasets.ImageFolder(train_path, transform=transformer),
     batch_size=64, shuffle=True
 )
 test_loader=DataLoader(
-    torchvision.datasets.ImageFolder(test_path,transform=transformer),
+    torchvision.datasets.ImageFolder(test_path, transform=transformer),
     batch_size=32, shuffle=True
 )
 
-print(train_loader)
\ No newline at end of file
+#categories
+root=pathlib.Path(train_path)
+classes=sorted([j.name.split('/')[-1] for j in root.iterdir()])
+
+model=Net(num_classes=6).to(device)
+
+#Optmizer and loss function
+optimizer=Adam(model.parameters(),lr=0.001,weight_decay=0.0001)
+loss_function=nn.CrossEntropyLoss()
+
+num_epochs=10
+
+train_count=len(glob.glob(train_path+'/**/*.jpg'))
+test_count=len(glob.glob(test_path+'/**/*.jpg'))
+
+print(train_count,test_count)
+
+best_accuracy = 0.0
+
+for epoch in range(num_epochs):
+
+    # Evaluation and training on training dataset
+    model.train()
+    train_accuracy = 0.0
+    train_loss = 0.0
+
+    for i, (images, labels) in enumerate(train_loader):
+        if torch.cuda.is_available():
+            images = Variable(images.cuda())
+            labels = Variable(labels.cuda())
+
+        optimizer.zero_grad()
+
+        outputs = model(images)
+        loss = loss_function(outputs, labels)
+        loss.backward()
+        optimizer.step()
+
+        train_loss += loss.cpu().data * images.size(0)
+        _, prediction = torch.max(outputs.data, 1)
+
+        train_accuracy += int(torch.sum(prediction == labels.data))
+
+    train_accuracy = train_accuracy / train_count
+    train_loss = train_loss / train_count
+
+    # Evaluation on testing dataset
+    model.eval()
+
+    test_accuracy = 0.0
+    for i, (images, labels) in enumerate(test_loader):
+        if torch.cuda.is_available():
+            images = Variable(images.cuda())
+            labels = Variable(labels.cuda())
+
+        outputs = model(images)
+        _, prediction = torch.max(outputs.data, 1)
+        test_accuracy += int(torch.sum(prediction == labels.data))
+
+    test_accuracy = test_accuracy / test_count
+
+    print('Epoch: ' + str(epoch) + ' Train Loss: ' + str(train_loss) + ' Train Accuracy: ' + str(
+        train_accuracy) + ' Test Accuracy: ' + str(test_accuracy))
+
+    # Save the best model
+    if test_accuracy > best_accuracy:
+        torch.save(model.state_dict(), 'best_checkpoint.model')
+        best_accuracy = test_accuracy
\ No newline at end of file