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diff --git a/src/machine_learning/neural_network/best_checkpoint.model b/src/machine_learning/neural_network/best_checkpoint.model
index 8be5bed..a4ba027 100644
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diff --git a/src/machine_learning/neural_network/learning.py b/src/machine_learning/neural_network/learning.py
index 2a79944..692c6d4 100644
--- a/src/machine_learning/neural_network/learning.py
+++ b/src/machine_learning/neural_network/learning.py
@@ -3,6 +3,7 @@ import numpy as np
 import torch
 import glob
 import torch.nn as nn
+from PIL import Image
 from torchvision.transforms import transforms
 from torch.utils.data import DataLoader
 from torch.optim import Adam
@@ -35,8 +36,9 @@ transformer=transforms.Compose([
 #Dataloader
 
 #Path for training and testing directory
-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'
+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'
+pred_path = r'D:\Documents\Studia\Semestr-4\sztuczna-inteligencja\super-saper\images\learning\prediction\prediction'
 
 print(train_path)
 
@@ -56,64 +58,138 @@ 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()
+optimizer=Adam(model.parameters(),lr=1e-3,weight_decay=0.0001)
+loss_fn=nn.CrossEntropyLoss()
 
 num_epochs=10
 
-train_count=len(glob.glob(train_path+'/**/*.jpg'))
-test_count=len(glob.glob(test_path+'/**/*.jpg'))
+train_count=len(glob.glob(train_path+'/**/*.*'))
+test_count=len(glob.glob(test_path+'/**/*.*'))
 
 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())
+def train(dataloader, model, loss_fn, optimizer):
+    size = len(dataloader.dataset)
+    for batch, (X, y) in enumerate(dataloader):
+        X, y = X.to(device), y.to(device)
 
+        # Compute prediction error
+        pred = model(X.float())
+        loss = loss_fn(pred, y)
+        # Backpropagation
         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)
+        if batch % 100 == 0:
+            loss, current = loss.item(), batch * len(X)
+            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")
 
-        train_accuracy += int(torch.sum(prediction == labels.data))
 
-    train_accuracy = train_accuracy / train_count
-    train_loss = train_loss / train_count
-
-    # Evaluation on testing dataset
+def test(dataloader, model):
+    size = len(dataloader.dataset)
     model.eval()
+    test_loss, correct = 0, 0
+    with torch.no_grad():
+        for X, y in dataloader:
+            X, y = X.to(device), y.to(device)
+            pred = model(X.float())
+            test_loss += loss_fn(pred, y).item()
+            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+    test_loss /= size
+    correct /= size
+    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
 
-    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))
+def prediction1(classes, img_path, model, transformer):
+    image = Image.open(img_path).convert('RGB')
+    image_tensor = transformer(image).float()
+    image_tensor = image_tensor.unsqueeze_(0)
+    if torch.cuda.is_available():
+        image_tensor.cuda()
+    input = Variable(image_tensor)
+    output = model(input)
+    index = output.data.numpy().argmax()
+    pred = classes[index]
+    return pred
 
-    test_accuracy = test_accuracy / test_count
+transformer1 = transforms.Compose([transforms.Resize((150, 150)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
 
-    print('Epoch: ' + str(epoch) + ' Train Loss: ' + str(train_loss) + ' Train Accuracy: ' + str(
-        train_accuracy) + ' Test Accuracy: ' + str(test_accuracy))
+# for t in range(9):
+#     print(f"Epoch {t+1}\n-------------------------------")
+#     train(train_loader, model, loss_fn, optimizer)
+#     test(test_loader, model)
+# print("Done!")
 
-    # 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
+checkpoint = torch.load(os.path.join('.', 'best_checkpoint.model'))
+model = Net(num_classes=6)
+model.load_state_dict(checkpoint)
+model.eval()
+transformer1 = transforms.Compose([transforms.Resize((150, 150)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
+images_path = glob.glob(pred_path+'/*.*')
+pred_dict = {}
+for i in images_path:
+    pred_dict[i[i.rfind('/') + 1:]] = prediction1(classes, i, model, transformer1)
+print(pred_dict)
+
+model = Net(num_classes=6)
+model.load_state_dict(checkpoint)
+model.eval()
+
+images_path = glob.glob(pred_path + '/*.*')
+pred_dict = {}
+for i in images_path:
+    pred_dict[i[i.rfind('/') + 1:]] = prediction1(classes, i, model, transformer1)
+print(pred_dict)
+
+# 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