diff --git a/NeuralNetwork/neural_network_learning.py b/NeuralNetwork/neural_network_learning.py
index a0c75e6..9f71d86 100644
--- a/NeuralNetwork/neural_network_learning.py
+++ b/NeuralNetwork/neural_network_learning.py
@@ -1,10 +1,10 @@
-from src.torchvision_resize_dataset import combined_dataset
+import glob
+from src.torchvision_resize_dataset import combined_dataset, letters_path, package_path
+import src.data_model
+from torch.optim import Adam
 import torch 
 import torch.nn as nn 
 from torch.utils.data import DataLoader 
-from torchvision import datasets 
-from torchvision.transforms import Compose, Lambda, ToTensor 
-import matplotlib.pyplot as plt
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -15,3 +15,46 @@ train_loader = DataLoader(
 )
 classes = ["package", "list"]
 
+model = src.data_model.DataModel(num_objects=2).to(device)
+
+#optimizer 
+optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)
+#loss function
+criterion = nn.CrossEntropyLoss()
+
+num_epochs = 10
+train_size = len(glob.glob(letters_path, '*.jpg')) + len(glob.glob(package_path, '*.png'))
+
+go_to_accuracy = 0.0
+for epoch in range(num_epochs):
+    #training on 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 = torch.Variable(images.cuda())
+            labels = torch.Variable(labels.cuda())
+        # clearing the optimizer gradients
+        optimizer.zero_grad()
+
+        outputs = model(images) # predoction
+        loss = criterion(outputs, labels)   #loss calculation
+        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_size
+    train_loss = train_loss/train_size
+
+    print('Epoch:  '+ str(epoch+1) +' Train Loss: '+ str(int(train_loss)) +' Train Accuracy: '+ str(train_accuracy))
+
+    if train_accuracy > go_to_accuracy:
+        go_to_accuracy= train_accuracy
+        torch.save(model.state_dict(), "best_model.pth") 
+
+
diff --git a/NeuralNetwork/src/torchvision_resize_dataset.py b/NeuralNetwork/src/torchvision_resize_dataset.py
index 7684909..666d072 100644
--- a/NeuralNetwork/src/torchvision_resize_dataset.py
+++ b/NeuralNetwork/src/torchvision_resize_dataset.py
@@ -1,7 +1,6 @@
 import torchvision.transforms as transforms
 from torchvision.datasets import ImageFolder
 from torch.utils.data import ConcatDataset
-import os
 
 # images have to be the same size for the algorithm to work
 transform = transforms.Compose([