diff --git a/algorithms/neural_network/neural_network_interface.py b/algorithms/neural_network/neural_network_interface.py
index 4033778..6ec7cb4 100644
--- a/algorithms/neural_network/neural_network_interface.py
+++ b/algorithms/neural_network/neural_network_interface.py
@@ -13,44 +13,6 @@ from pytorch_lightning.callbacks import EarlyStopping
 import torchvision.transforms.functional as F
 from PIL import Image
 
-def train(model):
-    model = model.to(DEVICE)
-    model.train()
-    trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
-    testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
-    train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
-    test_loader = DataLoader(testset, batch_size=BATCH_SIZE, shuffle=True)
-
-    criterion = nn.CrossEntropyLoss()
-    optimizer = Adam(model.parameters(), lr=LEARNING_RATE)
-
-    for epoch in range(NUM_EPOCHS):
-        for batch_idx, (data, targets) in enumerate(train_loader):
-            data = data.to(device=DEVICE)
-            targets = targets.to(device=DEVICE)
-
-            scores = model(data)
-            loss = criterion(scores, targets)
-
-            optimizer.zero_grad()
-            loss.backward()
-
-            optimizer.step()
-
-            if batch_idx % 4 == 0:
-                print("epoch: %d loss: %.4f" % (epoch, loss.item()))
-
-    print("FINISHED TRAINING!")
-    torch.save(model.state_dict(), "./learnednetwork.pth")
-
-    print("Checking accuracy for the train set.")
-    check_accuracy(train_loader)
-    print("Checking accuracy for the test set.")
-    check_accuracy(test_loader)
-    print("Checking accuracy for the tiles.")
-    check_accuracy_tiles()
-
-
 def check_accuracy_tiles():
     answer = 0
     for i in range(100):