diff --git a/algorithms/neural_network/lightning_logs/version_23/checkpoints/epoch=3-step=324.ckpt b/algorithms/neural_network/lightning_logs/version_23/checkpoints/epoch=3-step=324.ckpt
new file mode 100644
index 0000000..592f965
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diff --git a/algorithms/neural_network/lightning_logs/version_23/events.out.tfevents.1653922926.DESKTOP-97QK98R.9912.0 b/algorithms/neural_network/lightning_logs/version_23/events.out.tfevents.1653922926.DESKTOP-97QK98R.9912.0
new file mode 100644
index 0000000..bc7cd86
Binary files /dev/null and b/algorithms/neural_network/lightning_logs/version_23/events.out.tfevents.1653922926.DESKTOP-97QK98R.9912.0 differ
diff --git a/algorithms/neural_network/lightning_logs/version_23/hparams.yaml b/algorithms/neural_network/lightning_logs/version_23/hparams.yaml
new file mode 100644
index 0000000..0967ef4
--- /dev/null
+++ b/algorithms/neural_network/lightning_logs/version_23/hparams.yaml
@@ -0,0 +1 @@
+{}
diff --git a/algorithms/neural_network/neural_network_interface.py b/algorithms/neural_network/neural_network_interface.py
index 6ec7cb4..56905a3 100644
--- a/algorithms/neural_network/neural_network_interface.py
+++ b/algorithms/neural_network/neural_network_interface.py
@@ -13,6 +13,7 @@ from pytorch_lightning.callbacks import EarlyStopping
 import torchvision.transforms.functional as F
 from PIL import Image
 
+
 def check_accuracy_tiles():
     answer = 0
     for i in range(100):
@@ -72,6 +73,37 @@ def what_is_it(img_path, show_img=False):
         return ID_TO_CLASS[idx]
 
 
+def check_accuracy(tset):
+    model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_23/checkpoints/epoch=3-step=324.ckpt')
+    num_correct = 0
+    num_samples = 0
+    model = model.to(DEVICE)
+    model.eval()
+
+    with torch.no_grad():
+        for photo, label in tset:
+            photo = photo.to(DEVICE)
+            label = label.to(DEVICE)
+
+            scores = model(photo)
+            predictions = scores.argmax(dim=1)
+            num_correct += (predictions == label).sum()
+            num_samples += predictions.size(0)
+
+        print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}%')
+
+
+def check_accuracy_data():
+    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)
+
+    print("Accuracy of train_set:")
+    check_accuracy(train_loader)
+    print("Accuracy of test_set:")
+    check_accuracy(test_loader)
+
 #CNN = NeuralNetwork()
 #common.helpers.createCSV()
 
@@ -82,9 +114,12 @@ def what_is_it(img_path, show_img=False):
 #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)
-
 #trainer.fit(CNN, train_loader, test_loader)
 #trainer.tune(CNN, train_loader, test_loader)
-#check_accuracy_tiles()
+
 
 #print(what_is_it('../../resources/textures/grass2.png', True))
+
+#check_accuracy_data()
+
+#check_accuracy_tiles()