diff --git a/consts.py b/consts.py
index d138792..d4fe743 100644
--- a/consts.py
+++ b/consts.py
@@ -4,7 +4,7 @@ import torchvision.transforms as transforms
 CIFAR_DIR = Path('Data/CIFAR10')
 CIFAR_DIR.mkdir(exist_ok = True)
 
-
+#you can add more and bump the numbers even more up
 normalize = transforms.Compose([
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
diff --git a/dataset.py b/dataset.py
index 552aedb..8c4fdb4 100644
--- a/dataset.py
+++ b/dataset.py
@@ -1,20 +1,26 @@
 import torchvision
-from consts import CIFAR_DIR
+from consts import TRAIN_DATA
+import torchvision.transforms as transforms
+from dogs_cats_ds import DogCatDataset
+import random
 #optional transformations:
 # https://pytorch.org/vision/0.11/transforms.html
 
 #training data using torchvision cifar.
-cifar_data_train = torchvision.datasets.CIFAR10(root = CIFAR_DIR, train = True, transform = None, download = True)
-
 
 
 #example of cifar data sample. It is an image, class example.
-# here, the image is the image (PIL, or pillow) and the corresponding label, frog. I've chopped the dataset to only include cats
-# and dogs, so we can apply a different form of classification so it's easier to perform
-example_data = cifar_data_train[0]
-print(f'items in an instance of cifar10: {len(example_data)}')
-example_data[0].show()
+# here, the image is the image (PIL, or pillow) and the corresponding label. I've chopped the dataset to only include cats
+# and dogs, so we can apply a different form of classification so 
+r = random.randint(1, len(DogCatDataset(TRAIN_DATA)))
+# print(r)
+example_data = DogCatDataset(TRAIN_DATA)[r]
+
+print(f'items in an instance of the dataset: {len(example_data)}')
 print(f'class corresponding to image: {example_data[1]}')
+sample_img = torchvision.transforms.functional.to_pil_image(example_data[0])
+sample_img = sample_img.resize((224,224))
+sample_img.show()