diff --git a/examples/glvq_mnist.py b/examples/glvq_mnist.py
index 875fbfa..a98c958 100644
--- a/examples/glvq_mnist.py
+++ b/examples/glvq_mnist.py
@@ -1,8 +1,14 @@
 """GLVQ example using the MNIST dataset.
 
+TODO
+- Add model serialization/deserialization
+- Add evaluation metrics
+
 This script also shows how to use Tensorboard for visualizing the prototypes.
 """
 
+import argparse
+
 import pytorch_lightning as pl
 import torchvision
 from matplotlib import pyplot as plt
@@ -32,6 +38,30 @@ class VisualizationCallback(pl.Callback):
 
 
 if __name__ == "__main__":
+    # Arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--epochs",
+                        type=int,
+                        default=10,
+                        help="Epochs to train.")
+    parser.add_argument("--lr",
+                        type=float,
+                        default=0.001,
+                        help="Learning rate.")
+    parser.add_argument("--batch_size",
+                        type=int,
+                        default=256,
+                        help="Batch size.")
+    parser.add_argument("--gpus",
+                        type=int,
+                        default=0,
+                        help="Number of GPUs to use.")
+    parser.add_argument("--ppc",
+                        type=int,
+                        default=1,
+                        help="Prototypes-Per-Class.")
+    args = parser.parse_args()
+
     # Dataset
     mnist_train = MNIST(
         "./datasets",
@@ -63,19 +93,19 @@ if __name__ == "__main__":
     # Initialize the model
     model = ImageGLVQ(input_dim=28 * 28,
                       nclasses=10,
-                      prototypes_per_class=10,
+                      prototypes_per_class=args.ppc,
                       prototype_initializer="stratified_mean",
                       data=[x, y])
     # Model summary
     print(model)
 
     # Callbacks
-    vis = VisualizationCallback(to_shape=(-1, 1, 28, 28), nrow=10)
+    vis = VisualizationCallback(to_shape=(-1, 1, 28, 28), nrow=args.ppc)
 
     # Setup trainer
     trainer = pl.Trainer(
-        gpus=0,  # change to use GPUs for training
-        max_epochs=10,
+        gpus=args.gpus,  # change to use GPUs for training
+        max_epochs=args.epochs,
         callbacks=[vis],
         # accelerator="ddp_cpu",  # DEBUG-ONLY
         # num_processes=2,  # DEBUG-ONLY