diff --git a/src/symbolic_nn_tests/experiment1/__init__.py b/src/symbolic_nn_tests/experiment1/__init__.py
index dccaf1a..45c7a2b 100644
--- a/src/symbolic_nn_tests/experiment1/__init__.py
+++ b/src/symbolic_nn_tests/experiment1/__init__.py
@@ -1,7 +1,9 @@
 LEARNING_RATE = 10e-5
 
 
-def test(train_loss, val_loss, test_loss, version, tensorboard=True, wandb=True):
+def test(
+    train_loss, val_loss, test_loss, accuracy, version, tensorboard=True, wandb=True
+):
     from .model import main as test_model
 
     logger = []
@@ -36,6 +38,7 @@ def test(train_loss, val_loss, test_loss, version, tensorboard=True, wandb=True)
         train_loss=train_loss,
         val_loss=val_loss,
         test_loss=test_loss,
+        accuracy=accuracy,
         lr=LEARNING_RATE,
     )
 
@@ -44,6 +47,8 @@ def test(train_loss, val_loss, test_loss, version, tensorboard=True, wandb=True)
 
 
 def run(tensorboard: bool = True, wandb: bool = True):
+    from torchmetrics import Accuracy
+
     from . import semantic_loss
     from .model import oh_vs_cat_cross_entropy
 
@@ -51,6 +56,7 @@ def run(tensorboard: bool = True, wandb: bool = True):
         train_loss=oh_vs_cat_cross_entropy,
         val_loss=oh_vs_cat_cross_entropy,
         test_loss=oh_vs_cat_cross_entropy,
+        accuracy=Accuracy(task="multiclass", num_classes=10),
         version="cross_entropy",
         tensorboard=tensorboard,
         wandb=wandb,
@@ -59,6 +65,7 @@ def run(tensorboard: bool = True, wandb: bool = True):
         train_loss=semantic_loss.similarity_cross_entropy,
         val_loss=oh_vs_cat_cross_entropy,
         test_loss=oh_vs_cat_cross_entropy,
+        accuracy=Accuracy(task="multiclass", num_classes=10),
         version="similarity_cross_entropy",
         tensorboard=tensorboard,
         wandb=wandb,
@@ -67,6 +74,7 @@ def run(tensorboard: bool = True, wandb: bool = True):
         train_loss=semantic_loss.hasline_cross_entropy,
         val_loss=oh_vs_cat_cross_entropy,
         test_loss=oh_vs_cat_cross_entropy,
+        accuracy=Accuracy(task="multiclass", num_classes=10),
         version="hasline_cross_entropy",
         tensorboard=tensorboard,
         wandb=wandb,
@@ -75,6 +83,7 @@ def run(tensorboard: bool = True, wandb: bool = True):
         train_loss=semantic_loss.hasloop_cross_entropy,
         val_loss=oh_vs_cat_cross_entropy,
         test_loss=oh_vs_cat_cross_entropy,
+        accuracy=Accuracy(task="multiclass", num_classes=10),
         version="hasloop_cross_entropy",
         tensorboard=tensorboard,
         wandb=wandb,
@@ -83,6 +92,7 @@ def run(tensorboard: bool = True, wandb: bool = True):
         train_loss=semantic_loss.multisemantic_cross_entropy,
         val_loss=oh_vs_cat_cross_entropy,
         test_loss=oh_vs_cat_cross_entropy,
+        accuracy=Accuracy(task="multiclass", num_classes=10),
         version="multisemantic_cross_entropy",
         tensorboard=tensorboard,
         wandb=wandb,
@@ -91,6 +101,7 @@ def run(tensorboard: bool = True, wandb: bool = True):
         train_loss=semantic_loss.garbage_cross_entropy,
         val_loss=oh_vs_cat_cross_entropy,
         test_loss=oh_vs_cat_cross_entropy,
+        accuracy=Accuracy(task="multiclass", num_classes=10),
         version="garbage_cross_entropy",
         tensorboard=tensorboard,
         wandb=wandb,
diff --git a/src/symbolic_nn_tests/experiment1/model.py b/src/symbolic_nn_tests/experiment1/model.py
index de9aea2..3d41c7b 100644
--- a/src/symbolic_nn_tests/experiment1/model.py
+++ b/src/symbolic_nn_tests/experiment1/model.py
@@ -1,6 +1,7 @@
 from functools import lru_cache
 import torch
 from torch import nn
+from torchmetrics import Accuracy
 
 
 model = nn.Sequential(
@@ -46,6 +47,7 @@ def main(
     train_loss=oh_vs_cat_cross_entropy,
     val_loss=oh_vs_cat_cross_entropy,
     test_loss=oh_vs_cat_cross_entropy,
+    accuracy=Accuracy(task="multiclass", num_classes=10),
     logger=None,
     trainer_callbacks=None,
     **kwargs,
@@ -61,7 +63,11 @@ def main(
 
     train, val, test = get_singleton_dataset()
     lmodel = TrainingWrapper(
-        model, train_loss=train_loss, val_loss=val_loss, test_loss=test_loss
+        model,
+        train_loss=train_loss,
+        val_loss=val_loss,
+        test_loss=test_loss,
+        accuracy=accuracy,
     )
     lmodel.configure_optimizers(**kwargs)
     trainer = L.Trainer(max_epochs=20, logger=logger, callbacks=trainer_callbacks)