diff --git a/pl_examples/domain_templates/generative_adversarial_net.py b/pl_examples/domain_templates/generative_adversarial_net.py
index 427312ecaf..65505c1240 100644
--- a/pl_examples/domain_templates/generative_adversarial_net.py
+++ b/pl_examples/domain_templates/generative_adversarial_net.py
@@ -20,6 +20,7 @@ from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
 
 from pytorch_lightning.core import LightningModule
+from pytorch_lightning import TrainResult
 from pytorch_lightning.trainer import Trainer
 
 
@@ -127,12 +128,13 @@ class GAN(LightningModule):
             # adversarial loss is binary cross-entropy
             g_loss = self.adversarial_loss(self.discriminator(self(z)), valid)
             tqdm_dict = {'g_loss': g_loss}
-            output = OrderedDict({
-                'loss': g_loss,
-                'progress_bar': tqdm_dict,
-                'log': tqdm_dict
-            })
-            return output
+            result = TrainResult(
+                minimize=g_loss,
+                checkpoint_on=True
+            )
+            result.log_dict(tqdm_dict)
+
+            return result
 
         # train discriminator
         if optimizer_idx == 1:
@@ -154,12 +156,13 @@ class GAN(LightningModule):
             # discriminator loss is the average of these
             d_loss = (real_loss + fake_loss) / 2
             tqdm_dict = {'d_loss': d_loss}
-            output = OrderedDict({
-                'loss': d_loss,
-                'progress_bar': tqdm_dict,
-                'log': tqdm_dict
-            })
-            return output
+            result = TrainResult(
+                minimize=d_loss,
+                checkpoint_on=True
+            )
+            result.log_dict(tqdm_dict)
+
+            return result
 
     def configure_optimizers(self):
         lr = self.lr