diff --git a/pl_examples/full_examples/imagenet/imagenet_example.py b/pl_examples/full_examples/imagenet/imagenet_example.py
index c2175da807..ce2fbf6a12 100644
--- a/pl_examples/full_examples/imagenet/imagenet_example.py
+++ b/pl_examples/full_examples/imagenet/imagenet_example.py
@@ -34,9 +34,12 @@ class ImageNetLightningModel(pl.LightningModule):
         self.hparams = hparams
         self.model = models.__dict__[self.hparams.arch](pretrained=self.hparams.pretrained)
 
+    def forward(self, x):
+        return self.model(x)
+
     def training_step(self, batch, batch_idx):
         images, target = batch
-        output = self.model(images)
+        output = self.forward(images)
         loss_val = F.cross_entropy(output, target)
         acc1, acc5 = self.__accuracy(output, target, topk=(1, 5))
 
@@ -59,7 +62,7 @@ class ImageNetLightningModel(pl.LightningModule):
 
     def validation_step(self, batch, batch_idx):
         images, target = batch
-        output = self.model(images)
+        output = self.forward(images)
         loss_val = F.cross_entropy(output, target)
         acc1, acc5 = self.__accuracy(output, target, topk=(1, 5))
 
@@ -132,7 +135,7 @@ class ImageNetLightningModel(pl.LightningModule):
             std=[0.229, 0.224, 0.225],
         )
 
-        train_dir = os.path.join(self.hparams.data, 'train')
+        train_dir = os.path.join(self.hparams.data_path, 'train')
         train_dataset = datasets.ImageFolder(
             train_dir,
             transforms.Compose([
@@ -162,7 +165,7 @@ class ImageNetLightningModel(pl.LightningModule):
             mean=[0.485, 0.456, 0.406],
             std=[0.229, 0.224, 0.225],
         )
-        val_dir = os.path.join(self.hparams.data, 'val')
+        val_dir = os.path.join(self.hparams.data_path, 'val')
         val_loader = torch.utils.data.DataLoader(
             datasets.ImageFolder(val_dir, transforms.Compose([
                 transforms.Resize(256),
@@ -185,7 +188,7 @@ class ImageNetLightningModel(pl.LightningModule):
                                  ' (default: resnet18)')
         parser.add_argument('--epochs', default=90, type=int, metavar='N',
                             help='number of total epochs to run')
-        parser.add_argument('--seed', type=int, default=None,
+        parser.add_argument('--seed', type=int, default=42,
                             help='seed for initializing training. ')
         parser.add_argument('-b', '--batch-size', default=256, type=int,
                             metavar='N',
@@ -214,7 +217,7 @@ def get_args():
                                help='how many gpus')
     parent_parser.add_argument('--distributed-backend', type=str, default='dp', choices=('dp', 'ddp', 'ddp2'),
                                help='supports three options dp, ddp, ddp2')
-    parent_parser.add_argument('--use-16bit', dest='use-16bit', action='store_true',
+    parent_parser.add_argument('--use-16bit', dest='use_16bit', action='store_true',
                                help='if true uses 16 bit precision')
     parent_parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                                help='evaluate model on validation set')