diff --git a/examples/unet_segmentation_3d.ipynb b/examples/unet_segmentation_3d.ipynb
index 6af33fe27b..3a87b3224e 100644
--- a/examples/unet_segmentation_3d.ipynb
+++ b/examples/unet_segmentation_3d.ipynb
@@ -151,7 +151,7 @@
     "    num_res_units=2,\n",
     ")\n",
     "\n",
-    "loss = networks.losses.DiceLoss()\n",
+    "loss = networks.losses.DiceLoss(do_sigmoid=True)\n",
     "opt = torch.optim.Adam(net.parameters(), lr)"
    ]
   },
diff --git a/examples/unet_segmentation_3d.py b/examples/unet_segmentation_3d.py
index b1f921e6b5..364cdaab7f 100644
--- a/examples/unet_segmentation_3d.py
+++ b/examples/unet_segmentation_3d.py
@@ -93,7 +93,7 @@ def create_test_image_3d(height, width, depth, num_objs=12, rad_max=30, noise_ma
     num_res_units=2,
 )
 
-loss = networks.losses.DiceLoss()
+loss = networks.losses.DiceLoss(do_sigmoid=True)
 opt = torch.optim.Adam(net.parameters(), lr)
 
 train_epochs = 30
@@ -108,7 +108,7 @@ def _loss_fn(i, j):
 trainer = create_supervised_trainer(net, opt, _loss_fn, device, False,
                                     output_transform=lambda x, y, y_pred, loss: [y_pred, loss.item()])
 
-checkpoint_handler = ModelCheckpoint('./', 'net', n_saved=10, save_interval=3, require_empty=False)
+checkpoint_handler = ModelCheckpoint('./', 'net', n_saved=10, require_empty=False)
 trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=checkpoint_handler, to_save={'net': net})
 
 
diff --git a/monai/networks/losses/dice.py b/monai/networks/losses/dice.py
index c193dbd3f8..c98f503034 100644
--- a/monai/networks/losses/dice.py
+++ b/monai/networks/losses/dice.py
@@ -49,26 +49,25 @@ def forward(self, pred, ground, smooth=1e-5):
 
         psum = pred.float()
         if self.do_sigmoid:
-            psum = psum.sigmoid()
-        if pred.shape[1] == 1:  # binary dice loss, use sigmoid activation
+            psum = psum.sigmoid()  # use sigmoid activation
+        if pred.shape[1] == 1:
             if self.do_softmax:
                 raise ValueError('do_softmax is not compatible with single channel prediction.')
             if not self.include_background:
-                raise RuntimeWarning('single channel ground truth, `include_background=False` ignored.')
+                raise RuntimeWarning('single channel prediction, `include_background=False` ignored.')
             tsum = ground
-        else:
+        else:  # multiclass dice loss
             if self.do_softmax:
                 if self.do_sigmoid:
                     raise ValueError('do_sigmoid=True and do_softmax=Ture are not compatible.')
-                # multiclass dice loss, use softmax in the first dimension and convert target to one-hot encoding
                 psum = torch.softmax(pred, 1)
             tsum = one_hot(ground, pred.shape[1])  # B1HW(D) -> BNHW(D)
             # exclude background category so that it doesn't overwhelm the other segmentations if they are small
             if not self.include_background:
                 tsum = tsum[:, 1:]
                 psum = psum[:, 1:]
-        assert tsum.shape == pred.shape, ("Ground truth one-hot has differing shape (%r) from source (%r)" %
-                                          (tsum.shape, pred.shape))
+        assert tsum.shape == psum.shape, ("Ground truth one-hot has differing shape (%r) from source (%r)" %
+                                          (tsum.shape, psum.shape))
 
         batchsize = ground.size(0)
         tsum = tsum.float().view(batchsize, -1)
diff --git a/tests/test_dice_loss.py b/tests/test_dice_loss.py
index bbaa49c8ac..dac0039945 100644
--- a/tests/test_dice_loss.py
+++ b/tests/test_dice_loss.py
@@ -44,7 +44,7 @@
 
 TEST_CASE_3 = [  # shape: (2, 2, 3), (2, 1, 3)
     {
-        'include_background': True,
+        'include_background': False,
     },
     {
         'pred': torch.tensor([[[1., 1., 0.], [0., 0., 1.]], [[1., 0., 1.], [0., 1., 0.]]]),
@@ -80,6 +80,7 @@
     0.373045,
 ]
 
+
 class TestDiceLoss(unittest.TestCase):
 
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])