diff --git a/examples/deepvoice3/train.py b/examples/deepvoice3/train.py
index 3cedf57..c552217 100644
--- a/examples/deepvoice3/train.py
+++ b/examples/deepvoice3/train.py
@@ -114,9 +114,9 @@ def train(args, config):
             loss.numpy()[0], 
             causal_mel_loss.numpy()[0], 
             non_causal_mel_loss.numpy()[0]))
-        writer.add_scalar("loss/causal_mel_loss", causal_mel_loss.numpy()[0], global_step=global_step)
-        writer.add_scalar("loss/non_causal_mel_loss", non_causal_mel_loss.numpy()[0], global_step=global_step)
-        writer.add_scalar("loss/loss", loss.numpy()[0], global_step=global_step)
+        writer.add_scalar("loss/causal_mel_loss", causal_mel_loss.numpy()[0], step=global_step)
+        writer.add_scalar("loss/non_causal_mel_loss", non_causal_mel_loss.numpy()[0], step=global_step)
+        writer.add_scalar("loss/loss", loss.numpy()[0], step=global_step)
         
         if global_step % config["report_interval"] == 0:
             text_length = int(text_lengths.numpy()[0])
@@ -124,37 +124,37 @@ def train(args, config):
 
             tag = "train_mel/ground-truth"
             img = cm.viridis(normalize(mels.numpy()[0, :num_frame].T))
-            writer.add_image(tag, img, global_step=global_step, dataformats="HWC")
+            writer.add_image(tag, img, step=global_step)
 
             tag = "train_mel/decoded"
             img = cm.viridis(normalize(decoded.numpy()[0, :num_frame].T))
-            writer.add_image(tag, img, global_step=global_step, dataformats="HWC")
+            writer.add_image(tag, img, step=global_step)
 
             tag = "train_mel/refined"
             img = cm.viridis(normalize(refined.numpy()[0, :num_frame].T))
-            writer.add_image(tag, img, global_step=global_step, dataformats="HWC")
+            writer.add_image(tag, img, step=global_step)
 
             vocoder = WaveflowVocoder()
             vocoder.model.eval()
 
             tag = "train_audio/ground-truth-waveflow"
             wav = vocoder(F.transpose(mels[0:1, :num_frame, :], (0, 2, 1)))
-            writer.add_audio(tag, wav.numpy()[0], global_step=global_step, sample_rate=22050)
+            writer.add_audio(tag, wav.numpy()[0], step=global_step, sample_rate=22050)
 
             tag = "train_audio/decoded-waveflow"
             wav = vocoder(F.transpose(decoded[0:1, :num_frame, :], (0, 2, 1)))
-            writer.add_audio(tag, wav.numpy()[0], global_step=global_step, sample_rate=22050)
+            writer.add_audio(tag, wav.numpy()[0], step=global_step, sample_rate=22050)
 
             tag = "train_audio/refined-waveflow"
             wav = vocoder(F.transpose(refined[0:1, :num_frame, :], (0, 2, 1)))
-            writer.add_audio(tag, wav.numpy()[0], global_step=global_step, sample_rate=22050)
+            writer.add_audio(tag, wav.numpy()[0], step=global_step, sample_rate=22050)
             
             attentions_np = attentions.numpy()
             attentions_np = attentions_np[:, 0, :num_frame // 4 , :text_length]
             for i, attention_layer in enumerate(np.rot90(attentions_np, axes=(1,2))):
                 tag = "train_attention/layer_{}".format(i)
                 img = cm.viridis(normalize(attention_layer))
-                writer.add_image(tag, img, global_step=global_step, dataformats="HWC")
+                writer.add_image(tag, img, step=global_step, dataformats="HWC")
 
         if global_step % config["save_interval"] == 0:
             save_parameters(writer.logdir, global_step, model, optim)