diff --git a/train_db.py b/train_db.py
index c210767b..a3154cd1 100644
--- a/train_db.py
+++ b/train_db.py
@@ -206,6 +206,7 @@ def train(args):
   if accelerator.is_main_process:
     accelerator.init_trackers("dreambooth")
 
+  loss_list = []
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
     train_dataset.set_current_epoch(epoch + 1)
@@ -216,7 +217,6 @@ def train(args):
     if args.gradient_checkpointing or global_step < args.stop_text_encoder_training:
       text_encoder.train()
 
-    loss_total = 0
     for step, batch in enumerate(train_dataloader):
       # 指定したステップ数でText Encoderの学習を止める
       if global_step == args.stop_text_encoder_training:
@@ -291,8 +291,11 @@ def train(args):
         logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
         accelerator.log(logs, step=global_step)
 
-      loss_total += current_loss
-      avr_loss = loss_total / (step+1)
+      if epoch == 0:
+        loss_list.append(current_loss)
+      else:
+        loss_list[step] = current_loss
+      avr_loss = sum(loss_list) / len(loss_list)
       logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
       progress_bar.set_postfix(**logs)
 
@@ -300,7 +303,7 @@ def train(args):
         break
 
     if args.logging_dir is not None:
-      logs = {"epoch_loss": loss_total / len(train_dataloader)}
+      logs = {"epoch_loss": sum(loss_list) / len(loss_list)}
       accelerator.log(logs, step=epoch+1)
 
     accelerator.wait_for_everyone()
diff --git a/train_network.py b/train_network.py
index bb3159fd..c9c3c468 100644
--- a/train_network.py
+++ b/train_network.py
@@ -378,6 +378,7 @@ def train(args):
   if accelerator.is_main_process:
     accelerator.init_trackers("network_train")
 
+  loss_list = []
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
     train_dataset.set_current_epoch(epoch + 1)
@@ -386,7 +387,6 @@ def train(args):
 
     network.on_epoch_start(text_encoder, unet)
 
-    loss_total = 0
     for step, batch in enumerate(train_dataloader):
       with accelerator.accumulate(network):
         with torch.no_grad():
@@ -446,8 +446,11 @@ def train(args):
         global_step += 1
 
       current_loss = loss.detach().item()
-      loss_total += current_loss
-      avr_loss = loss_total / (step+1)
+      if epoch == 0:
+        loss_list.append(current_loss)
+      else:
+        loss_list[step] = current_loss
+      avr_loss = sum(loss_list) / len(loss_list)
       logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
       progress_bar.set_postfix(**logs)
 
@@ -459,7 +462,7 @@ def train(args):
         break
 
     if args.logging_dir is not None:
-      logs = {"loss/epoch": loss_total / len(train_dataloader)}
+      logs = {"loss/epoch": sum(loss_list) / len(loss_list)}
       accelerator.log(logs, step=epoch+1)
 
     accelerator.wait_for_everyone()