diff --git a/face_swapper/README.md b/face_swapper/README.md
index 633b832..fc19b7a 100644
--- a/face_swapper/README.md
+++ b/face_swapper/README.md
@@ -63,12 +63,12 @@ kernel_size = 4
 
 ```
 [training.losses]
-weight_adversarial = 1.5
-weight_identity = 15
-weight_attribute = 10
-weight_reconstruction = 15
-weight_pose = 0
-weight_gaze = 0
+adversarial_weight = 1.5
+attribute_weight = 10
+reconstruction_weight = 15
+identity_weight = 15
+pose_weight = 0
+gaze_weight = 0
 ```
 
 ```
diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py
index b66a2e6..1f85614 100644
--- a/face_swapper/src/models/loss.py
+++ b/face_swapper/src/models/loss.py
@@ -141,6 +141,28 @@ class FaceSwapperLoss:
 		return translation, scale, rotation
 
 
+class ReconstructionLoss(torch.nn.Module):
+	def __init__(self) -> None:
+		super(ReconstructionLoss, self).__init__()
+
+	def calc(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tensor:
+		batch_size = CONFIG.getint('training.loader', 'batch_size')
+
+		loss_tensor = torch.pow(output_tensor - target_tensor, 2).reshape(batch_size, -1)
+		loss_tensor = torch.mean(loss_tensor, dim = 1) * 0.5
+
+		if torch.equal(source_tensor, target_tensor):
+			loss_tensor = torch.sum(loss_tensor * torch.tensor(0)) / (torch.tensor(0).sum() + 1e-4)
+		else:
+			loss_tensor = torch.sum(loss_tensor * torch.tensor(1)) / (torch.tensor(1).sum() + 1e-4)
+
+		data_range = float(torch.max(output_tensor) - torch.min(output_tensor))
+		similarity = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean()
+
+		loss_tensor = (loss_tensor + similarity) * 0.5
+		return loss_tensor
+
+
 class IdentityLoss(torch.nn.Module):
 	def __init__(self) -> None:
 		super(IdentityLoss, self).__init__()
@@ -148,8 +170,8 @@ class IdentityLoss(torch.nn.Module):
 		self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call]
 		self.embedder.eval()
 
-	def calc_loss(self, source_tensor : Tensor, output_tensor : Tensor) -> Tensor:
+	def calc(self, source_tensor : Tensor, output_tensor : Tensor) -> Tensor:
 		output_embedding = calc_embedding(self.embedder, output_tensor, (30, 0, 10, 10))
 		source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10))
-		loss = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean()
-		return loss
+		loss_tensor = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean()
+		return loss_tensor
diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py
index 169e6d8..4e3e0c2 100644
--- a/face_swapper/src/training.py
+++ b/face_swapper/src/training.py
@@ -16,7 +16,7 @@ from .dataset import DynamicDataset
 from .helper import calc_embedding
 from .models.discriminator import Discriminator
 from .models.generator import Generator
-from .models.loss import FaceSwapperLoss, IdentityLoss
+from .models.loss import FaceSwapperLoss, IdentityLoss, ReconstructionLoss
 from .types import Batch, Embedding, VisionTensor
 
 CONFIG = configparser.ConfigParser()
@@ -31,6 +31,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss):
 
 		self.generator = Generator()
 		self.discriminator = Discriminator()
+		self.reconstruction_loss = ReconstructionLoss()
 		self.identity_loss = IdentityLoss()
 		self.automatic_optimization = automatic_optimization
 
@@ -76,18 +77,24 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss):
 		self.log('loss_adversarial', generator_loss_set.get('loss_adversarial'))
 		self.log('loss_attribute', generator_loss_set.get('loss_attribute'))
 		self.log('loss_identity', generator_loss_set.get('loss_identity'), prog_bar = True)
-		self.log('loss_reconstruction', generator_loss_set.get('loss_reconstruction'))
+		self.log('loss_reconstruction', generator_loss_set.get('loss_reconstruction'), prog_bar = True)
 
-		identity_loss = self.identity_loss.calc_loss(generator_output_tensor, source_tensor)
-		generator_loss = self.calc_generator_loss_new(identity_loss)
+		reconstruction_loss = self.reconstruction_loss.calc(source_tensor, target_tensor, generator_output_tensor)
+		identity_loss = self.identity_loss.calc(generator_output_tensor, source_tensor)
+		generator_loss = self.calc_generator_loss_new(reconstruction_loss, identity_loss)
 
-		self.log('loss_generator_new', generator_loss, prog_bar = True)
+		self.log('generator_loss_new', generator_loss, prog_bar = True)
+		self.log('loss_reconstruction_new', reconstruction_loss, prog_bar = True)
 		self.log('loss_identity_new', identity_loss, prog_bar = True)
 		return generator_loss_set.get('loss_generator')
 
-	def calc_generator_loss_new(self, identity_loss : Tensor) -> Tensor:
-		weight_identity = CONFIG.getfloat('training.losses', 'weight_identity')
-		generator_loss = identity_loss * weight_identity
+	@staticmethod
+	def calc_generator_loss_new(reconstruction_loss : Tensor, identity_loss : Tensor) -> Tensor:
+		reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight')
+		identity_weight = CONFIG.getfloat('training.losses', 'identity_weight')
+
+		generator_loss = reconstruction_loss * reconstruction_weight
+		generator_loss += identity_loss * identity_weight
 
 		return generator_loss