diff --git a/hyperswap/README.md b/hyperswap/README.md
index eb773de..03d6d0e 100644
--- a/hyperswap/README.md
+++ b/hyperswap/README.md
@@ -89,19 +89,34 @@ mask_weight = 5.0
 ```
 [training.trainer]
 accumulate_size = 4
-generator_learning_rate = 0.0004
-discriminator_learning_rate = 0.0002
-momentum = 0.5
 gradient_clip = 20.0
 noise_factor = 0.05
-scheduler_factor = 0.7
-scheduler_patience = 2000
 max_epochs = 50
 strategy = auto
 precision = 16-mixed
+preview_frequency = 100
+```
+
+```
+[training.logger]
 logger_path = .logs
 logger_name = hyperswap
-preview_frequency = 100
+```
+
+```
+[training.generator]
+learning_rate = 0.0004
+momentum = 0.5
+scheduler_factor = 0.7
+scheduler_patience = 2000
+```
+
+```
+[training.discriminator]
+learning_rate = 0.0002
+momentum = 0.5
+scheduler_factor = 0.7
+scheduler_patience = 2000
 ```
 
 ```
diff --git a/hyperswap/config.ini b/hyperswap/config.ini
index f93daa9..2f37ba9 100644
--- a/hyperswap/config.ini
+++ b/hyperswap/config.ini
@@ -47,19 +47,28 @@ mask_weight =
 
 [training.trainer]
 accumulate_size =
-generator_learning_rate =
-discriminator_learning_rate =
-momentum =
 gradient_clip =
 noise_factor =
-scheduler_factor =
-scheduler_patience =
 max_epochs =
 strategy =
 precision =
+preview_frequency =
+
+[training.logger]
 logger_path =
 logger_name =
-preview_frequency =
+
+[training.generator]
+learning_rate =
+momentum =
+scheduler_factor =
+scheduler_patience =
+
+[training.discriminator]
+learning_rate =
+momentum =
+scheduler_factor =
+scheduler_patience =
 
 [training.output]
 directory_path =
diff --git a/hyperswap/src/training.py b/hyperswap/src/training.py
index 0559111..442650f 100644
--- a/hyperswap/src/training.py
+++ b/hyperswap/src/training.py
@@ -35,11 +35,14 @@ class HyperSwapTrainer(LightningModule):
 		self.config_face_masker_path = config_parser.get('training.model', 'face_masker_path')
 		self.config_noise_factor = config_parser.getfloat('training.trainer', 'noise_factor')
 		self.config_accumulate_size = config_parser.getfloat('training.trainer', 'accumulate_size')
-		self.config_generator_learning_rate = config_parser.getfloat('training.trainer', 'generator_learning_rate')
-		self.config_discriminator_learning_rate = config_parser.getfloat('training.trainer', 'discriminator_learning_rate')
-		self.config_momentum = config_parser.getfloat('training.trainer', 'momentum')
-		self.config_scheduler_factor = config_parser.getfloat('training.trainer', 'scheduler_factor')
-		self.config_scheduler_patience = config_parser.getint('training.trainer', 'scheduler_patience')
+		self.config_generator_learning_rate = config_parser.getfloat('training.generator', 'learning_rate')
+		self.config_discriminator_learning_rate = config_parser.getfloat('training.discriminator', 'learning_rate')
+		self.config_generator_momentum = config_parser.getfloat('training.generator', 'momentum')
+		self.config_discriminator_momentum = config_parser.getfloat('training.discriminator', 'momentum')
+		self.config_generator_scheduler_factor = config_parser.getfloat('training.generator', 'scheduler_factor')
+		self.config_discriminator_scheduler_factor = config_parser.getfloat('training.discriminator', 'scheduler_factor')
+		self.config_generator_scheduler_patience = config_parser.getint('training.generator', 'scheduler_patience')
+		self.config_discriminator_scheduler_patience = config_parser.getint('training.discriminator', 'scheduler_patience')
 		self.config_gradient_clip = config_parser.getfloat('training.trainer', 'gradient_clip')
 		self.config_preview_frequency = config_parser.getint('training.trainer', 'preview_frequency')
 		self.generator_embedder = torch.jit.load(self.config_generator_embedder_path, map_location = 'cpu').eval()
@@ -66,10 +69,10 @@ class HyperSwapTrainer(LightningModule):
 		return output_tensor, output_mask
 
 	def configure_optimizers(self) -> Tuple[OptimizerSet, OptimizerSet]:
-		generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = self.config_generator_learning_rate, betas = (self.config_momentum, 0.999), weight_decay = 1e-4, eps = 1e-8)
-		discriminator_optimizer = torch.optim.AdamW(self.discriminator.parameters(), lr = self.config_discriminator_learning_rate, betas = (self.config_momentum, 0.999), weight_decay = 1e-4, eps = 1e-8)
-		generator_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(generator_optimizer, mode = 'min', factor = self.config_scheduler_factor, patience = self.config_scheduler_patience, min_lr = 1e-8)
-		discriminator_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(discriminator_optimizer, mode = 'min', factor = self.config_scheduler_factor, patience = self.config_scheduler_patience, min_lr = 1e-8)
+		generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = self.config_generator_learning_rate, betas = (self.config_generator_momentum, 0.999), weight_decay = 1e-4, eps = 1e-8)
+		discriminator_optimizer = torch.optim.AdamW(self.discriminator.parameters(), lr = self.config_discriminator_learning_rate, betas = (self.config_discriminator_momentum, 0.999), weight_decay = 1e-4, eps = 1e-8)
+		generator_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(generator_optimizer, mode = 'min', factor = self.config_generator_scheduler_factor, patience = self.config_generator_scheduler_patience, min_lr = 1e-8)
+		discriminator_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(discriminator_optimizer, mode = 'min', factor = self.config_discriminator_scheduler_factor, patience = self.config_discriminator_scheduler_patience, min_lr = 1e-8)
 
 		generator_config =\
 		{
@@ -234,8 +237,8 @@ def create_trainer() -> Trainer:
 	config_max_epochs = CONFIG_PARSER.getint('training.trainer', 'max_epochs')
 	config_strategy = CONFIG_PARSER.get('training.trainer', 'strategy')
 	config_precision = CONFIG_PARSER.get('training.trainer', 'precision')
-	config_logger_path = CONFIG_PARSER.get('training.trainer', 'logger_path')
-	config_logger_name = CONFIG_PARSER.get('training.trainer', 'logger_name')
+	config_logger_path = CONFIG_PARSER.get('training.logger', 'logger_path')
+	config_logger_name = CONFIG_PARSER.get('training.logger', 'logger_name')
 	config_directory_path = CONFIG_PARSER.get('training.output', 'directory_path')
 	config_file_pattern = CONFIG_PARSER.get('training.output', 'file_pattern')
 	logger = TensorBoardLogger(config_logger_path, config_logger_name)