diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py
index 62a8fae..6ed1a4f 100644
--- a/embedding_converter/src/dataset.py
+++ b/embedding_converter/src/dataset.py
@@ -9,13 +9,13 @@ from torchvision import transforms
 from .types import Batch
 
 
-class DynamicDataset(Dataset[Tensor]):
+class StaticDataset(Dataset[Tensor]):
 	def __init__(self, file_pattern : str) -> None:
 		self.file_paths = glob.glob(file_pattern)
 		self.transforms = self.compose_transforms()
 
 	def __getitem__(self, index : int) -> Batch:
-		file_path = random.choice(self.file_paths)
+		file_path = self.file_paths[index]
 		vision_frame = cv2.imread(file_path)
 		return self.transforms(vision_frame)
 
diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py
index 14358fa..b43429b 100644
--- a/embedding_converter/src/training.py
+++ b/embedding_converter/src/training.py
@@ -11,7 +11,7 @@ from lightning.pytorch.tuner import Tuner
 from torch import Tensor, nn
 from torch.utils.data import DataLoader, Dataset, random_split
 
-from .dataset import DynamicDataset
+from .dataset import StaticDataset
 from .models.embedding_converter import EmbeddingConverter
 from .types import Batch, Embedding, OptimizerConfig
 
@@ -24,11 +24,13 @@ class EmbeddingConverterTrainer(lightning.LightningModule):
 		super(EmbeddingConverterTrainer, self).__init__()
 		source_path = CONFIG.get('training.model', 'source_path')
 		target_path = CONFIG.get('training.model', 'target_path')
+		learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate')
 
 		self.embedding_converter = EmbeddingConverter()
 		self.source_embedder = torch.jit.load(source_path, map_location = 'cpu') # type:ignore[no-untyped-call]
 		self.target_embedder = torch.jit.load(target_path, map_location = 'cpu') # type:ignore[no-untyped-call]
 		self.mse_loss = nn.MSELoss()
+		self.lr = learning_rate
 
 	def forward(self, source_embedding : Embedding) -> Embedding:
 		return self.embedding_converter(source_embedding)
@@ -115,7 +117,7 @@ def train() -> None:
 	dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern')
 	output_resume_path = CONFIG.get('training.output', 'resume_path')
 
-	dataset = DynamicDataset(dataset_file_pattern)
+	dataset = StaticDataset(dataset_file_pattern)
 	training_loader, validation_loader = create_loaders(dataset)
 	embedding_converter_trainer = EmbeddingConverterTrainer()
 	trainer = create_trainer()