diff --git a/create_submission.py b/create_submission.py
index 2716653b4df04d1042f3d8b1950d222dfb9d37fc..612fbff03fc6cc91e8f6c6e803a8dc2f8ea3996c 100644
--- a/create_submission.py
+++ b/create_submission.py
@@ -30,7 +30,7 @@ def dummy_model(X):
     # Divided by a magic number
     return X[:, :, 4:].mean(dim=2) / 26  # This is (B, T)
 
-def create_submission(model, transform, device):
+def create_submission(model, transform, device, rootDir):
     step_days = 10
     batch_size = 1024
     # We make chunks of num_days consecutive samples; As our dummy predictor
@@ -57,7 +57,7 @@ def create_submission(model, transform, device):
     num_days_test = test_loader.dataset.ntimes
 
     logging.info("= Filling in the submission file")
-    with open("submission.csv", "w") as fh_submission:
+    with open(rootDir + "submission.csv", "w") as fh_submission:
         fh_submission.write("Id,Predicted\n")
         submission_offset = 0
 
diff --git a/logs/RNN_15/best_model.pt b/logs/RNN_15/best_model.pt
index 7034d7d184d9491adee8243de7b184cb60cab5d4..8c780c8949334bc211e4cb22336d09b34a2e4bc1 100644
Binary files a/logs/RNN_15/best_model.pt and b/logs/RNN_15/best_model.pt differ
diff --git a/logs/RNN_16/best_model.pt b/logs/RNN_16/best_model.pt
index 9e1c6e16495f61163f0018aebb46b9b954c015fc..b829de358f3632b2f04079d9b0419a27f1da6d7b 100644
Binary files a/logs/RNN_16/best_model.pt and b/logs/RNN_16/best_model.pt differ
diff --git a/main.py b/main.py
index 34d114d6007f9c0d1c02592713f35361a425636a..029e3796d27f5785f40a248cb6c8d69dcd0ee2a4 100644
--- a/main.py
+++ b/main.py
@@ -132,4 +132,4 @@ if __name__ == "__main__":
             wandb.log({"val_loss": val_loss})
 
 
-    create_submission.create_submission(network, dataloader.composite_transform(dataloader.transform_remove_space_time(), dataloader.transform_min_max_scaling(MIN, MAX)), device)
+    create_submission.create_submission(network, dataloader.composite_transform(dataloader.transform_remove_space_time(), dataloader.transform_min_max_scaling(MIN, MAX)), device, rootDir)