early stopping

main.py

@@ -1,10 +1,10 @@
 import argparse
 
 import pytorch_lightning as pl
+from pytorch_lightning.callbacks import EarlyStopping
 
 from dataloader import create_named_dataloader
 from model import ColorTransformerModel
-from pytorch_lightning.callbacks import EarlyStopping
 
 
 def parse_args():
@@ -44,10 +44,10 @@ def parse_args():
 if __name__ == "__main__":
     args = parse_args()
     early_stop_callback = EarlyStopping(
-        monitor='hp_metric',  # Metric to monitor
-        min_delta=0,      # Minimum change in the monitored quantity to qualify as an improvement
-        patience=50,          # Number of epochs with no improvement after which training will be stopped
-        mode='min',           # Mode can be either 'min' for minimizing the monitored quantity or 'max' for maximizing it.
+        monitor="hp_metric",  # Metric to monitor
+        min_delta=0,  # Minimum change in the monitored quantity to qualify as an improvement
+        patience=50,  # Number of epochs with no improvement after which training will be stopped
+        mode="min",  # Mode can be either 'min' for minimizing the monitored quantity or 'max' for maximizing it.
         verbose=True,
     )