combing training script

scripts/train.py

@@ -200,6 +200,7 @@ def train_and_eval(
     weights: str = None,
     save_model=False,
     verbose=False,
+    **kwargs,
 ):
     classifier = tails.models.DNN(name=model_name)
 
@@ -208,15 +209,18 @@ def train_and_eval(
     # convert position RMSE to pixels
     position_rmse = PositionRootMeanSquarredError(scaling_factor=scaling_factor)
 
+    learning_rate = kwargs.get("learning_rate", 3e-4)
+    patience = kwargs.get("patience", 30)
+
     classifier.setup(
         input_shape=input_shape,
         n_output_neurons=3,
         architecture="tails",
         loss=tails_loss,
         optimizer="adam",
-        lr=3e-4,  # epsilon=1e-3, beta_1=0.7,
+        lr=learning_rate,  # epsilon=1e-3, beta_1=0.7,
         metrics=[label_accuracy, position_rmse],
-        patience=30,
+        patience=patience,
         monitor="val_position_rmse",
         restore_best_weights=True,
         callbacks=("early_stopping", "tensorboard"),