Simplify the code

deepxde/nn/regularizers.py

@@ -1,14 +1,5 @@
 from ..backend import tf
 
-REGULARIZER_DICT = {
-    "l1": tf.keras.regularizers.L1,
-    "l2": tf.keras.regularizers.L2,
-    "l1l2": tf.keras.regularizers.L1L2,
-    "l1+l2": tf.keras.regularizers.L1L2,
-}
-if hasattr(tf.keras.regularizers, "OrthogonalRegularizer"):
-    REGULARIZER_DICT["orthogonal"] = tf.keras.regularizers.OrthogonalRegularizer
-
 
 def get(identifier):
     """Retrieves a TensorFlow regularizer instance based on the given identifier.
@@ -24,35 +15,27 @@ def get(identifier):
     # TODO: other backends
     if identifier is None or not identifier:
         return None
+    if not isinstance(identifier, (list, tuple)):
+        raise ValueError("Identifier must be a list or a tuple.")
 
-    if isinstance(identifier, (list, tuple)):
-        name = identifier[0].lower()
-        factor = identifier[1:]
-    else:
-        raise ValueError("Identifier must be a non-empty list or tuple.")
-
+    name = identifier[0].lower()
+    factor = identifier[1:]
     if not factor:
         raise ValueError("Regularization factor must be provided.")
 
-    regularizer_class = REGULARIZER_DICT.get(name)
-    if not regularizer_class:
-        if name == "orthogonal":
+    if name == "l1":
+        return tf.keras.regularizers.L1(l1=factor[0])
+    if name == "l2":
+        return tf.keras.regularizers.L2(l2=factor[0])
+    if name == "orthogonal":
+        if not hasattr(tf.keras.regularizers, "OrthogonalRegularizer"):
             raise ValueError(
                 "The 'orthogonal' regularizer is not available "
                 "in your version of TensorFlow"
             )
-        raise ValueError(f"Unknown regularizer name: {name}")
-
-    regularizer_kwargs = {}
-    if name == "l1":
-        regularizer_kwargs["l1"] = factor[0]
-    elif name == "l2":
-        regularizer_kwargs["l2"] = factor[0]
-    elif name == "orthogonal":
-        regularizer_kwargs["factor"] = factor[0]
-    elif name in ("l1l2", "l1+l2"):
+        return tf.keras.regularizers.OrthogonalRegularizer(factor=factor[0])
+    if name in ("l1l2", "l1+l2"):
         if len(factor) < 2:
             raise ValueError("L1L2 regularizer requires both L1/L2 penalties.")
-        regularizer_kwargs["l1"] = factor[0]
-        regularizer_kwargs["l2"] = factor[1]
-    return regularizer_class(**regularizer_kwargs)
+        return tf.keras.regularizers.L1L2(l1=factor[0], l2=factor[1])
+    raise ValueError(f"Unknown regularizer name: {name}")