Make regularization factor required

Author: vl-dud

deepxde/nn/regularizers.py

@@ -14,27 +14,25 @@ def get(identifier):
     """Retrieves a TensorFlow regularizer instance based on the given identifier.
 
     Args:
-        identifier (str or list/tuple): Specifies the type of regularizer and
-            the optional scale values. If a string, it should be one of "l1",
-            "l2", "orthogonal", or "l1l2" ("l1+l2"); default scale values will
-            be used. If a list or tuple, the first element should be one of
-            the above strings, followed by scale values. For "l1", "l2", or
-            "orthogonal", you can provide a single scale value. For "l1l2" ("l1+l2"),
-            you can provide both "l1" and "l2" scale values.
+        identifier (list/tuple): Specifies the type of regularizer and
+            regularization factor. The first element should be one of "l1", "l2",
+            "orthogonal", or "l1l2" ("l1+l2"). For "l1", "l2", or "orthogonal",
+            you can provide a single factor value. For "l1l2" ("l1+l2"),
+            both "l1" and "l2" factors are required.
     """
 
     # TODO: other backends
     if identifier is None:
         return None
 
-    if isinstance(identifier, str):
-        name = identifier.lower()
-        scales = []
-    elif isinstance(identifier, (list, tuple)) and identifier:
+    if isinstance(identifier, (list, tuple)) and identifier:
         name = identifier[0].lower()
-        scales = identifier[1:]
+        factor = identifier[1:]
     else:
-        raise ValueError("Identifier must be a string or a non-empty list or tuple.")
+        raise ValueError("Identifier must be a non-empty list or tuple.")
+
+    if not factor:
+        raise ValueError("Regularization factor must be provided.")
 
     regularizer_class = REGULARIZER_DICT.get(name)
     if not regularizer_class:
@@ -46,16 +44,15 @@ def get(identifier):
         raise ValueError(f"Unknown regularizer name: {name}")
 
     regularizer_kwargs = {}
-    if scales:
-        if name == "l1":
-            regularizer_kwargs["l1"] = scales[0]
-        elif name == "l2":
-            regularizer_kwargs["l2"] = scales[0]
-        elif name == "orthogonal":
-            regularizer_kwargs["factor"] = scales[0]
-        elif name in ("l1l2", "l1+l2"):
-            if len(scales) < 2:
-                raise ValueError("L1L2 regularizer requires both L1/L2 penalties.")
-            regularizer_kwargs["l1"] = scales[0]
-            regularizer_kwargs["l2"] = scales[1]
+    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"):
+        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)