update code for adamw optimizer

deepxde/model.py

@@ -517,15 +517,7 @@ def outputs_losses_test(inputs, targets, auxiliary_vars):
         trainable_variables = (
             list(self.net.parameters()) + self.external_trainable_variables
         )
-        regularizer = getattr(self.net, "regularizer", None)
-        if regularizer is not None:
-            weight_decay = (
-                self.net.regularizer_value
-                if self.opt_name == "adamw"
-                else self.net.regularizer
-            )
-        else:
-            weight_decay = None
+        weight_decay = getattr(self.net, "regularizer", None)
         self.opt = optimizers.get(
             trainable_variables,
             self.opt_name,

deepxde/optimizers/paddle/optimizers.py

@@ -62,11 +62,13 @@ def get(params, optimizer, learning_rate=None, decay=None, weight_decay=None):
             weight_decay=weight_decay,
         )
     if optimizer == "adamw":
-        if weight_decay[0] == 0:
-            raise ValueError("AdamW optimizer requires non-zero weight decay")
-        return paddle.optimizer.AdamW(
-            learning_rate=learning_rate,
-            parameters=params,
-            weight_decay=weight_decay[0],
-        )
+        if isinstance(weight_decay, paddle.regularizer.L2Decay):
+            if weight_decay._coeff == 0:
+                raise ValueError("AdamW optimizer requires non-zero weight decay")
+            return paddle.optimizer.AdamW(
+                learning_rate=learning_rate,
+                parameters=params,
+                weight_decay=weight_decay._coeff,
+            )
+        raise ValueError("AdamW optimizer requires l2 regularizer")
     raise NotImplementedError(f"{optimizer} to be implemented for backend Paddle.")