Refactor code using black

lululxvi · Dec 11, 2024 · 01429e3 · 01429e3
1 parent 95de72a
commit 01429e3
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Showing 2 changed files with 24 additions and 8 deletions.
diff --git a/deepxde/model.py b/deepxde/model.py
@@ -517,13 +517,21 @@ def outputs_losses_test(inputs, targets, auxiliary_vars):
         trainable_variables = (
             list(self.net.parameters()) + self.external_trainable_variables
         )
-        regularizer = getattr(self.net, 'regularizer', None)
+        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 = (
+                self.net.regularizer_value
+                if self.opt_name == "adamw"
+                else self.net.regularizer
+            )
+        else:
             weight_decay = None
         self.opt = optimizers.get(
-            trainable_variables, self.opt_name, learning_rate=lr, decay=decay, weight_decay=weight_decay,
+            trainable_variables,
+            self.opt_name,
+            learning_rate=lr,
+            decay=decay,
+            weight_decay=weight_decay,
         )
 
         def train_step(inputs, targets, auxiliary_vars):

diff --git a/deepxde/optimizers/paddle/optimizers.py b/deepxde/optimizers/paddle/optimizers.py
@@ -48,17 +48,25 @@ def get(params, optimizer, learning_rate=None, decay=None, weight_decay=None):
         learning_rate = _get_lr_scheduler(learning_rate, decay)
 
     if optimizer == "adam":
-        return paddle.optimizer.Adam(learning_rate=learning_rate, parameters=params, weight_decay=weight_decay)
+        return paddle.optimizer.Adam(
+            learning_rate=learning_rate, parameters=params, weight_decay=weight_decay
+        )
     elif optimizer == "sgd":
-        return paddle.optimizer.SGD(learning_rate=learning_rate, parameters=params, weight_decay=weight_decay)
+        return paddle.optimizer.SGD(
+            learning_rate=learning_rate, parameters=params, weight_decay=weight_decay
+        )
     elif optimizer == "rmsprop":
         return paddle.optimizer.RMSProp(
-            learning_rate=learning_rate, parameters=params, weight_decay=weight_decay,
+            learning_rate=learning_rate,
+            parameters=params,
+            weight_decay=weight_decay,
         )
     elif 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],
+            learning_rate=learning_rate,
+            parameters=params,
+            weight_decay=weight_decay[0],
         )
     raise NotImplementedError(f"{optimizer} to be implemented for backend Paddle.")