Address comment

keras/src/layers/layer.py

@@ -32,6 +32,7 @@
 from keras.src.backend import KerasTensor
 from keras.src.backend.common import global_state
 from keras.src.backend.common.name_scope import current_path
+from keras.src.backend.common.symbolic_scope import in_symbolic_scope
 from keras.src.distribution import distribution_lib
 from keras.src.dtype_policies import DTypePolicyMap
 from keras.src.layers import input_spec
@@ -1139,7 +1140,10 @@ def _get_regularization_losses(self):
         for variable in self.trainable_weights:
             if variable.regularizer is None:
                 continue
-            if backend.in_stateless_scope():
+            if backend.in_stateless_scope() and not in_symbolic_scope():
+                # If in symbolic scope, we might get `None` from
+                # `get_current_value` in `backend.compute_output_spec`. So we
+                # assign `variable` instead.
                 v = backend.get_stateless_scope().get_current_value(variable)
             else:
                 v = variable

keras/src/trainers/trainer.py

@@ -7,7 +7,6 @@
 from keras.src import ops
 from keras.src import optimizers
 from keras.src import tree
-from keras.src.backend.common.symbolic_scope import in_symbolic_scope
 from keras.src.optimizers.loss_scale_optimizer import LossScaleOptimizer
 from keras.src.saving import serialization_lib
 from keras.src.trainers.compile_utils import CompileLoss
@@ -328,12 +327,8 @@ def metrics(self):
             loss = self._compile_loss(y, y_pred, sample_weight)
             if loss is not None:
                 losses.append(loss)
-
-        # If in symbolic scope, skip `self.losses` to ensure we don't access
-        # any variables. Otherwise, it might break.
-        if not in_symbolic_scope():
-            for loss in self.losses:
-                losses.append(ops.sum(ops.cast(loss, dtype=backend.floatx())))
+        for loss in self.losses:
+            losses.append(ops.sum(ops.cast(loss, dtype=backend.floatx())))
         if backend.backend() != "jax" and len(losses) == 0:
             raise ValueError(
                 "No loss to compute. Provide a `loss` argument in `compile()`."