From e395f16bfd03e0ec65dd77247f465caa69cc83ab Mon Sep 17 00:00:00 2001
From: mihirparadkar <mkp65@cornell.edu>
Date: Thu, 13 Jul 2023 21:17:16 -0700
Subject: [PATCH] Weighted metrics without metrics (#474)

* Compile weighted_metrics even if metrics is None

Fixes https://github.com/keras-team/keras-core/issues/454

* Update trainer_test.py

* fixed formatting
---
 keras_core/trainers/trainer.py      |  2 +-
 keras_core/trainers/trainer_test.py | 16 ++++++++++++++++
 2 files changed, 17 insertions(+), 1 deletion(-)

diff --git a/keras_core/trainers/trainer.py b/keras_core/trainers/trainer.py
index 6eeb85ec2..6ec53fe6e 100644
--- a/keras_core/trainers/trainer.py
+++ b/keras_core/trainers/trainer.py
@@ -44,7 +44,7 @@ class Trainer:
             )
         else:
             self._compile_loss = None
-        if metrics is not None:
+        if metrics is not None or weighted_metrics is not None:
             self._compile_metrics = CompileMetrics(
                 metrics, weighted_metrics, output_names=output_names
             )
diff --git a/keras_core/trainers/trainer_test.py b/keras_core/trainers/trainer_test.py
index 7248f4faf..ad83b66f7 100644
--- a/keras_core/trainers/trainer_test.py
+++ b/keras_core/trainers/trainer_test.py
@@ -109,6 +109,22 @@ class TestTrainer(testing.TestCase, parameterized.TestCase):
         # And those weights are tracked at the model level
         self.assertEqual(len(model.metrics_variables), 6)
 
+        # Models with only weighted_metrics should have the same 3 metrics
+        model_weighted = ModelWithMetric(units=3)
+        model_weighted.compile(
+            optimizer=optimizers.SGD(),
+            loss=losses.MeanSquaredError(),
+            weighted_metrics=[metrics.MeanSquaredError()],
+        )
+        model_weighted.fit(
+            x,
+            y,
+            batch_size=2,
+            epochs=1,
+            sample_weight=np.ones(2),
+        )
+        self.assertEqual(len(model_weighted.metrics), 3)
+
     @parameterized.named_parameters(
         [
             ("eager", True, False, False),