Keep "mse" as the metric name in the log (#812)

* add the test * add the fix * fix other broken tests --------- Co-authored-by: Haifeng Jin <[email protected]>
keras-team · Aug 29, 2023 · ca1a177 · ca1a177
1 parent 4e35327
commit ca1a177
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Showing 4 changed files with 24 additions and 17 deletions.
diff --git a/keras_core/models/model_test.py b/keras_core/models/model_test.py
@@ -220,17 +220,16 @@ def test_functional_list_outputs_list_losses_abbr(self):
         hist = model.fit(x, (y1, y2), batch_size=2, epochs=1, verbose=0)
         hist_keys = sorted(hist.history.keys())
         # TODO `tf.keras` also outputs individual losses for outputs
-        # TODO Align output names with 'bce', `mse`, `mae` of `tf.keras`
         ref_keys = sorted(
             [
                 "loss",
                 # "output_a_loss",
-                "output_a_binary_crossentropy",
-                "output_a_mean_absolute_error",
-                "output_a_mean_squared_error",
+                "output_a_bce",
+                "output_a_mae",
+                "output_a_mse",
                 "output_b_acc",
                 # "output_b_loss",
-                "output_b_mean_squared_error",
+                "output_b_mse",
             ]
         )
         self.assertListEqual(hist_keys, ref_keys)

diff --git a/keras_core/trainers/compile_utils.py b/keras_core/trainers/compile_utils.py
@@ -79,13 +79,14 @@ def get_metric(identifier, y_true, y_pred):
             )
 
     if not isinstance(metric_obj, metrics_module.Metric):
-        if isinstance(identifier, str):
-            metric_name = identifier
-        else:
-            metric_name = get_object_name(metric_obj)
-        metric_obj = metrics_module.MeanMetricWrapper(
-            metric_obj, name=metric_name
-        )
+        metric_obj = metrics_module.MeanMetricWrapper(metric_obj)
+
+    if isinstance(identifier, str):
+        metric_name = identifier
+    else:
+        metric_name = get_object_name(metric_obj)
+    metric_obj.name = metric_name
+
     return metric_obj
 
 

diff --git a/keras_core/trainers/compile_utils_test.py b/keras_core/trainers/compile_utils_test.py
@@ -198,7 +198,7 @@ def test_dict_output_case(self):
 
     def test_name_conversions(self):
         compile_metrics = CompileMetrics(
-            metrics=["acc", "accuracy"],
+            metrics=["acc", "accuracy", "mse"],
             weighted_metrics=[],
         )
         y_true = np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]])
@@ -207,9 +207,10 @@ def test_name_conversions(self):
         compile_metrics.update_state(y_true, y_pred, sample_weight=None)
         result = compile_metrics.result()
         self.assertTrue(isinstance(result, dict))
-        self.assertEqual(len(result), 2)
+        self.assertEqual(len(result), 3)
         self.assertAllClose(result["acc"], 0.333333)
         self.assertAllClose(result["accuracy"], 0.333333)
+        self.assertTrue("mse" in result)
 
 
 class TestCompileLoss(testing.TestCase, parameterized.TestCase):

diff --git a/keras_core/trainers/trainer_test.py b/keras_core/trainers/trainer_test.py
@@ -525,7 +525,9 @@ def on_predict_batch_end(self, batch, logs=None):
                 assert keys == ["outputs"]
 
         model = ExampleModel(units=3)
-        model.compile(optimizer="adam", loss="mse", metrics=["mae"])
+        model.compile(
+            optimizer="adam", loss="mse", metrics=["mean_absolute_error"]
+        )
         x = np.ones((16, 4))
         y = np.zeros((16, 3))
         x_test = np.ones((16, 4))
@@ -651,12 +653,16 @@ def test_recompile(self):
         inputs = layers.Input((2,))
         outputs = layers.Dense(3)(inputs)
         model = keras_core.Model(inputs, outputs)
-        model.compile(optimizer="sgd", loss="mse", metrics=["mse"])
+        model.compile(
+            optimizer="sgd", loss="mse", metrics=["mean_squared_error"]
+        )
         history_1 = model.fit(np.ones((3, 2)), np.ones((3, 3))).history
         eval_out_1 = model.evaluate(
             np.ones((3, 2)), np.ones((3, 3)), return_dict=True
         )
-        model.compile(optimizer="sgd", loss="mse", metrics=["mae"])
+        model.compile(
+            optimizer="sgd", loss="mse", metrics=["mean_absolute_error"]
+        )
         history_2 = model.fit(np.ones((3, 2)), np.ones((3, 3))).history
         eval_out_2 = model.evaluate(
             np.ones((3, 2)), np.ones((3, 3)), return_dict=True