Use sparse_categorical_crossentropy for test as the loss object default does not work on tpustrategy +

the single task trainer already handles the reduction.

PiperOrigin-RevId: 367757677

Author: saberkun

orbit/examples/single_task/single_task_trainer.py

@@ -107,6 +107,10 @@ def train_fn(inputs):
         # replicas. This ensures that we don't end up multiplying our loss by
         # the number of workers - gradients are summed, not averaged, across
         # replicas during the apply_gradients call.
+        # Note, the reduction of loss is explicitly handled and scaled by
+        # num_replicas_in_sync. Recommend to use a plain loss function.
+        # If you're using tf.keras.losses.Loss object, you may need to set
+        # reduction argument explicitly.
         loss = tf.reduce_mean(self.loss_fn(target, output))
         scaled_loss = loss / self.strategy.num_replicas_in_sync
 

orbit/examples/single_task/single_task_trainer_test.py

@@ -30,14 +30,15 @@ def test_single_task_training(self):
         tf.keras.Input(shape=(4,), name='features'),
         tf.keras.layers.Dense(10, activation=tf.nn.relu),
         tf.keras.layers.Dense(10, activation=tf.nn.relu),
-        tf.keras.layers.Dense(3)
+        tf.keras.layers.Dense(3),
+        tf.keras.layers.Softmax(),
     ])
 
     trainer = single_task_trainer.SingleTaskTrainer(
         train_ds,
         label_key='label',
         model=model,
-        loss_fn=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+        loss_fn=tf.keras.losses.sparse_categorical_crossentropy,
         optimizer=tf.keras.optimizers.SGD(learning_rate=0.01))
 
     controller = orbit.Controller(