Merge pull request #30053 from yongtang:30040-BinaryCrossEntropy

PiperOrigin-RevId: 258598105

tensorflow/python/keras/backend.py

@@ -4328,6 +4328,7 @@ def binary_crossentropy(target, output, from_logits=False):
   if not from_logits:
     if (isinstance(output, (ops.EagerTensor, variables_module.Variable)) or
         output.op.type != 'Sigmoid'):
+      target.get_shape().assert_is_compatible_with(output.get_shape())
       epsilon_ = _constant_to_tensor(epsilon(), output.dtype.base_dtype)
       output = clip_ops.clip_by_value(output, epsilon_, 1. - epsilon_)
 

tensorflow/python/keras/distribute/distribute_strategy_test.py

@@ -846,7 +846,7 @@ class TestDistributionStrategyWithDatasets(test.TestCase,
     with self.cached_session():
       with distribution.scope():
         input_img = keras.layers.Input([64, 64, 3], name='img')
-        input_lbl = keras.layers.Input([64, 64, 1], name='lbl')
+        input_lbl = keras.layers.Input([64, 64, 2], name='lbl')
         input_weight = keras.layers.Input([64, 64], name='weight')
         predict = keras.layers.Conv2D(2, [1, 1], padding='same')(input_img)
         loss_lambda = keras.layers.Lambda(
@@ -864,7 +864,7 @@ class TestDistributionStrategyWithDatasets(test.TestCase,
         return inputs, targets
 
       fake_imgs = np.ones([50, 64, 64, 3], dtype=np.float32)
-      fake_lbls = np.ones([50, 64, 64, 1], dtype=np.float32)
+      fake_lbls = np.ones([50, 64, 64, 2], dtype=np.float32)
       fake_weights = np.ones([50, 64, 64], dtype=np.float32)
 
       data = dataset_ops.Dataset.from_tensor_slices(

tensorflow/python/keras/losses_test.py

@@ -825,6 +825,18 @@ class BinaryCrossentropyTest(test.TestCase):
     expected_value = (100.0 + 50.0 * label_smoothing) / 3.0
     self.assertAlmostEqual(self.evaluate(loss), expected_value, 3)
 
+  def test_shape_mismatch(self):
+    y_true = np.array([[1.], [1.], [1.], [0.], [1.], [0.], [0.], [1.], [1.],
+                       [0.]]).astype(np.float32)
+    y_pred = np.array([[0.], [0.], [0.], [1.], [1.], [0.], [0.], [1.], [0.],
+                       [1.]]).astype(np.float32)
+
+    bce_obj = keras.losses.BinaryCrossentropy()
+    loss = bce_obj(y_true, y_pred)
+    self.assertAlmostEqual(self.evaluate(loss), 9.23662, 3)
+    with self.assertRaisesRegexp(ValueError, 'Shapes .+ are incompatible'):
+      loss = bce_obj(np.squeeze(y_true), y_pred)
+
 
 @test_util.run_all_in_graph_and_eager_modes
 class CategoricalCrossentropyTest(test.TestCase):