Fix ReLU supports_masking flag.

PiperOrigin-RevId: 343335081
Change-Id: I4a580e3a62630b26d22ad2028f1a096f626524cb

tensorflow/python/keras/layers/advanced_activations.py

@@ -408,7 +408,7 @@ class ReLU(Layer):
       raise ValueError('threshold of Relu layer '
                        'cannot be None. Required a float')
 
-    self.support_masking = True
+    self.supports_masking = True
     if max_value is not None:
       max_value = K.cast_to_floatx(max_value)
     self.max_value = max_value

tensorflow/python/keras/layers/advanced_activations_test.py

@@ -34,37 +34,44 @@ class AdvancedActivationsTest(keras_parameterized.TestCase):
     for alpha in [0., .5, -1.]:
       testing_utils.layer_test(keras.layers.LeakyReLU,
                                kwargs={'alpha': alpha},
-                               input_shape=(2, 3, 4))
+                               input_shape=(2, 3, 4),
+                               supports_masking=True)
 
   def test_prelu(self):
     testing_utils.layer_test(keras.layers.PReLU, kwargs={},
-                             input_shape=(2, 3, 4))
+                             input_shape=(2, 3, 4),
+                             supports_masking=True)
 
   def test_prelu_share(self):
     testing_utils.layer_test(keras.layers.PReLU,
                              kwargs={'shared_axes': 1},
-                             input_shape=(2, 3, 4))
+                             input_shape=(2, 3, 4),
+                             supports_masking=True)
 
   def test_elu(self):
     for alpha in [0., .5, -1.]:
       testing_utils.layer_test(keras.layers.ELU,
                                kwargs={'alpha': alpha},
-                               input_shape=(2, 3, 4))
+                               input_shape=(2, 3, 4),
+                               supports_masking=True)
 
   def test_thresholded_relu(self):
     testing_utils.layer_test(keras.layers.ThresholdedReLU,
                              kwargs={'theta': 0.5},
-                             input_shape=(2, 3, 4))
+                             input_shape=(2, 3, 4),
+                             supports_masking=True)
 
   def test_softmax(self):
     testing_utils.layer_test(keras.layers.Softmax,
                              kwargs={'axis': 1},
-                             input_shape=(2, 3, 4))
+                             input_shape=(2, 3, 4),
+                             supports_masking=True)
 
   def test_relu(self):
     testing_utils.layer_test(keras.layers.ReLU,
                              kwargs={'max_value': 10},
-                             input_shape=(2, 3, 4))
+                             input_shape=(2, 3, 4),
+                             supports_masking=True)
     x = keras.backend.ones((3, 4))
     if not context.executing_eagerly():
       # Test that we use `leaky_relu` when appropriate in graph mode.
@@ -80,7 +87,8 @@ class AdvancedActivationsTest(keras_parameterized.TestCase):
         ValueError, 'max_value of Relu layer cannot be negative value: -10'):
       testing_utils.layer_test(keras.layers.ReLU,
                                kwargs={'max_value': -10},
-                               input_shape=(2, 3, 4))
+                               input_shape=(2, 3, 4),
+                               supports_masking=True)
     with self.assertRaisesRegex(
         ValueError,
         'negative_slope of Relu layer cannot be negative value: -2'):

tensorflow/python/keras/testing_utils.py

@@ -100,7 +100,8 @@ def layer_test(layer_cls,
                validate_training=True,
                adapt_data=None,
                custom_objects=None,
-               test_harness=None):
+               test_harness=None,
+               supports_masking=None):
   """Test routine for a layer with a single input and single output.
 
   Arguments:
@@ -122,6 +123,8 @@ def layer_test(layer_cls,
       in the layer class. This is helpful for testing custom layers.
     test_harness: The Tensorflow test, if any, that this function is being
       called in.
+    supports_masking: Optional boolean to check the `supports_masking` property
+      of the layer. If None, the check will not be performed.
 
   Returns:
     The output data (Numpy array) returned by the layer, for additional
@@ -165,6 +168,13 @@ def layer_test(layer_cls,
   kwargs = kwargs or {}
   layer = layer_cls(**kwargs)
 
+  if (supports_masking is not None
+      and layer.supports_masking != supports_masking):
+    raise AssertionError(
+        'When testing layer %s, the `supports_masking` property is %r'
+        'but expected to be %r.\nFull kwargs: %s' %
+        (layer_cls.__name__, layer.supports_masking, supports_masking, kwargs))
+
   # Test adapt, if data was passed.
   if adapt_data is not None:
     layer.adapt(adapt_data)