Updating cosine similarity loss.

- Changed CosineProximity to CosineSimilarity in v2
- Added `tf.keras.losses.cosine_similarity` to the namespaces. Keeping the existing v1 namespaces.
- Removed the negate sign from cosine similarity.

PiperOrigin-RevId: 234885212

tensorflow/contrib/keras/api/keras/losses/__init__.py

@@ -22,7 +22,7 @@ from __future__ import print_function
 from tensorflow.python.keras.losses import binary_crossentropy
 from tensorflow.python.keras.losses import categorical_crossentropy
 from tensorflow.python.keras.losses import categorical_hinge
-from tensorflow.python.keras.losses import cosine_proximity
+from tensorflow.python.keras.losses import cosine_similarity
 from tensorflow.python.keras.losses import hinge
 from tensorflow.python.keras.losses import kullback_leibler_divergence
 from tensorflow.python.keras.losses import logcosh

tensorflow/contrib/keras/api/keras/metrics/__init__.py

@@ -23,7 +23,7 @@ from tensorflow.python.keras.metrics import binary_accuracy
 from tensorflow.python.keras.metrics import binary_crossentropy
 from tensorflow.python.keras.metrics import categorical_accuracy
 from tensorflow.python.keras.metrics import categorical_crossentropy
-from tensorflow.python.keras.metrics import cosine_proximity
+from tensorflow.python.keras.metrics import cosine_similarity
 from tensorflow.python.keras.metrics import hinge
 from tensorflow.python.keras.metrics import kullback_leibler_divergence
 from tensorflow.python.keras.metrics import mean_absolute_error

tensorflow/python/keras/losses.py

@@ -943,38 +943,53 @@ def poisson(y_true, y_pred):
   return K.mean(y_pred - y_true * math_ops.log(y_pred + K.epsilon()), axis=-1)
 
 
-@keras_export('keras.metrics.cosine_proximity',
-              'keras.metrics.cosine',
-              'keras.losses.cosine_proximity',
-              'keras.losses.cosine')
+# Retaining the legacy namespaces: 'cosine_proximity' and 'cosine'.
+# TODO(psv): Change name of this function to `cosine_similarity` after fixing
+# estimator test.
+@keras_export(
+    'keras.losses.cosine_similarity',
+    v1=[
+        'keras.metrics.cosine_proximity',
+        'keras.metrics.cosine',
+        'keras.losses.cosine_proximity',
+        'keras.losses.cosine',
+        'keras.losses.cosine_similarity',
+    ])
 def cosine_proximity(y_true, y_pred, axis=-1):
+  """Computes the cosine similarity between labels and predictions."""
   y_true = nn.l2_normalize(y_true, axis=axis)
   y_pred = nn.l2_normalize(y_pred, axis=axis)
-  return -math_ops.reduce_sum(y_true * y_pred, axis=axis)
+  return math_ops.reduce_sum(y_true * y_pred, axis=axis)
 
 
-@keras_export('keras.losses.CosineProximity')
-class CosineProximity(Loss):
-  """Computes the cosine proximity between `y_true` and `y_pred`.
+@keras_export('keras.losses.CosineSimilarity')
+class CosineSimilarity(LossFunctionWrapper):
+  """Computes the cosine similarity between `y_true` and `y_pred`.
 
   Usage:
 
   ```python
-  cosine_loss = tf.keras.losses.CosineProximity()
-  loss = cosine_loss([0., 1., 1.], [1., 0., 1.])
-  print('Loss: ', loss.numpy())  # Loss: -0.5
+  cosine_loss = tf.keras.losses.CosineSimilarity(axis=1)
+  loss = cosine_loss([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]])
+  # l2_norm(y_true) = [[0., 1.], [1./1.414], 1./1.414]]]
+  # l2_norm(y_pred) = [[1., 0.], [1./1.414], 1./1.414]]]
+  # l2_norm(y_true) . l2_norm(y_pred) = [[0., 0.], [0.5, 0.5]]
+  # loss = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))
+         = ((0. + 0.) +  (0.5 + 0.5)) / 2
+
+  print('Loss: ', loss.numpy())  # Loss: 0.5
   ```
 
   Usage with tf.keras API:
 
   ```python
   model = tf.keras.Model(inputs, outputs)
-  model.compile('sgd', loss=tf.keras.losses.CosineProximity())
+  model.compile('sgd', loss=tf.keras.losses.CosineSimilarity(axis=1))
   ```
 
   Args:
     axis: (Optional) Defaults to -1. The dimension along which the cosine
-      proximity is computed.
+      similarity is computed.
     reduction: (Optional) Type of `tf.keras.losses.Reduction` to apply to loss.
       Default value is `SUM_OVER_BATCH_SIZE`.
     name: Optional name for the op.
@@ -983,23 +998,9 @@ class CosineProximity(Loss):
   def __init__(self,
                axis=-1,
                reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
-               name=None):
-    super(CosineProximity, self).__init__(reduction=reduction, name=name)
-    self.axis = axis
-
-  def call(self, y_true, y_pred):
-    """Calculates the cosine proximity loss.
-
-    Args:
-      y_true: Ground truth values.
-      y_pred: The predicted values.
-
-    Returns:
-      Cosine distance loss.
-    """
-    y_pred = ops.convert_to_tensor(y_pred)
-    y_true = math_ops.cast(y_true, y_pred.dtype)
-    return cosine_proximity(y_true, y_pred, axis=self.axis)
+               name='cosine_similarity'):
+    super(CosineSimilarity, self).__init__(
+        cosine_similarity, reduction=reduction, name=name, axis=axis)
 
 
 # Aliases.
@@ -1009,7 +1010,7 @@ mae = MAE = mean_absolute_error
 mape = MAPE = mean_absolute_percentage_error
 msle = MSLE = mean_squared_logarithmic_error
 kld = KLD = kullback_leibler_divergence
-cosine = cosine_proximity
+cosine_similarity = cosine_proximity
 
 
 def is_categorical_crossentropy(loss):

tensorflow/python/keras/losses_test.py

@@ -45,7 +45,7 @@ ALL_LOSSES = [keras.losses.mean_squared_error,
               keras.losses.binary_crossentropy,
               keras.losses.kullback_leibler_divergence,
               keras.losses.poisson,
-              keras.losses.cosine_proximity,
+              keras.losses.cosine_similarity,
               keras.losses.logcosh,
               keras.losses.categorical_hinge]
 
@@ -501,7 +501,7 @@ class MeanSquaredLogarithmicErrorTest(test.TestCase):
 
 
 @test_util.run_all_in_graph_and_eager_modes
-class CosineProximityTest(test.TestCase):
+class CosineSimilarityTest(test.TestCase):
 
   def l2_norm(self, x, axis):
     epsilon = 1e-12
@@ -515,28 +515,27 @@ class CosineProximityTest(test.TestCase):
 
     y_true = self.l2_norm(self.np_y_true, axis)
     y_pred = self.l2_norm(self.np_y_pred, axis)
-    self.expected_loss = -np.sum(np.multiply(y_true, y_pred), axis=(axis,))
+    self.expected_loss = np.sum(np.multiply(y_true, y_pred), axis=(axis,))
 
     self.y_true = constant_op.constant(self.np_y_true)
     self.y_pred = constant_op.constant(self.np_y_pred)
 
   def test_config(self):
-    cosine_obj = keras.losses.CosineProximity(
+    cosine_obj = keras.losses.CosineSimilarity(
         axis=2, reduction=losses_utils.ReductionV2.SUM, name='cosine_loss')
     self.assertEqual(cosine_obj.name, 'cosine_loss')
     self.assertEqual(cosine_obj.reduction, losses_utils.ReductionV2.SUM)
-    self.assertEqual(cosine_obj.axis, 2)
 
   def test_unweighted(self):
     self.setup()
-    cosine_obj = keras.losses.CosineProximity()
+    cosine_obj = keras.losses.CosineSimilarity()
     loss = cosine_obj(self.y_true, self.y_pred)
     expected_loss = np.mean(self.expected_loss)
     self.assertAlmostEqual(self.evaluate(loss), expected_loss, 3)
 
   def test_scalar_weighted(self):
     self.setup()
-    cosine_obj = keras.losses.CosineProximity()
+    cosine_obj = keras.losses.CosineSimilarity()
     sample_weight = 2.3
     loss = cosine_obj(self.y_true, self.y_pred, sample_weight=sample_weight)
     expected_loss = np.mean(self.expected_loss * sample_weight)
@@ -544,7 +543,7 @@ class CosineProximityTest(test.TestCase):
 
   def test_sample_weighted(self):
     self.setup()
-    cosine_obj = keras.losses.CosineProximity()
+    cosine_obj = keras.losses.CosineSimilarity()
     sample_weight = np.asarray([1.2, 3.4])
     loss = cosine_obj(
         self.y_true,
@@ -555,14 +554,14 @@ class CosineProximityTest(test.TestCase):
 
   def test_timestep_weighted(self):
     self.setup()
-    cosine_obj = keras.losses.CosineProximity()
+    cosine_obj = keras.losses.CosineSimilarity()
     np_y_true = self.np_y_true.reshape((2, 3, 1))
     np_y_pred = self.np_y_pred.reshape((2, 3, 1))
     sample_weight = np.asarray([3, 6, 5, 0, 4, 2]).reshape((2, 3))
 
     y_true = self.l2_norm(np_y_true, 2)
     y_pred = self.l2_norm(np_y_pred, 2)
-    expected_loss = -np.sum(np.multiply(y_true, y_pred), axis=(2,))
+    expected_loss = np.sum(np.multiply(y_true, y_pred), axis=(2,))
 
     y_true = constant_op.constant(np_y_true)
     y_pred = constant_op.constant(np_y_pred)
@@ -574,13 +573,13 @@ class CosineProximityTest(test.TestCase):
 
   def test_zero_weighted(self):
     self.setup()
-    cosine_obj = keras.losses.CosineProximity()
+    cosine_obj = keras.losses.CosineSimilarity()
     loss = cosine_obj(self.y_true, self.y_pred, sample_weight=0)
     self.assertAlmostEqual(self.evaluate(loss), 0., 3)
 
   def test_axis(self):
     self.setup(axis=1)
-    cosine_obj = keras.losses.CosineProximity(axis=1)
+    cosine_obj = keras.losses.CosineSimilarity(axis=1)
     loss = cosine_obj(self.y_true, self.y_pred)
     expected_loss = np.mean(self.expected_loss)
     self.assertAlmostEqual(self.evaluate(loss), expected_loss, 3)

tensorflow/python/keras/metrics.py

@@ -34,7 +34,7 @@ from tensorflow.python.keras.engine.base_layer import Layer
 from tensorflow.python.keras.losses import binary_crossentropy
 from tensorflow.python.keras.losses import categorical_crossentropy
 from tensorflow.python.keras.losses import categorical_hinge
-from tensorflow.python.keras.losses import cosine_proximity
+from tensorflow.python.keras.losses import cosine_similarity
 from tensorflow.python.keras.losses import hinge
 from tensorflow.python.keras.losses import kullback_leibler_divergence
 from tensorflow.python.keras.losses import logcosh
@@ -1818,21 +1818,30 @@ class AUC(Metric):
     return dict(list(base_config.items()) + list(config.items()))
 
 
-@keras_export('keras.metrics.CosineProximity')
-class CosineProximity(MeanMetricWrapper):
-  """Computes the cosine distance between the labels and predictions.
+@keras_export('keras.metrics.CosineSimilarity')
+class CosineSimilarity(MeanMetricWrapper):
+  """Computes the cosine similarity between the labels and predictions.
+
+  cosine similarity = (a . b) / ||a|| ||b||
+  (https://en.wikipedia.org/wiki/Cosine_similarity)
 
   For example, if `y_true` is [0, 1, 1], and `y_pred` is [1, 0, 1], the cosine
-  proximity is -0.5.
+  similarity is 0.5.
 
-  This metric keeps the average cosine distance between `predictions` and
+  This metric keeps the average cosine similarity between `predictions` and
   `labels` over a stream of data.
 
   Usage:
   ```python
-  m = tf.keras.metrics.CosineProximity()
-  m.update_state([0, 1, 1], [1, 0, 1])
-  print('Final result: ', m.result().numpy())  # Final result: -0.5
+  m = tf.keras.metrics.CosineSimilarity(axis=1)
+  m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]])
+  # l2_norm(y_true) = [[0., 1.], [1./1.414], 1./1.414]]]
+  # l2_norm(y_pred) = [[1., 0.], [1./1.414], 1./1.414]]]
+  # l2_norm(y_true) . l2_norm(y_pred) = [[0., 0.], [0.5, 0.5]]
+  # result = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))
+         = ((0. + 0.) +  (0.5 + 0.5)) / 2
+
+  print('Final result: ', m.result().numpy())  # Final result: 0.5
   ```
 
   Usage with tf.keras API:
@@ -1842,20 +1851,21 @@ class CosineProximity(MeanMetricWrapper):
   model.compile(
       'sgd',
       loss='mse',
-      metrics=[tf.keras.metrics.CosineProximity()])
+      metrics=[tf.keras.metrics.CosineSimilarity(axis=1)])
   ```
   """
 
-  def __init__(self, name='cosine_proximity', dtype=None, axis=-1):
-    """Creates a `CosineProximity` instance.
+  def __init__(self, name='cosine_similarity', dtype=None, axis=-1):
+    """Creates a `CosineSimilarity` instance.
 
     Args:
       name: (Optional) string name of the metric instance.
       dtype: (Optional) data type of the metric result.
       axis: (Optional) Defaults to -1. The dimension along which the cosine
-        proximity is computed.
+        similarity is computed.
     """
-    super(CosineProximity, self).__init__(cosine, name, dtype=dtype, axis=axis)
+    super(CosineSimilarity, self).__init__(
+        cosine_similarity, name, dtype=dtype, axis=axis)
 
 
 @keras_export('keras.metrics.MeanAbsoluteError')
@@ -2744,7 +2754,7 @@ mse = MSE = mean_squared_error
 mae = MAE = mean_absolute_error
 mape = MAPE = mean_absolute_percentage_error
 msle = MSLE = mean_squared_logarithmic_error
-cosine = cosine_proximity
+cosine_proximity = cosine_similarity
 
 
 def clone_metric(metric):

tensorflow/python/keras/metrics_test.py

@@ -453,7 +453,7 @@ class KerasAccuracyTest(test.TestCase):
 
 
 @test_util.run_all_in_graph_and_eager_modes
-class CosineProximityTest(test.TestCase):
+class CosineSimilarityTest(test.TestCase):
 
   def l2_norm(self, x, axis):
     epsilon = 1e-12
@@ -467,25 +467,25 @@ class CosineProximityTest(test.TestCase):
 
     y_true = self.l2_norm(self.np_y_true, axis)
     y_pred = self.l2_norm(self.np_y_pred, axis)
-    self.expected_loss = -np.sum(np.multiply(y_true, y_pred), axis=(axis,))
+    self.expected_loss = np.sum(np.multiply(y_true, y_pred), axis=(axis,))
 
     self.y_true = constant_op.constant(self.np_y_true)
     self.y_pred = constant_op.constant(self.np_y_pred)
 
   def test_config(self):
-    cosine_obj = metrics.CosineProximity(
+    cosine_obj = metrics.CosineSimilarity(
         axis=2, name='my_cos', dtype=dtypes.int32)
     self.assertEqual(cosine_obj.name, 'my_cos')
     self.assertEqual(cosine_obj._dtype, dtypes.int32)
 
     # Check save and restore config
-    cosine_obj2 = metrics.CosineProximity.from_config(cosine_obj.get_config())
+    cosine_obj2 = metrics.CosineSimilarity.from_config(cosine_obj.get_config())
     self.assertEqual(cosine_obj2.name, 'my_cos')
     self.assertEqual(cosine_obj2._dtype, dtypes.int32)
 
   def test_unweighted(self):
     self.setup()
-    cosine_obj = metrics.CosineProximity()
+    cosine_obj = metrics.CosineSimilarity()
     self.evaluate(variables.variables_initializer(cosine_obj.variables))
     loss = cosine_obj(self.y_true, self.y_pred)
     expected_loss = np.mean(self.expected_loss)
@@ -493,7 +493,7 @@ class CosineProximityTest(test.TestCase):
 
   def test_weighted(self):
     self.setup()
-    cosine_obj = metrics.CosineProximity()
+    cosine_obj = metrics.CosineSimilarity()
     self.evaluate(variables.variables_initializer(cosine_obj.variables))
     sample_weight = np.asarray([1.2, 3.4])
     loss = cosine_obj(
@@ -506,7 +506,7 @@ class CosineProximityTest(test.TestCase):
 
   def test_axis(self):
     self.setup(axis=1)
-    cosine_obj = metrics.CosineProximity(axis=1)
+    cosine_obj = metrics.CosineSimilarity(axis=1)
     self.evaluate(variables.variables_initializer(cosine_obj.variables))
     loss = cosine_obj(self.y_true, self.y_pred)
     expected_loss = np.mean(self.expected_loss)

tensorflow/tools/api/golden/v1/tensorflow.keras.losses.-cosine-similarity.pbtxt

@@ -1,11 +1,12 @@
-path: "tensorflow.losses.CosineProximity"
+path: "tensorflow.keras.losses.CosineSimilarity"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.losses.CosineProximity\'>"
+  is_instance: "<class \'tensorflow.python.keras.losses.CosineSimilarity\'>"
+  is_instance: "<class \'tensorflow.python.keras.losses.LossFunctionWrapper\'>"
   is_instance: "<class \'tensorflow.python.keras.losses.Loss\'>"
   is_instance: "<type \'object\'>"
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'axis\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'sum_over_batch_size\', \'None\'], "
+    argspec: "args=[\'self\', \'axis\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'sum_over_batch_size\', \'cosine_similarity\'], "
   }
   member_method {
     name: "call"

tensorflow/tools/api/golden/v1/tensorflow.keras.losses.pbtxt

@@ -13,7 +13,7 @@ tf_module {
     mtype: "<type \'type\'>"
   }
   member {
-    name: "CosineProximity"
+    name: "CosineSimilarity"
     mtype: "<type \'type\'>"
   }
   member {
@@ -108,6 +108,10 @@ tf_module {
     name: "cosine_proximity"
     argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'], "
   }
+  member_method {
+    name: "cosine_similarity"
+    argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'], "
+  }
   member_method {
     name: "deserialize"
     argspec: "args=[\'name\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.-cosine-similarity.pbtxt

@@ -1,6 +1,6 @@
-path: "tensorflow.keras.metrics.CosineProximity"
+path: "tensorflow.keras.metrics.CosineSimilarity"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.metrics.CosineProximity\'>"
+  is_instance: "<class \'tensorflow.python.keras.metrics.CosineSimilarity\'>"
   is_instance: "<class \'tensorflow.python.keras.metrics.MeanMetricWrapper\'>"
   is_instance: "<class \'tensorflow.python.keras.metrics.Mean\'>"
   is_instance: "<class \'tensorflow.python.keras.metrics.Reduce\'>"
@@ -90,7 +90,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'name\', \'dtype\', \'axis\'], varargs=None, keywords=None, defaults=[\'cosine_proximity\', \'None\', \'-1\'], "
+    argspec: "args=[\'self\', \'name\', \'dtype\', \'axis\'], varargs=None, keywords=None, defaults=[\'cosine_similarity\', \'None\', \'-1\'], "
   }
   member_method {
     name: "add_loss"

tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.pbtxt

@@ -29,7 +29,7 @@ tf_module {
     mtype: "<type \'type\'>"
   }
   member {
-    name: "CosineProximity"
+    name: "CosineSimilarity"
     mtype: "<type \'type\'>"
   }
   member {

tensorflow/tools/api/golden/v2/tensorflow.keras.losses.-cosine-similarity.pbtxt

@@ -1,11 +1,12 @@
-path: "tensorflow.keras.losses.CosineProximity"
+path: "tensorflow.keras.losses.CosineSimilarity"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.losses.CosineProximity\'>"
+  is_instance: "<class \'tensorflow.python.keras.losses.CosineSimilarity\'>"
+  is_instance: "<class \'tensorflow.python.keras.losses.LossFunctionWrapper\'>"
   is_instance: "<class \'tensorflow.python.keras.losses.Loss\'>"
   is_instance: "<type \'object\'>"
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'axis\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'sum_over_batch_size\', \'None\'], "
+    argspec: "args=[\'self\', \'axis\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'sum_over_batch_size\', \'cosine_similarity\'], "
   }
   member_method {
     name: "call"

tensorflow/tools/api/golden/v2/tensorflow.keras.losses.pbtxt

@@ -13,7 +13,7 @@ tf_module {
     mtype: "<type \'type\'>"
   }
   member {
-    name: "CosineProximity"
+    name: "CosineSimilarity"
     mtype: "<type \'type\'>"
   }
   member {
@@ -105,11 +105,7 @@ tf_module {
     argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "cosine"
-    argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'], "
-  }
-  member_method {
-    name: "cosine_proximity"
+    name: "cosine_similarity"
     argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'], "
   }
   member_method {

tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.-cosine-similarity.pbtxt

@@ -1,6 +1,6 @@
-path: "tensorflow.keras.metrics.CosineProximity"
+path: "tensorflow.keras.metrics.CosineSimilarity"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.metrics.CosineProximity\'>"
+  is_instance: "<class \'tensorflow.python.keras.metrics.CosineSimilarity\'>"
   is_instance: "<class \'tensorflow.python.keras.metrics.MeanMetricWrapper\'>"
   is_instance: "<class \'tensorflow.python.keras.metrics.Mean\'>"
   is_instance: "<class \'tensorflow.python.keras.metrics.Reduce\'>"
@@ -90,7 +90,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'name\', \'dtype\', \'axis\'], varargs=None, keywords=None, defaults=[\'cosine_proximity\', \'None\', \'-1\'], "
+    argspec: "args=[\'self\', \'name\', \'dtype\', \'axis\'], varargs=None, keywords=None, defaults=[\'cosine_similarity\', \'None\', \'-1\'], "
   }
   member_method {
     name: "add_loss"

tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.pbtxt

@@ -29,7 +29,7 @@ tf_module {
     mtype: "<type \'type\'>"
   }
   member {
-    name: "CosineProximity"
+    name: "CosineSimilarity"
     mtype: "<type \'type\'>"
   }
   member {
@@ -180,14 +180,6 @@ tf_module {
     name: "categorical_crossentropy"
     argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywords=None, defaults=[\'False\', \'0\'], "
   }
-  member_method {
-    name: "cosine"
-    argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'], "
-  }
-  member_method {
-    name: "cosine_proximity"
-    argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'], "
-  }
   member_method {
     name: "deserialize"
     argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.losses.-cosine-similarity.pbtxt

@@ -1,11 +1,12 @@
-path: "tensorflow.keras.losses.CosineProximity"
+path: "tensorflow.losses.CosineSimilarity"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.losses.CosineProximity\'>"
+  is_instance: "<class \'tensorflow.python.keras.losses.CosineSimilarity\'>"
+  is_instance: "<class \'tensorflow.python.keras.losses.LossFunctionWrapper\'>"
   is_instance: "<class \'tensorflow.python.keras.losses.Loss\'>"
   is_instance: "<type \'object\'>"
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'axis\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'sum_over_batch_size\', \'None\'], "
+    argspec: "args=[\'self\', \'axis\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'sum_over_batch_size\', \'cosine_similarity\'], "
   }
   member_method {
     name: "call"

tensorflow/tools/api/golden/v2/tensorflow.losses.pbtxt

@@ -13,7 +13,7 @@ tf_module {
     mtype: "<type \'type\'>"
   }
   member {
-    name: "CosineProximity"
+    name: "CosineSimilarity"
     mtype: "<type \'type\'>"
   }
   member {
@@ -105,11 +105,7 @@ tf_module {
     argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "cosine"
-    argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'], "
-  }
-  member_method {
-    name: "cosine_proximity"
+    name: "cosine_similarity"
     argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'], "
   }
   member_method {

tensorflow/tools/api/golden/v2/tensorflow.metrics.-cosine-similarity.pbtxt

@@ -1,6 +1,6 @@
-path: "tensorflow.metrics.CosineProximity"
+path: "tensorflow.metrics.CosineSimilarity"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.metrics.CosineProximity\'>"
+  is_instance: "<class \'tensorflow.python.keras.metrics.CosineSimilarity\'>"
   is_instance: "<class \'tensorflow.python.keras.metrics.MeanMetricWrapper\'>"
   is_instance: "<class \'tensorflow.python.keras.metrics.Mean\'>"
   is_instance: "<class \'tensorflow.python.keras.metrics.Reduce\'>"
@@ -90,7 +90,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'name\', \'dtype\', \'axis\'], varargs=None, keywords=None, defaults=[\'cosine_proximity\', \'None\', \'-1\'], "
+    argspec: "args=[\'self\', \'name\', \'dtype\', \'axis\'], varargs=None, keywords=None, defaults=[\'cosine_similarity\', \'None\', \'-1\'], "
   }
   member_method {
     name: "add_loss"

tensorflow/tools/api/golden/v2/tensorflow.metrics.pbtxt

@@ -29,7 +29,7 @@ tf_module {
     mtype: "<type \'type\'>"
   }
   member {
-    name: "CosineProximity"
+    name: "CosineSimilarity"
     mtype: "<type \'type\'>"
   }
   member {
@@ -180,14 +180,6 @@ tf_module {
     name: "categorical_crossentropy"
     argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywords=None, defaults=[\'False\', \'0\'], "
   }
-  member_method {
-    name: "cosine"
-    argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'], "
-  }
-  member_method {
-    name: "cosine_proximity"
-    argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'], "
-  }
   member_method {
     name: "deserialize"
     argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "