Remove logloss v2 as this is a duplication of bce with probabilities.

PiperOrigin-RevId: 236696942

tensorflow/python/keras/losses.py

@@ -532,34 +532,6 @@ class CategoricalHinge(LossFunctionWrapper):
         categorical_hinge, name=name, reduction=reduction)
 
 
-@keras_export('keras.losses.LogLoss')
-class LogLoss(LossFunctionWrapper):
-  """Computes the log loss between `y_true` and `y_pred`.
-
-  `logloss = - y_true * log(y_pred) - (1 - y_true) * log(1 - y_pred)`
-
-  Usage:
-
-  ```python
-  l = tf.keras.losses.LogLoss()
-  loss = l([0., 1., 1.], [1., 0., 1.])
-  print('Loss: ', loss.numpy())  # Loss: 10.745
-  ```
-
-  Usage with tf.keras API:
-
-  ```python
-  model = tf.keras.Model(inputs, outputs)
-  model.compile('sgd', loss=tf.keras.losses.LogLoss())
-  ```
-  """
-
-  def __init__(self,
-               reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
-               name='logloss'):
-    super(LogLoss, self).__init__(logloss, name=name, reduction=reduction)
-
-
 @keras_export('keras.losses.Poisson')
 class Poisson(LossFunctionWrapper):
   """Computes the Poisson loss between `y_true` and `y_pred`.
@@ -801,15 +773,6 @@ def categorical_hinge(y_true, y_pred):
   return math_ops.maximum(0., neg - pos + 1.)
 
 
-def logloss(y_true, y_pred):
-  y_pred = ops.convert_to_tensor(y_pred)
-  y_true = math_ops.cast(y_true, y_pred.dtype)
-  losses = math_ops.multiply(y_true, math_ops.log(y_pred + K.epsilon()))
-  losses += math_ops.multiply((1 - y_true),
-                              math_ops.log(1 - y_pred + K.epsilon()))
-  return K.mean(-losses, axis=-1)
-
-
 def huber_loss(y_true, y_pred, delta=1.0):
   """Computes Huber loss value.
 

tensorflow/python/keras/losses_test.py

@@ -1193,97 +1193,6 @@ class CategoricalHingeTest(test.TestCase):
     self.assertAlmostEqual(self.evaluate(loss), 0., 3)
 
 
-@test_util.run_all_in_graph_and_eager_modes
-class LogLossTest(test.TestCase):
-
-  def setup(self):
-    # TODO(psv): Change to setUp() after b/122319309 is fixed.
-    y_pred = np.asarray([.9, .2, .2, .8, .4, .6]).reshape((2, 3))
-    y_true = np.asarray([1., 0., 1., 1., 0., 0.]).reshape((2, 3))
-    epsilon = 1e-7  # to avoid log 0
-
-    self.batch_size = 6
-    self.expected_losses = np.multiply(y_true, np.log(y_pred + epsilon))
-    self.expected_losses += np.multiply(1 - y_true,
-                                        np.log(1 - y_pred + epsilon))
-    self.expected_losses = -self.expected_losses
-
-    self.y_pred = constant_op.constant(y_pred)
-    self.y_true = constant_op.constant(y_true)
-
-  def test_config(self):
-    log_loss_obj = keras.losses.LogLoss(
-        reduction=losses_utils.ReductionV2.SUM, name='log')
-    self.assertEqual(log_loss_obj.name, 'log')
-    self.assertEqual(log_loss_obj.reduction, losses_utils.ReductionV2.SUM)
-
-  def test_all_correct(self):
-    self.setup()
-    log_loss_obj = keras.losses.LogLoss()
-    loss = log_loss_obj(self.y_true, self.y_true)
-    self.assertAlmostEqual(self.evaluate(loss), 0.0, 3)
-
-  def test_unweighted(self):
-    self.setup()
-    log_loss_obj = keras.losses.LogLoss()
-    loss = log_loss_obj(self.y_true, self.y_pred)
-    actual_loss = np.sum(self.expected_losses) / self.batch_size
-    self.assertAlmostEqual(self.evaluate(loss), actual_loss, 3)
-
-  def test_scalar_weighted(self):
-    self.setup()
-    log_loss_obj = keras.losses.LogLoss()
-    sample_weight = 2.3
-    loss = log_loss_obj(self.y_true, self.y_pred, sample_weight=sample_weight)
-    actual_loss = sample_weight * np.sum(self.expected_losses) / self.batch_size
-    self.assertAlmostEqual(self.evaluate(loss), actual_loss, 3)
-
-    # Verify we get the same output when the same input is given
-    loss_2 = log_loss_obj(self.y_true, self.y_pred, sample_weight=sample_weight)
-    self.assertAlmostEqual(self.evaluate(loss), self.evaluate(loss_2), 3)
-
-  def test_sample_weighted(self):
-    self.setup()
-    log_loss_obj = keras.losses.LogLoss()
-    sample_weight = constant_op.constant((1.2, 3.4), shape=(2, 1))
-
-    loss = log_loss_obj(self.y_true, self.y_pred, sample_weight=sample_weight)
-    actual_loss = np.multiply(
-        self.expected_losses,
-        np.asarray([1.2, 1.2, 1.2, 3.4, 3.4, 3.4]).reshape((2, 3)))
-    actual_loss = np.sum(actual_loss) / self.batch_size
-    self.assertAlmostEqual(self.evaluate(loss), actual_loss, 3)
-
-  def test_timestep_weighted(self):
-    log_loss_obj = keras.losses.LogLoss()
-
-    y_pred = np.asarray([.9, .2, .2, .8, .4, .6]).reshape((2, 3, 1))
-    y_true = np.asarray([1., 0., 1., 1., 0., 0.]).reshape((2, 3, 1))
-    epsilon = 1e-7  # to avoid log 0
-    batch_size = 6
-
-    expected_losses = np.multiply(y_true, np.log(y_pred + epsilon))
-    expected_losses += np.multiply(1 - y_true, np.log(1 - y_pred + epsilon))
-
-    y_pred = constant_op.constant(y_pred)
-    y_true = constant_op.constant(y_true)
-    sample_weight = np.array([3, 6, 5, 0, 4, 2]).reshape((2, 3, 1))
-    loss = log_loss_obj(
-        y_true,
-        y_pred,
-        sample_weight=constant_op.constant(sample_weight, shape=(2, 3)))
-    actual_loss = np.multiply(-expected_losses, sample_weight)
-    actual_loss = np.sum(actual_loss) / batch_size
-    self.assertAlmostEqual(self.evaluate(loss), actual_loss, 3)
-
-  def test_zero_weighted(self):
-    self.setup()
-    log_loss_obj = keras.losses.LogLoss()
-    sample_weight = 0
-    loss = log_loss_obj(self.y_true, self.y_pred, sample_weight=sample_weight)
-    self.assertAlmostEqual(self.evaluate(loss), 0., 3)
-
-
 @test_util.run_all_in_graph_and_eager_modes
 class LogCoshTest(test.TestCase):
 

tensorflow/tools/api/golden/v1/tensorflow.keras.losses.-log-loss.pbtxt

@@ -1,23 +0,0 @@
-path: "tensorflow.keras.losses.LogLoss"
-tf_class {
-  is_instance: "<class \'tensorflow.python.keras.losses.LogLoss\'>"
-  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\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'sum_over_batch_size\', \'logloss\'], "
-  }
-  member_method {
-    name: "call"
-    argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "from_config"
-    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "get_config"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
-}

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

@@ -32,10 +32,6 @@ tf_module {
     name: "LogCosh"
     mtype: "<type \'type\'>"
   }
-  member {
-    name: "LogLoss"
-    mtype: "<type \'type\'>"
-  }
   member {
     name: "Loss"
     mtype: "<type \'type\'>"

tensorflow/tools/api/golden/v2/tensorflow.keras.losses.-log-loss.pbtxt

@@ -1,23 +0,0 @@
-path: "tensorflow.keras.losses.LogLoss"
-tf_class {
-  is_instance: "<class \'tensorflow.python.keras.losses.LogLoss\'>"
-  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\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'sum_over_batch_size\', \'logloss\'], "
-  }
-  member_method {
-    name: "call"
-    argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "from_config"
-    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "get_config"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
-}

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

@@ -32,10 +32,6 @@ tf_module {
     name: "LogCosh"
     mtype: "<type \'type\'>"
   }
-  member {
-    name: "LogLoss"
-    mtype: "<type \'type\'>"
-  }
   member {
     name: "Loss"
     mtype: "<type \'type\'>"

tensorflow/tools/api/golden/v2/tensorflow.losses.-log-loss.pbtxt

@@ -1,23 +0,0 @@
-path: "tensorflow.losses.LogLoss"
-tf_class {
-  is_instance: "<class \'tensorflow.python.keras.losses.LogLoss\'>"
-  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\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'sum_over_batch_size\', \'logloss\'], "
-  }
-  member_method {
-    name: "call"
-    argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "from_config"
-    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "get_config"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
-}

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

@@ -32,10 +32,6 @@ tf_module {
     name: "LogCosh"
     mtype: "<type \'type\'>"
   }
-  member {
-    name: "LogLoss"
-    mtype: "<type \'type\'>"
-  }
   member {
     name: "Loss"
     mtype: "<type \'type\'>"