Move binary_hinge_loss under contrib/losses/

Change: 128892982

tensorflow/contrib/layers/python/layers/target_column.py

@@ -22,6 +22,7 @@ import inspect
 
 import six
 
+from tensorflow.contrib import losses
 from tensorflow.contrib import metrics as metrics_lib
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -29,7 +30,6 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import logging_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
-from tensorflow.python.ops import nn_ops
 
 
 def regression_target(label_name=None,
@@ -297,8 +297,17 @@ class _BinarySvmTargetColumn(_MultiClassTargetColumn):
   """_TargetColumn for binary classification using SVMs."""
 
   def __init__(self, label_name, weight_column_name):
+    def loss_fn(logits, target):
+      check_shape_op = logging_ops.Assert(
+          math_ops.less_equal(array_ops.rank(target), 2),
+          ["target's shape should be either [batch_size, 1] or [batch_size]"])
+      with ops.control_dependencies([check_shape_op]):
+        target = array_ops.reshape(
+            target, shape=[array_ops.shape(target)[0], 1])
+      return losses.hinge_loss(logits, target)
+
     super(_BinarySvmTargetColumn, self).__init__(
-        loss_fn=_binary_hinge_loss,
+        loss_fn=loss_fn,
         n_classes=2,
         label_name=label_name,
         weight_column_name=weight_column_name)
@@ -331,22 +340,6 @@ def _log_loss_with_two_classes(logits, target):
   return loss_vec
 
 
-# TODO(sibyl-vie3Poto): Move this to contrib/losses/python/losses/loss_ops.py.
-def _binary_hinge_loss(logits, target):
-  """Method that returns the loss vector for binary hinge loss."""
-  check_shape_op = logging_ops.Assert(
-      math_ops.less_equal(
-          array_ops.rank(target), 2),
-      ["target's shape should be either [batch_size, 1] or [batch_size]"])
-  with ops.control_dependencies([check_shape_op]):
-    target = array_ops.reshape(target, shape=[array_ops.shape(target)[0], 1])
-  # First need to convert binary labels to -1/1 labels (as floats).
-  all_ones = array_ops.ones_like(logits)
-  labels = math_ops.sub(2 * math_ops.to_float(target), all_ones)
-  loss_vec = nn_ops.relu(math_ops.sub(all_ones, math_ops.mul(labels, logits)))
-  return loss_vec
-
-
 def _softmax_cross_entropy_loss(logits, target):
   # sigmoid_cross_entropy_with_logits requires [batch_size, 1] target.
   # Check that we got int32/int64 for classification.

tensorflow/contrib/losses/python/losses/__init__.py

@@ -106,6 +106,7 @@ weighted average over the individual prediction errors:
 
 @@absolute_difference
 @@add_loss
+@@hinge_loss
 @@cosine_distance
 @@get_losses
 @@get_regularization_losses

tensorflow/contrib/losses/python/losses/loss_ops.py

@@ -25,6 +25,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
+from tensorflow.python.ops import nn_ops
 
 
 __all__ = ["absolute_difference",
@@ -33,6 +34,7 @@ __all__ = ["absolute_difference",
            "get_losses",
            "get_regularization_losses",
            "get_total_loss",
+           "hinge_loss",
            "log_loss",
            "sigmoid_cross_entropy",
            "softmax_cross_entropy",
@@ -410,6 +412,31 @@ def log_loss(predictions, targets, weight=1.0, epsilon=1e-7, scope=None):
     return _compute_weighted_loss(losses, weight)
 
 
+def hinge_loss(logits, target, scope=None):
+  """Method that returns the loss tensor for hinge loss.
+
+  Args:
+    logits: The logits, a float tensor.
+    target: The ground truth output tensor. Its shape should match the shape of
+      logits. The values of the tensor are expected to be 0.0 or 1.0.
+    scope: The scope for the operations performed in computing the loss.
+
+  Returns:
+    A `Tensor` of same shape as logits and target representing the loss values
+      across the batch.
+
+  Raises:
+    ValueError: If the shapes of `logits` and `target` don't match.
+  """
+  with ops.op_scope([logits, target], scope, "hinge_loss") as scope:
+    logits.get_shape().assert_is_compatible_with(target.get_shape())
+    # We first need to convert binary labels to -1/1 labels (as floats).
+    target = math_ops.to_float(target)
+    all_ones = array_ops.ones_like(target)
+    labels = math_ops.sub(2 * target, all_ones)
+    return nn_ops.relu(math_ops.sub(all_ones, math_ops.mul(labels, logits)))
+
+
 def sum_of_squares(predictions, targets, weight=1.0, scope=None):
   """Adds a Sum-of-Squares loss to the training procedure.
 

tensorflow/contrib/losses/python/losses/loss_ops_test.py

@@ -499,6 +499,42 @@ class LogLossTest(tf.test.TestCase):
       self.assertAlmostEqual(0.0, loss.eval(), 3)
 
 
+class HingeLossTest(tf.test.TestCase):
+
+  def testIncompatibleShapes(self):
+    with self.test_session():
+      logits = tf.constant([[-1.0], [2.1]])
+      target = tf.constant([0.0, 1.0])
+      with self.assertRaises(ValueError):
+        _ = tf.contrib.losses.hinge_loss(logits, target).eval()
+
+  def testAllOutsideMargin(self):
+    with self.test_session():
+      logits = tf.constant([1.2, -1.4, -1.0, 2.1])
+      target = tf.constant([1.0, 0.0, 0.0, 1.0])
+      loss = tf.contrib.losses.hinge_loss(logits, target)
+      self.assertAllClose(loss.eval(), [0.0, 0.0, 0.0, 0.0], atol=1e-3)
+
+  def testSomeInsideMargin(self):
+    with self.test_session():
+      logits = tf.constant([[-0.7], [-1.4], [1.4], [0.6]])
+      target = tf.constant([[0.0], [0.0], [1.0], [1.0]])
+      loss = tf.contrib.losses.hinge_loss(logits, target)
+      # Examples 1 and 4 are on the correct side of the hyperplane but within
+      # the margin so they incur some (small) loss.
+      self.assertAllClose(loss.eval(), [[0.3], [0.0], [0.0], [0.4]], atol=1e-3)
+
+  def testSomeMisclassified(self):
+    with self.test_session():
+      logits = tf.constant([[[1.2], [0.4], [-1.0], [-1.1]]])
+      target = tf.constant([[[1.0], [0.0], [0.0], [1.0]]])
+      loss = tf.contrib.losses.hinge_loss(logits, target)
+      # Examples 2 and 4 are on the wrong side of the hyperplane so they incur
+      # some (fairly large) loss.
+      self.assertAllClose(
+          loss.eval(), [[[0.0], [1.4], [0.0], [2.1]]], atol=1e-3)
+
+
 class SumOfSquaresLossTest(tf.test.TestCase):
 
   def setUp(self):