Change loss_ops_test to pass in data with batch_size 1.

Change: 138925660

1 files changed, 21 insertions, 8 deletions

diff --git a/tensorflow/contrib/losses/python/losses/loss_ops_test.py b/tensorflow/contrib/losses/python/losses/loss_ops_test.py
index e08752c8d2..75785f00a1 100644
--- a/tensorflow/contrib/losses/python/losses/loss_ops_test.py
+++ b/tensorflow/contrib/losses/python/losses/loss_ops_test.py
@@ -1350,30 +1350,43 @@ class ComputeWeightedLossTest(tf.test.TestCase):
 class AddLossTest(tf.test.TestCase):
 
   def testAddExternalLoss(self):
-    logits = tf.constant([1.2, 0.4, -1.0, -1.1])
-    labels = tf.constant([1.0, 0.0, 0.0, 1.0])
+    logits = tf.constant([[1.2, 0.4, -1.0, -1.1]])
+    labels = tf.constant([[1.0, 0.0, 0.0, 1.0]])
     losses = tf.contrib.losses.hinge_loss(logits, labels)
     self.assertFalse(tf.contrib.losses.get_losses())
     tf.contrib.losses.add_loss(tf.reduce_mean(losses))
     self.assertTrue(tf.contrib.losses.get_losses())
     total_loss = tf.contrib.losses.get_total_loss()
     with self.test_session():
-      self.assertAllClose(losses.eval(), [0.0, 1.4, 0.0, 2.1], atol=1e-3)
+      self.assertAllClose(losses.eval(), [[0.0, 1.4, 0.0, 2.1]], atol=1e-3)
       self.assertAllClose(total_loss.eval(), 3.5/4.0, atol=1e-3)
 
   def testNoneLossCollection(self):
-    logits = tf.constant([1.2, 0.4, -1.0, -1.1])
-    labels = tf.constant([1.0, 0.0, 0.0, 1.0])
+    logits = tf.constant([[1.2, 0.4, -1.0, -1.1]])
+    labels = tf.constant([[1.0, 0.0, 0.0, 1.0]])
     losses = tf.contrib.losses.hinge_loss(logits, labels)
     self.assertFalse(tf.contrib.losses.get_losses())
     tf.contrib.losses.add_loss(tf.reduce_mean(losses), loss_collection=None)
     self.assertFalse(tf.contrib.losses.get_losses())
     with self.test_session():
-      self.assertAllClose(losses.eval(), [0.0, 1.4, 0.0, 2.1], atol=1e-3)
+      self.assertAllClose(losses.eval(), [[0.0, 1.4, 0.0, 2.1]], atol=1e-3)
 
   def testNoCollectLosses(self):
-    logits = tf.constant([1.2, 0.4, -1.0, -1.1])
-    labels = tf.constant([1.0, 0.0, 0.0, 1.0])
+    logits = tf.constant([[1.2, 0.4, -1.0, -1.1]])
+    labels = tf.constant([[1.0, 0.0, 0.0, 1.0]])
+    self.assertFalse(tf.contrib.losses.get_losses())
+    with tf.contrib.framework.arg_scope([tf.contrib.losses.add_loss],
+                                        loss_collection=None):
+      tf.contrib.losses.absolute_difference(logits, labels)
+      tf.contrib.losses.log_loss(logits, labels)
+      tf.contrib.losses.mean_squared_error(logits, labels)
+      tf.contrib.losses.sigmoid_cross_entropy(logits, labels)
+      tf.contrib.losses.softmax_cross_entropy(logits, labels)
+    self.assertFalse(tf.contrib.losses.get_losses())
+
+  def testNoCollectLossesBatch2(self):
+    logits = tf.constant([[1.2, 0.4, -1.0, -1.1]] * 2)
+    labels = tf.constant([[1.0, 0.0, 0.0, 1.0]] * 2)
     self.assertFalse(tf.contrib.losses.get_losses())
     with tf.contrib.framework.arg_scope([tf.contrib.losses.add_loss],
                                         loss_collection=None):