Fix sparse_softmax_cross_entropy_with_logits for empty tensor

If the batch size is zero, we need to avoid calling into Eigen because Eigen
will explode.  Zero classes is an error.
Change: 126359444

1 files changed, 10 insertions, 0 deletions

diff --git a/tensorflow/python/kernel_tests/sparse_xent_op_test.py b/tensorflow/python/kernel_tests/sparse_xent_op_test.py
index eb6bdff8b5..ea379fbac0 100644
--- a/tensorflow/python/kernel_tests/sparse_xent_op_test.py
+++ b/tensorflow/python/kernel_tests/sparse_xent_op_test.py
@@ -120,6 +120,13 @@ class SparseXentTest(tf.test.TestCase):
         tf.nn.sparse_softmax_cross_entropy_with_logits(
             tf.constant(1.0), tf.constant(0))
 
+  def testLabelsPlaceholderScalar(self):
+    with self.test_session():
+      labels = tf.placeholder(np.int32)
+      y = tf.nn.sparse_softmax_cross_entropy_with_logits([[7.]], labels)
+      with self.assertRaisesOpError("labels must be 1-D"):
+        y.eval(feed_dict={labels: 0})
+
   def testVector(self):
     with self.test_session():
       loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
@@ -145,6 +152,9 @@ class SparseXentTest(tf.test.TestCase):
           np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float16),
           np.array([3, 0]).astype(label_dtype))
 
+  def testEmpty(self):
+    self._testXent(np.zeros((0, 3)), np.zeros((0,), dtype=np.int32))
+
   def testGradient(self):
     with self.test_session():
       l = tf.constant([3, 0, 1], name="l")