Use 32bit floats to compute cross entropies. 16 bit floats aren't accurate

enough to deal with more than a few labels
Change: 128208074

1 files changed, 11 insertions, 10 deletions

diff --git a/tensorflow/python/kernel_tests/xent_op_test.py b/tensorflow/python/kernel_tests/xent_op_test.py
index 9e7c563547..70b3bdcbb4 100644
--- a/tensorflow/python/kernel_tests/xent_op_test.py
+++ b/tensorflow/python/kernel_tests/xent_op_test.py
@@ -21,6 +21,8 @@ from __future__ import print_function
 import numpy as np
 import tensorflow as tf
 
+from tensorflow.python.ops import gen_nn_ops
+
 
 class XentTest(tf.test.TestCase):
 
@@ -38,8 +40,8 @@ class XentTest(tf.test.TestCase):
   def _testXent(self, np_features, np_labels, use_gpu=False):
     np_loss, np_backprop = self._npXent(np_features, np_labels)
     with self.test_session(use_gpu=use_gpu) as sess:
-      loss = tf.nn.softmax_cross_entropy_with_logits(np_features, np_labels)
-      backprop = loss.op.outputs[1]
+      loss, backprop = gen_nn_ops._softmax_cross_entropy_with_logits(
+          np_features, np_labels)
       tf_loss, tf_backprop = sess.run([loss, backprop])
     self.assertAllCloseAccordingToType(np_loss, tf_loss)
     self.assertAllCloseAccordingToType(np_backprop, tf_backprop)
@@ -51,10 +53,9 @@ class XentTest(tf.test.TestCase):
   def _testSingleClass(self, use_gpu=False):
     for dtype in np.float16, np.float32:
       with self.test_session(use_gpu=use_gpu) as sess:
-        loss = tf.nn.softmax_cross_entropy_with_logits(
+        loss, backprop = gen_nn_ops._softmax_cross_entropy_with_logits(
             np.array([[1.], [-1.], [0.]]).astype(dtype),
             np.array([[-1.], [0.], [1.]]).astype(dtype))
-        backprop = loss.op.outputs[1]
         tf_loss, tf_backprop = sess.run([loss, backprop])
       self.assertAllClose([0.0, 0.0, 0.0], tf_loss)
       self.assertAllClose([[2.0], [1.0], [0.0]], tf_backprop)
@@ -69,9 +70,9 @@ class XentTest(tf.test.TestCase):
           [[[1., 1., 1., 1.]], [[1., 2., 3., 4.]]]).astype(dtype)
       np_labels = np.array(
           [[[0., 0., 0., 1.]], [[0., .5, .5, 0.]]]).astype(dtype)
-      self.assertRaisesRegexp(
-          ValueError, "must have rank 2",
-          tf.nn.softmax_cross_entropy_with_logits, np_features, np_labels)
+      self.assertRaisesRegexp(ValueError, "must have rank 2",
+                              gen_nn_ops._softmax_cross_entropy_with_logits,
+                              np_features, np_labels)
 
   def testNpXent(self):
     # We create 2 batches of logits for testing.
@@ -110,14 +111,14 @@ class XentTest(tf.test.TestCase):
   def testShapeMismatch(self):
     with self.test_session():
       with self.assertRaises(ValueError):
-        tf.nn.softmax_cross_entropy_with_logits(
+        gen_nn_ops._softmax_cross_entropy_with_logits(
             [[0., 1.], [2., 3.]], [[0., 1., 0.], [1., 0., 0.]])
 
   def testNotMatrix(self):
     with self.test_session():
       with self.assertRaises(ValueError):
-        tf.nn.softmax_cross_entropy_with_logits([0., 1., 2., 3.],
-                                                [0., 1., 0., 1.])
+        gen_nn_ops._softmax_cross_entropy_with_logits([0., 1., 2., 3.],
+                                                      [0., 1., 0., 1.])
 
   def testHalf(self):
     self._testAll(