Change arg order for {softmax,sparse_softmax,sigmoid}_cross_entropy_with_logits to be (labels, predictions), and force use of named args to avoid accidents.

Change: 143629623

2 files changed, 14 insertions, 10 deletions

diff --git a/tensorflow/python/kernel_tests/sparse_xent_op_test.py b/tensorflow/python/kernel_tests/sparse_xent_op_test.py
index ef94af54fe..d2a815a0d7 100644
--- a/tensorflow/python/kernel_tests/sparse_xent_op_test.py
+++ b/tensorflow/python/kernel_tests/sparse_xent_op_test.py
@@ -141,25 +141,26 @@ class SparseXentTest(test.TestCase):
     with self.test_session(use_gpu=True):
       with self.assertRaisesRegexp(ValueError, ".*Rank mismatch:*"):
         nn_ops.sparse_softmax_cross_entropy_with_logits(
-            [[0., 1.], [2., 3.], [2., 3.]], [[0, 2]])
+            labels=[[0, 2]], logits=[[0., 1.], [2., 3.], [2., 3.]])
 
   def testScalar(self):
     with self.test_session(use_gpu=True):
       with self.assertRaisesRegexp(ValueError, ".*Logits cannot be scalars*"):
         nn_ops.sparse_softmax_cross_entropy_with_logits(
-            constant_op.constant(1.0), constant_op.constant(0))
+            labels=constant_op.constant(0), logits=constant_op.constant(1.0))
 
   def testLabelsPlaceholderScalar(self):
     with self.test_session(use_gpu=True):
       labels = array_ops.placeholder(np.int32)
-      y = nn_ops.sparse_softmax_cross_entropy_with_logits([[7.]], labels)
+      y = nn_ops.sparse_softmax_cross_entropy_with_logits(
+          labels=labels, logits=[[7.]])
       with self.assertRaisesOpError("labels must be 1-D"):
         y.eval(feed_dict={labels: 0})
 
   def testVector(self):
     with self.test_session(use_gpu=True):
       loss = nn_ops.sparse_softmax_cross_entropy_with_logits(
-          constant_op.constant([1.0]), constant_op.constant(0))
+          labels=constant_op.constant(0), logits=constant_op.constant([1.0]))
       self.assertAllClose(0.0, loss.eval())
 
   def testFloat(self):
@@ -191,7 +192,8 @@ class SparseXentTest(test.TestCase):
           shape=[3, 4],
           dtype=dtypes.float64,
           name="f")
-      x = nn_ops.sparse_softmax_cross_entropy_with_logits(f, l, name="xent")
+      x = nn_ops.sparse_softmax_cross_entropy_with_logits(
+          labels=l, logits=f, name="xent")
       err = gradient_checker.compute_gradient_error(f, [3, 4], x, [3])
     print("cross entropy gradient err = ", err)
     self.assertLess(err, 5e-8)
@@ -201,7 +203,8 @@ class SparseXentTest(test.TestCase):
     # manually reshape loss
     np_loss = np.reshape(np_loss, np.array(labels).shape)
     with self.test_session(use_gpu=True) as sess:
-      loss = nn_ops.sparse_softmax_cross_entropy_with_logits(features, labels)
+      loss = nn_ops.sparse_softmax_cross_entropy_with_logits(
+          labels=labels, logits=features)
       backprop = loss.op.inputs[0].op.outputs[1]
       tf_loss, tf_backprop = sess.run([loss, backprop])
     self.assertAllCloseAccordingToType(np_loss, tf_loss)
@@ -225,7 +228,7 @@ class SparseXentTest(test.TestCase):
         labels = array_ops.placeholder(dtypes.int32, shape=[None, 1])
         logits = array_ops.placeholder(dtypes.float32, shape=[None, 3])
         ce = nn_ops.sparse_softmax_cross_entropy_with_logits(
-            logits, array_ops.squeeze(labels))
+            labels=array_ops.squeeze(labels), logits=logits)
         labels_v2 = np.zeros((1, 1), dtype=np.int32)
         logits_v2 = np.random.randn(1, 3)
         sess.run([ce], feed_dict={labels: labels_v2, logits: logits_v2})
@@ -243,7 +246,7 @@ def _sparse_vs_dense_xent_benchmark_dense(labels, logits):
                                         array_ops.stack([length]), 1.0, 0.0)
   target = array_ops.reshape(target, array_ops.stack([-1, num_entries]))
   crossent = nn_ops.softmax_cross_entropy_with_logits(
-      logits, target, name="SequenceLoss/CrossEntropy")
+      labels=target, logits=logits, name="SequenceLoss/CrossEntropy")
   crossent_sum = math_ops.reduce_sum(crossent)
   grads = gradients_impl.gradients([crossent_sum], [logits])[0]
 
diff --git a/tensorflow/python/kernel_tests/xent_op_test.py b/tensorflow/python/kernel_tests/xent_op_test.py
index ac56f567ce..e1e0566124 100644
--- a/tensorflow/python/kernel_tests/xent_op_test.py
+++ b/tensorflow/python/kernel_tests/xent_op_test.py
@@ -57,7 +57,7 @@ class XentTest(test.TestCase):
     np_loss, _ = self._npXent(np_features, np_labels, dim=dim)
     with self.test_session(use_gpu=use_gpu) as sess:
       loss = nn_ops.softmax_cross_entropy_with_logits(
-          np_features, np_labels, dim=dim)
+          labels=np_labels, logits=np_features, dim=dim)
       tf_loss = sess.run(loss)
     print("np_loss:", np_loss)
     print("tf_loss:", tf_loss)
@@ -166,7 +166,8 @@ class XentTest(test.TestCase):
           shape=[3, 4],
           dtype=dtypes.float64,
           name="f")
-      x = nn_ops.softmax_cross_entropy_with_logits(f, l, name="xent")
+      x = nn_ops.softmax_cross_entropy_with_logits(labels=l, logits=f,
+                                                   name="xent")
       err = gradient_checker.compute_gradient_error(f, [3, 4], x, [3])
     print("cross entropy gradient err = ", err)
     self.assertLess(err, 5e-8)