fixed typo tf.nn.(sparse_)softmax_cross_entropy_with_logits

Change: 139913751

1 files changed, 3 insertions, 3 deletions

diff --git a/tensorflow/g3doc/tutorials/mnist/beginners/index.md b/tensorflow/g3doc/tutorials/mnist/beginners/index.md
index 5d3d6d42e3..e5d3f28de6 100644
--- a/tensorflow/g3doc/tutorials/mnist/beginners/index.md
+++ b/tensorflow/g3doc/tutorials/mnist/beginners/index.md
@@ -343,13 +343,13 @@ each element of `y_` with the corresponding element of `tf.log(y)`. Then
 `reduction_indices=[1]` parameter. Finally, `tf.reduce_mean` computes the mean
 over all the examples in the batch.
 
-(Note that in the source code, we don't use this formulation, because it is
+Note that in the source code, we don't use this formulation, because it is
 numerically unstable.  Instead, we apply
 `tf.nn.softmax_cross_entropy_with_logits` on the unnormalized logits (e.g., we
 call `softmax_cross_entropy_with_logits` on `tf.matmul(x, W) + b`), because this
 more numerically stable function internally computes the softmax activation.  In
-your code, consider using tf.nn.(sparse_)softmax_cross_entropy_with_logits
-instead).
+your code, consider using `tf.nn.softmax_cross_entropy_with_logits`
+instead.
 
 Now that we know what we want our model to do, it's very easy to have TensorFlow
 train it to do so.  Because TensorFlow knows the entire graph of your