1 files changed, 38 insertions, 0 deletions
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.sparse_softmax_cross_entropy_with_logits.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.sparse_softmax_cross_entropy_with_logits.md
new file mode 100644
index 0000000000..6d53d84c5b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.sparse_softmax_cross_entropy_with_logits.md
@@ -0,0 +1,38 @@
+### `tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels, name=None)` {#sparse_softmax_cross_entropy_with_logits}
+
+Computes sparse softmax cross entropy between `logits` and `labels`.
+
+Measures the probability error in discrete classification tasks in which the
+classes are mutually exclusive (each entry is in exactly one class).  For
+example, each CIFAR-10 image is labeled with one and only one label: an image
+can be a dog or a truck, but not both.
+
+**NOTE:**  For this operation, the probability of a given label is considered
+exclusive.  That is, soft classes are not allowed, and the `labels` vector
+must provide a single specific index for the true class for each row of
+`logits` (each minibatch entry).  For soft softmax classification with
+a probability distribution for each entry, see
+`softmax_cross_entropy_with_logits`.
+
+**WARNING:** This op expects unscaled logits, since it performs a softmax
+on `logits` internally for efficiency.  Do not call this op with the
+output of `softmax`, as it will produce incorrect results.
+
+`logits` must have the shape `[batch_size, num_classes]`
+and dtype `float32` or `float64`.
+
+`labels` must have the shape `[batch_size]` and dtype `int32` or `int64`.
+
+##### Args:
+
+
+*  <b>`logits`</b>: Unscaled log probabilities.
+*  <b>`labels`</b>: Each entry `labels[i]` must be an index in `[0, num_classes)`. Other
+    values will result in a loss of 0, but incorrect gradient computations.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A 1-D `Tensor` of length `batch_size` of the same type as `logits` with the
+  softmax cross entropy loss.
+