Update generated Python Op docs.

Change: 123137273

tensorflow/g3doc/api_docs/python/functions_and_classes/tf.sparse_softmax.md

@@ -0,0 +1,51 @@
+### `tf.sparse_softmax(sp_input, name=None)` {#sparse_softmax}
+
+Applies softmax to a batched N-D `SparseTensor`.
+
+The inputs represent an N-D SparseTensor  with logical shape `[..., B, C]`
+(where `N >= 2`), and with indices sorted in the canonical lexicographic
+order.
+
+This op is equivalent to applying the normal `tf.nn.softmax()` to each
+innermost logical submatrix with shape `[B, C]`, but with the catch that *the
+implicitly zero elements do not participate*.  Specifically, the algorithm is
+equivalent to:
+
+  (1) Applies `tf.nn.softmax()` to a densified view of each innermost
+      submatrix with shape `[B, C]`, along the size-C dimension;
+  (2) Masks out the original implicitly-zero locations;
+  (3) Renormalizes the remaining elements.
+
+Hence, the `SparseTensor` result has exactly the same non-zero indices and
+shape.
+
+Example:
+```python
+# First batch:
+# [?   e.]
+# [1.  ? ]
+# Second batch:
+# [e   ? ]
+# [e   e ]
+shape = [2, 2, 2]  # 3-D SparseTensor
+values = np.asarray([[[0., np.e], [1., 0.]], [[np.e, 0.], [np.e, np.e]]])
+indices = np.vstack(np.where(values)).astype(np.int64).T
+
+result = tf.sparse_softmax(tf.SparseTensor(indices, values, shape))
+# ...returning a 3-D SparseTensor, equivalent to:
+# [?   1.]     [1    ?]
+# [1.  ? ] and [.5  .5]
+# where ? means implicitly zero.
+```
+
+##### Args:
+
+
+*  <b>`sp_input`</b>: N-D `SparseTensor`, where `N >= 2`.
+*  <b>`name`</b>: optional name of the operation.
+
+##### Returns:
+
+
+*  <b>`output`</b>: N-D `SparseTensor` representing the results.
+

tensorflow/g3doc/api_docs/python/index.md

@@ -352,6 +352,7 @@
   * [`sparse_reorder`](../../api_docs/python/sparse_ops.md#sparse_reorder)
   * [`sparse_reset_shape`](../../api_docs/python/sparse_ops.md#sparse_reset_shape)
   * [`sparse_retain`](../../api_docs/python/sparse_ops.md#sparse_retain)
+  * [`sparse_softmax`](../../api_docs/python/sparse_ops.md#sparse_softmax)
   * [`sparse_split`](../../api_docs/python/sparse_ops.md#sparse_split)
   * [`sparse_tensor_dense_matmul`](../../api_docs/python/sparse_ops.md#sparse_tensor_dense_matmul)
   * [`sparse_tensor_to_dense`](../../api_docs/python/sparse_ops.md#sparse_tensor_to_dense)

tensorflow/g3doc/api_docs/python/sparse_ops.md

@@ -826,6 +826,60 @@ Then,
 *  <b>`TypeError`</b>: If both `a` and `b` are `Tensor`s.  Use `tf.add()` instead.
 
 
+- - -
+
+### `tf.sparse_softmax(sp_input, name=None)` {#sparse_softmax}
+
+Applies softmax to a batched N-D `SparseTensor`.
+
+The inputs represent an N-D SparseTensor  with logical shape `[..., B, C]`
+(where `N >= 2`), and with indices sorted in the canonical lexicographic
+order.
+
+This op is equivalent to applying the normal `tf.nn.softmax()` to each
+innermost logical submatrix with shape `[B, C]`, but with the catch that *the
+implicitly zero elements do not participate*.  Specifically, the algorithm is
+equivalent to:
+
+  (1) Applies `tf.nn.softmax()` to a densified view of each innermost
+      submatrix with shape `[B, C]`, along the size-C dimension;
+  (2) Masks out the original implicitly-zero locations;
+  (3) Renormalizes the remaining elements.
+
+Hence, the `SparseTensor` result has exactly the same non-zero indices and
+shape.
+
+Example:
+```python
+# First batch:
+# [?   e.]
+# [1.  ? ]
+# Second batch:
+# [e   ? ]
+# [e   e ]
+shape = [2, 2, 2]  # 3-D SparseTensor
+values = np.asarray([[[0., np.e], [1., 0.]], [[np.e, 0.], [np.e, np.e]]])
+indices = np.vstack(np.where(values)).astype(np.int64).T
+
+result = tf.sparse_softmax(tf.SparseTensor(indices, values, shape))
+# ...returning a 3-D SparseTensor, equivalent to:
+# [?   1.]     [1    ?]
+# [1.  ? ] and [.5  .5]
+# where ? means implicitly zero.
+```
+
+##### Args:
+
+
+*  <b>`sp_input`</b>: N-D `SparseTensor`, where `N >= 2`.
+*  <b>`name`</b>: optional name of the operation.
+
+##### Returns:
+
+
+*  <b>`output`</b>: N-D `SparseTensor` representing the results.
+
+
 - - -
 
 ### `tf.sparse_tensor_dense_matmul(sp_a, b, adjoint_a=False, adjoint_b=False, name=None)` {#sparse_tensor_dense_matmul}