Update generated Python Op docs.

Change: 126350719

6 files changed, 206 insertions, 88 deletions

diff --git a/tensorflow/g3doc/api_docs/python/contrib.framework.md b/tensorflow/g3doc/api_docs/python/contrib.framework.md
index 4c23455597..df4df30d19 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.framework.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.framework.md
@@ -227,50 +227,49 @@ adds them via `tf.add_n`.
 
 - - -
 
-### `tf.contrib.framework.safe_embedding_lookup_sparse(embedding_weights, sparse_ids, sparse_weights=None, combiner='mean', default_id=None, name=None, partition_strategy='div')` {#safe_embedding_lookup_sparse}
-
-Lookup embedding results, accounting for invalid IDs and empty features.
-
-The partitioned embedding in `embedding_weights` must all be the same shape
-except for the first dimension. The first dimension is allowed to vary as the
-vocabulary size is not necessarily a multiple of `P`.
-
-Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
-with non-positive weight. For an entry with no features, the embedding vector
-for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
-
-The ids and weights may be multi-dimensional. Embeddings are always aggregated
-along the last dimension.
-
-##### Args:
-
-
-*  <b>`embedding_weights`</b>: A list of `P` float tensors or values representing
-      partitioned embedding tensors.  The total unpartitioned shape should be
-      `[e_0, e_1, ..., e_m]`, where `e_0` represents the vocab size and
-      `e_1, ..., e_m` are the embedding dimensions.
-*  <b>`sparse_ids`</b>: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
-      ids. `d_0` is typically batch size.
-*  <b>`sparse_weights`</b>: `SparseTensor` of same shape as `sparse_ids`, containing
-      float weights corresponding to `sparse_ids`, or `None` if all weights
-      are be assumed to be 1.0.
-*  <b>`combiner`</b>: A string specifying how to combine embedding results for each
-      entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
-      the default.
-*  <b>`default_id`</b>: The id to use for an entry with no features.
-*  <b>`name`</b>: A name for this operation (optional).
-*  <b>`partition_strategy`</b>: A string specifying the partitioning strategy.
-      Currently `"div"` and `"mod"` are supported. Default is `"div"`.
-
-
-##### Returns:
-
-  Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `embedding_weights` is empty.
+### `tf.contrib.framework.safe_embedding_lookup_sparse(*args, **kwargs)` {#safe_embedding_lookup_sparse}
+
+Lookup embedding results, accounting for invalid IDs and empty features. (deprecated)
+
+THIS FUNCTION IS DEPRECATED. It will be removed after 2016-09-01.
+Instructions for updating:
+Please use tf.contrib.layers.safe_embedding_lookup_sparse.
+
+  The partitioned embedding in `embedding_weights` must all be the same shape
+  except for the first dimension. The first dimension is allowed to vary as the
+  vocabulary size is not necessarily a multiple of `P`.
+
+  Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
+  with non-positive weight. For an entry with no features, the embedding vector
+  for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
+
+  The ids and weights may be multi-dimensional. Embeddings are always aggregated
+  along the last dimension.
+
+  Args:
+    embedding_weights:  A list of `P` float tensors or values representing
+        partitioned embedding tensors.  The total unpartitioned shape should be
+        `[e_0, e_1, ..., e_m]`, where `e_0` represents the vocab size and
+        `e_1, ..., e_m` are the embedding dimensions.
+    sparse_ids: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
+        ids. `d_0` is typically batch size.
+    sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing
+        float weights corresponding to `sparse_ids`, or `None` if all weights
+        are be assumed to be 1.0.
+    combiner: A string specifying how to combine embedding results for each
+        entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
+        the default.
+    default_id: The id to use for an entry with no features.
+    name: A name for this operation (optional).
+    partition_strategy: A string specifying the partitioning strategy.
+        Currently `"div"` and `"mod"` are supported. Default is `"div"`.
+
+
+  Returns:
+    Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
+
+  Raises:
+    ValueError: if `embedding_weights` is empty.
 
 
 - - -
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.safe_embedding_lookup_sparse.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.safe_embedding_lookup_sparse.md
index f56043cc0d..3d5491c98a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.safe_embedding_lookup_sparse.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.safe_embedding_lookup_sparse.md
@@ -1,45 +1,44 @@
-### `tf.contrib.framework.safe_embedding_lookup_sparse(embedding_weights, sparse_ids, sparse_weights=None, combiner='mean', default_id=None, name=None, partition_strategy='div')` {#safe_embedding_lookup_sparse}
-
-Lookup embedding results, accounting for invalid IDs and empty features.
-
-The partitioned embedding in `embedding_weights` must all be the same shape
-except for the first dimension. The first dimension is allowed to vary as the
-vocabulary size is not necessarily a multiple of `P`.
-
-Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
-with non-positive weight. For an entry with no features, the embedding vector
-for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
-
-The ids and weights may be multi-dimensional. Embeddings are always aggregated
-along the last dimension.
-
-##### Args:
-
-
-*  <b>`embedding_weights`</b>: A list of `P` float tensors or values representing
-      partitioned embedding tensors.  The total unpartitioned shape should be
-      `[e_0, e_1, ..., e_m]`, where `e_0` represents the vocab size and
-      `e_1, ..., e_m` are the embedding dimensions.
-*  <b>`sparse_ids`</b>: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
-      ids. `d_0` is typically batch size.
-*  <b>`sparse_weights`</b>: `SparseTensor` of same shape as `sparse_ids`, containing
-      float weights corresponding to `sparse_ids`, or `None` if all weights
-      are be assumed to be 1.0.
-*  <b>`combiner`</b>: A string specifying how to combine embedding results for each
-      entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
-      the default.
-*  <b>`default_id`</b>: The id to use for an entry with no features.
-*  <b>`name`</b>: A name for this operation (optional).
-*  <b>`partition_strategy`</b>: A string specifying the partitioning strategy.
-      Currently `"div"` and `"mod"` are supported. Default is `"div"`.
-
-
-##### Returns:
-
-  Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `embedding_weights` is empty.
+### `tf.contrib.framework.safe_embedding_lookup_sparse(*args, **kwargs)` {#safe_embedding_lookup_sparse}
+
+Lookup embedding results, accounting for invalid IDs and empty features. (deprecated)
+
+THIS FUNCTION IS DEPRECATED. It will be removed after 2016-09-01.
+Instructions for updating:
+Please use tf.contrib.layers.safe_embedding_lookup_sparse.
+
+  The partitioned embedding in `embedding_weights` must all be the same shape
+  except for the first dimension. The first dimension is allowed to vary as the
+  vocabulary size is not necessarily a multiple of `P`.
+
+  Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
+  with non-positive weight. For an entry with no features, the embedding vector
+  for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
+
+  The ids and weights may be multi-dimensional. Embeddings are always aggregated
+  along the last dimension.
+
+  Args:
+    embedding_weights:  A list of `P` float tensors or values representing
+        partitioned embedding tensors.  The total unpartitioned shape should be
+        `[e_0, e_1, ..., e_m]`, where `e_0` represents the vocab size and
+        `e_1, ..., e_m` are the embedding dimensions.
+    sparse_ids: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
+        ids. `d_0` is typically batch size.
+    sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing
+        float weights corresponding to `sparse_ids`, or `None` if all weights
+        are be assumed to be 1.0.
+    combiner: A string specifying how to combine embedding results for each
+        entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
+        the default.
+    default_id: The id to use for an entry with no features.
+    name: A name for this operation (optional).
+    partition_strategy: A string specifying the partitioning strategy.
+        Currently `"div"` and `"mod"` are supported. Default is `"div"`.
+
+
+  Returns:
+    Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
+
+  Raises:
+    ValueError: if `embedding_weights` is empty.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.sparse_minimum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.sparse_minimum.md
new file mode 100644
index 0000000000..4419f736b9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.sparse_minimum.md
@@ -0,0 +1,28 @@
+### `tf.sparse_minimum(sp_a, sp_b, name=None)` {#sparse_minimum}
+
+Returns the element-wise min of two SparseTensors.
+
+Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
+Example:
+
+```python
+sp_zero = ops.SparseTensor([[0]], [0], [7])
+sp_one = ops.SparseTensor([[1]], [1], [7])
+res = tf.sparse_minimum(sp_zero, sp_one).eval()
+# "res" should be equal to SparseTensor([[0], [1]], [0, 0], [7]).
+```
+
+##### Args:
+
+
+*  <b>`sp_a`</b>: a `SparseTensor` operand whose dtype is real, and indices
+    lexicographically ordered.
+*  <b>`sp_b`</b>: the other `SparseTensor` operand with the same requirements (and the
+    same shape).
+*  <b>`name`</b>: optional name of the operation.
+
+##### Returns:
+
+
+*  <b>`output`</b>: the output SparseTensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_maximum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_maximum.md
new file mode 100644
index 0000000000..b934c3b1cd
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_maximum.md
@@ -0,0 +1,28 @@
+### `tf.sparse_maximum(sp_a, sp_b, name=None)` {#sparse_maximum}
+
+Returns the element-wise max of two SparseTensors.
+
+Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
+Example:
+
+```python
+sp_zero = ops.SparseTensor([[0]], [0], [7])
+sp_one = ops.SparseTensor([[1]], [1], [7])
+res = tf.sparse_maximum(sp_zero, sp_one).eval()
+# "res" should be equal to SparseTensor([[0], [1]], [0, 1], [7]).
+```
+
+##### Args:
+
+
+*  <b>`sp_a`</b>: a `SparseTensor` operand whose dtype is real, and indices
+    lexicographically ordered.
+*  <b>`sp_b`</b>: the other `SparseTensor` operand with the same requirements (and the
+    same shape).
+*  <b>`name`</b>: optional name of the operation.
+
+##### Returns:
+
+
+*  <b>`output`</b>: the output SparseTensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 5c4f7107d1..e9af0e6575 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -367,7 +367,9 @@
   * [`sparse_add`](../../api_docs/python/sparse_ops.md#sparse_add)
   * [`sparse_concat`](../../api_docs/python/sparse_ops.md#sparse_concat)
   * [`sparse_fill_empty_rows`](../../api_docs/python/sparse_ops.md#sparse_fill_empty_rows)
+  * [`sparse_maximum`](../../api_docs/python/sparse_ops.md#sparse_maximum)
   * [`sparse_merge`](../../api_docs/python/sparse_ops.md#sparse_merge)
+  * [`sparse_minimum`](../../api_docs/python/sparse_ops.md#sparse_minimum)
   * [`sparse_reduce_sum`](../../api_docs/python/sparse_ops.md#sparse_reduce_sum)
   * [`sparse_reorder`](../../api_docs/python/sparse_ops.md#sparse_reorder)
   * [`sparse_reset_shape`](../../api_docs/python/sparse_ops.md#sparse_reset_shape)
diff --git a/tensorflow/g3doc/api_docs/python/sparse_ops.md b/tensorflow/g3doc/api_docs/python/sparse_ops.md
index 6781eadef4..1665420b5b 100644
--- a/tensorflow/g3doc/api_docs/python/sparse_ops.md
+++ b/tensorflow/g3doc/api_docs/python/sparse_ops.md
@@ -1155,3 +1155,65 @@ B dense [k, n]
     return A*B
 
 
+- - -
+
+### `tf.sparse_maximum(sp_a, sp_b, name=None)` {#sparse_maximum}
+
+Returns the element-wise max of two SparseTensors.
+
+Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
+Example:
+
+```python
+sp_zero = ops.SparseTensor([[0]], [0], [7])
+sp_one = ops.SparseTensor([[1]], [1], [7])
+res = tf.sparse_maximum(sp_zero, sp_one).eval()
+# "res" should be equal to SparseTensor([[0], [1]], [0, 1], [7]).
+```
+
+##### Args:
+
+
+*  <b>`sp_a`</b>: a `SparseTensor` operand whose dtype is real, and indices
+    lexicographically ordered.
+*  <b>`sp_b`</b>: the other `SparseTensor` operand with the same requirements (and the
+    same shape).
+*  <b>`name`</b>: optional name of the operation.
+
+##### Returns:
+
+
+*  <b>`output`</b>: the output SparseTensor.
+
+
+- - -
+
+### `tf.sparse_minimum(sp_a, sp_b, name=None)` {#sparse_minimum}
+
+Returns the element-wise min of two SparseTensors.
+
+Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
+Example:
+
+```python
+sp_zero = ops.SparseTensor([[0]], [0], [7])
+sp_one = ops.SparseTensor([[1]], [1], [7])
+res = tf.sparse_minimum(sp_zero, sp_one).eval()
+# "res" should be equal to SparseTensor([[0], [1]], [0, 0], [7]).
+```
+
+##### Args:
+
+
+*  <b>`sp_a`</b>: a `SparseTensor` operand whose dtype is real, and indices
+    lexicographically ordered.
+*  <b>`sp_b`</b>: the other `SparseTensor` operand with the same requirements (and the
+    same shape).
+*  <b>`name`</b>: optional name of the operation.
+
+##### Returns:
+
+
+*  <b>`output`</b>: the output SparseTensor.
+
+