Update generated Python Op docs.

Change: 131279298

tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.reduce_logsumexp.md

@@ -0,0 +1,41 @@
+### `tf.reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_logsumexp}
+
+Computes log(sum(exp(elements across dimensions of a tensor))).
+
+Reduces `input_tensor` along the dimensions given in `reduction_indices`.
+Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
+entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
+are retained with length 1.
+
+If `reduction_indices` has no entries, all dimensions are reduced, and a
+tensor with a single element is returned.
+
+This funciton is more numerically stable than log(sum(exp(input))). It avoids
+overflows caused by taking the exp of large inputs and underflows caused by
+taking the log of small inputs.
+
+For example:
+
+```python
+# 'x' is [[0, 0, 0]]
+#         [0, 0, 0]]
+tf.reduce_logsumexp(x) ==> log(6)
+tf.reduce_logsumexp(x, 0) ==> [log(2), log(2), log(2)]
+tf.reduce_logsumexp(x, 1) ==> [log(3), log(3)]
+tf.reduce_logsumexp(x, 1, keep_dims=True) ==> [[log(3)], [log(3)]]
+tf.reduce_logsumexp(x, [0, 1]) ==> log(6)
+```
+
+##### Args:
+
+
+*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
+*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the defaut),
+    reduces all dimensions.
+*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The reduced tensor.
+

tensorflow/g3doc/api_docs/python/index.md

@@ -239,6 +239,7 @@
   * [`real`](../../api_docs/python/math_ops.md#real)
   * [`reduce_all`](../../api_docs/python/math_ops.md#reduce_all)
   * [`reduce_any`](../../api_docs/python/math_ops.md#reduce_any)
+  * [`reduce_logsumexp`](../../api_docs/python/math_ops.md#reduce_logsumexp)
   * [`reduce_max`](../../api_docs/python/math_ops.md#reduce_max)
   * [`reduce_mean`](../../api_docs/python/math_ops.md#reduce_mean)
   * [`reduce_min`](../../api_docs/python/math_ops.md#reduce_min)

tensorflow/g3doc/api_docs/python/math_ops.md

@@ -2667,6 +2667,50 @@ tf.reduce_any(x, 1) ==> [True, False]
   The reduced tensor.
 
 
+- - -
+
+### `tf.reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_logsumexp}
+
+Computes log(sum(exp(elements across dimensions of a tensor))).
+
+Reduces `input_tensor` along the dimensions given in `reduction_indices`.
+Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
+entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
+are retained with length 1.
+
+If `reduction_indices` has no entries, all dimensions are reduced, and a
+tensor with a single element is returned.
+
+This funciton is more numerically stable than log(sum(exp(input))). It avoids
+overflows caused by taking the exp of large inputs and underflows caused by
+taking the log of small inputs.
+
+For example:
+
+```python
+# 'x' is [[0, 0, 0]]
+#         [0, 0, 0]]
+tf.reduce_logsumexp(x) ==> log(6)
+tf.reduce_logsumexp(x, 0) ==> [log(2), log(2), log(2)]
+tf.reduce_logsumexp(x, 1) ==> [log(3), log(3)]
+tf.reduce_logsumexp(x, 1, keep_dims=True) ==> [[log(3)], [log(3)]]
+tf.reduce_logsumexp(x, [0, 1]) ==> log(6)
+```
+
+##### Args:
+
+
+*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
+*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the defaut),
+    reduces all dimensions.
+*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The reduced tensor.
+
+
 
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