Surface control_flow_ops.case to public. Update docs. Add unit tests.

Change: 115496194

12 files changed, 421 insertions, 43 deletions

diff --git a/tensorflow/g3doc/api_docs/python/array_ops.md b/tensorflow/g3doc/api_docs/python/array_ops.md
index d532731f5c..c634ee799a 100644
--- a/tensorflow/g3doc/api_docs/python/array_ops.md
+++ b/tensorflow/g3doc/api_docs/python/array_ops.md
@@ -1285,6 +1285,120 @@ boolean_mask(tensor, mask) ==> [[1, 2], [5, 6]]
 ```
 
 
+- - -
+
+### `tf.one_hot(indices, depth, on_value, off_value, axis=None, name=None)` {#one_hot}
+
+Returns a one-hot tensor.
+
+The locations represented by indices in `indices` take value `on_value`,
+while all other locations take value `off_value`.
+
+If the input `indices` is rank `N`, the output will have rank `N+1`,
+The new axis is created at dimension `axis` (default: the new axis is
+appended at the end).
+
+If `indices` is a scalar the output shape will be a vector of length `depth`.
+
+If `indices` is a vector of length `features`, the output shape will be:
+```
+  features x depth if axis == -1
+  depth x features if axis == 0
+```
+
+If `indices` is a matrix (batch) with shape `[batch, features]`,
+the output shape will be:
+```
+  batch x features x depth if axis == -1
+  batch x depth x features if axis == 1
+  depth x batch x features if axis == 0
+```
+
+
+Examples
+=========
+
+Suppose that
+
+```
+  indices = [0, 2, -1, 1]
+  depth = 3
+  on_value = 5.0
+  off_value = 0.0
+  axis = -1
+```
+
+Then output is `[4 x 3]`:
+
+    ```output =
+      [5.0 0.0 0.0]  // one_hot(0)
+      [0.0 0.0 5.0]  // one_hot(2)
+      [0.0 0.0 0.0]  // one_hot(-1)
+      [0.0 5.0 0.0]  // one_hot(1)
+    ```
+
+Suppose that
+
+```
+  indices = [0, 2, -1, 1]
+  depth = 3
+  on_value = 0.0
+  off_value = 3.0
+  axis = 0
+```
+
+Then output is `[3 x 4]`:
+
+    ```output =
+      [0.0 3.0 3.0 3.0]
+      [3.0 3.0 3.0 0.0]
+      [3.0 3.0 3.0 3.0]
+      [3.0 0.0 3.0 3.0]
+    //  ^                one_hot(0)
+    //      ^            one_hot(2)
+    //          ^        one_hot(-1)
+    //              ^    one_hot(1)
+    ```
+Suppose that
+
+```
+  indices = [[0, 2], [1, -1]]
+  depth = 3
+  on_value = 1.0
+  off_value = 0.0
+  axis = -1
+```
+
+Then output is `[2 x 2 x 3]`:
+
+    ```output =
+      [
+        [1.0, 0.0, 0.0]  // one_hot(0)
+        [0.0, 0.0, 1.0]  // one_hot(2)
+      ][
+        [0.0, 1.0, 0.0]  // one_hot(1)
+        [0.0, 0.0, 0.0]  // one_hot(-1)
+      ]```
+
+##### Args:
+
+
+*  <b>`indices`</b>: A `Tensor` of type `int64`. A tensor of indices.
+*  <b>`depth`</b>: A `Tensor` of type `int32`.
+    A scalar defining the depth of the one hot dimension.
+*  <b>`on_value`</b>: A `Tensor`.
+    A scalar defining the value to fill in output when `indices[j] = i`.
+*  <b>`off_value`</b>: A `Tensor`. Must have the same type as `on_value`.
+    A scalar defining the value to fill in output when `indices[j] != i`.
+*  <b>`axis`</b>: An optional `int`. Defaults to `-1`.
+    The axis to fill (default: -1, a new inner-most axis).
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `on_value`. The one-hot tensor.
+
+
 
 ## Other Functions and Classes
 - - -
diff --git a/tensorflow/g3doc/api_docs/python/contrib.layers.md b/tensorflow/g3doc/api_docs/python/contrib.layers.md
index d9351e47ed..78bc28d8d5 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.layers.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.layers.md
@@ -301,7 +301,7 @@ activation.
 
 - - -
 
-### `tf.contrib.layers.summarize_tensor(tensor)` {#summarize_tensor}
+### `tf.contrib.layers.summarize_tensor(tensor, tag=None)` {#summarize_tensor}
 
 Summarize a tensor using a suitable summary type.
 
@@ -313,6 +313,7 @@ other tensors, `histogram_summary` is used.
 
 
 *  <b>`tensor`</b>: The tensor to summarize
+*  <b>`tag`</b>: The tag to use, if None then use tensor's op's name.
 
 ##### Returns:
 
@@ -377,3 +378,31 @@ be `dtypes.float32` or `dtypes.float64`. If neither `tensors` nor
       float.
 
 
+- - -
+
+### `tf.contrib.layers.assert_scalar_int(tensor)` {#assert_scalar_int}
+
+Assert `tensor` is 0-D, of type `tf.int32` or `tf.int64`.
+
+##### Args:
+
+
+*  <b>`tensor`</b>: Tensor to test.
+
+##### Returns:
+
+  `tensor`, for chaining.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `tensor` is not 0-D, of type `tf.int32` or `tf.int64`.
+
+
+- - -
+
+### `tf.contrib.layers.is_numeric_tensor(tensor)` {#is_numeric_tensor}
+
+
+
+
diff --git a/tensorflow/g3doc/api_docs/python/control_flow_ops.md b/tensorflow/g3doc/api_docs/python/control_flow_ops.md
index c4b4d6e4f4..dded55f87c 100644
--- a/tensorflow/g3doc/api_docs/python/control_flow_ops.md
+++ b/tensorflow/g3doc/api_docs/python/control_flow_ops.md
@@ -182,6 +182,84 @@ the same non-zero number and type of outputs.
 ```
 
 
+- - -
+
+### `tf.case(pred_fn_pairs, default, exclusive=False, name='case')` {#case}
+
+Create a case operation.
+
+The `pred_fn_pairs` parameter is a dict or list of pairs of size N.
+Each pair contains a boolean scalar tensor and a python callable that
+creates the tensors to be returned if the boolean evaluates to True. `default`
+is a callable generating a list of tensors. All the callables in
+`pred_fn_pairs` as well as `default` should return the same number and types
+of tensors.
+
+If `exclusive==True`, all predicates are evaluated, and a logging operation
+with an error is returned if more than one of the predicates evaluates to
+True. If `exclusive==False`, execution stops are the first predicate which
+evaluates to True, and the tensors generated by the corresponding function
+are returned immediately. If none of the predicates evaluate to True, this
+operation returns the tensors generated by `default`.
+
+Example 1:
+  Pseudocode:
+  ```
+    if (x < y) return 17;
+    else return 23;
+  ```
+
+  Expressions:
+  ```
+    f1 = lambda: tf.constant(17)
+    f2 = lambda: tf.constant(23)
+    r = case([(tf.less(x, y), f1)], default=f2)
+  ```
+
+Example 2:
+  Pseudocode:
+  ```
+    if (x < y && x > z) raise OpError("Only one predicate may evaluate true");
+    if (x < y) return 17;
+    else if (x > z) return 23;
+    else return -1;
+  ```
+
+  Expressions:
+  ```
+    x = tf.constant(0)
+    y = tf.constant(1)
+    z = tf.constant(2)
+    def f1(): return tf.constant(17)
+    def f2(): return tf.constant(23)
+    def f3(): return tf.constant(-1)
+    r = case({tf.less(x, y): f1, tf.greater(x, z): f2},
+             default=f3, exclusive=True)
+  ```
+
+##### Args:
+
+
+*  <b>`pred_fn_pairs`</b>: Dict or list of pairs of a boolean scalar tensor and a
+                 callable which returns a list of tensors.
+*  <b>`default`</b>: A callable that returns a list of tensors.
+*  <b>`exclusive`</b>: True iff more than one predicate is allowed to evaluate to True.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  The tensors returned by the first pair whose predicate evaluated to True, or
+  those returned by `default` if none does.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `pred_fn_pairs` is not a list/dictionary.
+*  <b>`TypeError`</b>: If `pred_fn_pairs` is a list but does not contain 2-tuples.
+*  <b>`TypeError`</b>: If `fns[i]` is not callable for any i, or `default` is not
+             callable.
+
+
 
 ## Logical Operators
 
diff --git a/tensorflow/g3doc/api_docs/python/framework.md b/tensorflow/g3doc/api_docs/python/framework.md
index 590355bdab..52d2560dd3 100644
--- a/tensorflow/g3doc/api_docs/python/framework.md
+++ b/tensorflow/g3doc/api_docs/python/framework.md
@@ -523,7 +523,7 @@ This method may be called concurrently from multiple threads.
 
 - - -
 
-#### `tf.Graph.unique_name(name)` {#Graph.unique_name}
+#### `tf.Graph.unique_name(name, mark_as_used=True)` {#Graph.unique_name}
 
 Return a unique operation name for `name`.
 
@@ -537,10 +537,17 @@ Operation names are displayed in error messages reported by the
 TensorFlow runtime, and in various visualization tools such as
 TensorBoard.
 
+If `mark_as_used` is set to `True`, which is the default, a new
+unique name is created and marked as in use. If it's set to `False`,
+the unique name is returned without actually being marked as used.
+This is useful when the caller simply wants to know what the name
+to be created will be.
+
 ##### Args:
 
 
 *  <b>`name`</b>: The name for an operation.
+*  <b>`mark_as_used`</b>: Whether to mark this name as being used.
 
 ##### Returns:
 
diff --git a/tensorflow/g3doc/api_docs/python/image.md b/tensorflow/g3doc/api_docs/python/image.md
index 5998b591a0..622edac7a9 100644
--- a/tensorflow/g3doc/api_docs/python/image.md
+++ b/tensorflow/g3doc/api_docs/python/image.md
@@ -400,6 +400,39 @@ dimension.
 
 - - -
 
+### `tf.image.central_crop(image, central_fraction)` {#central_crop}
+
+Crop the central region of the image.
+
+Remove the outer parts of an image but retain the central region of the image
+along each dimension. If we specify central_fraction = 0.5, this function
+returns the region marked with "X" in the below diagram.
+
+     --------
+    |        |
+    |  XXXX  |
+    |  XXXX  |
+    |        |   where "X" is the central 50% of the image.
+     --------
+
+##### Args:
+
+
+*  <b>`image`</b>: 3-D float Tensor of shape [height, width, depth]
+*  <b>`central_fraction`</b>: float (0, 1], fraction of size to crop
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if central_crop_fraction is not within (0, 1].
+
+##### Returns:
+
+  3-D float Tensor
+
+
+- - -
+
 ### `tf.image.pad_to_bounding_box(image, offset_height, offset_width, target_height, target_width)` {#pad_to_bounding_box}
 
 Pad `image` with zeros to the specified `height` and `width`.
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 82f7fa02fc..c85e25bbe8 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -92,6 +92,7 @@
   * [`dynamic_stitch`](../../api_docs/python/array_ops.md#dynamic_stitch)
   * [`expand_dims`](../../api_docs/python/array_ops.md#expand_dims)
   * [`gather`](../../api_docs/python/array_ops.md#gather)
+  * [`one_hot`](../../api_docs/python/array_ops.md#one_hot)
   * [`pack`](../../api_docs/python/array_ops.md#pack)
   * [`pad`](../../api_docs/python/array_ops.md#pad)
   * [`rank`](../../api_docs/python/array_ops.md#rank)
@@ -193,6 +194,7 @@
   * [`sparse_segment_sum`](../../api_docs/python/math_ops.md#sparse_segment_sum)
   * [`sqrt`](../../api_docs/python/math_ops.md#sqrt)
   * [`square`](../../api_docs/python/math_ops.md#square)
+  * [`squared_difference`](../../api_docs/python/math_ops.md#squared_difference)
   * [`sub`](../../api_docs/python/math_ops.md#sub)
   * [`transpose`](../../api_docs/python/math_ops.md#transpose)
   * [`truediv`](../../api_docs/python/math_ops.md#truediv)
@@ -203,6 +205,7 @@
 * **[Control Flow](../../api_docs/python/control_flow_ops.md)**:
   * [`add_check_numerics_ops`](../../api_docs/python/control_flow_ops.md#add_check_numerics_ops)
   * [`Assert`](../../api_docs/python/control_flow_ops.md#Assert)
+  * [`case`](../../api_docs/python/control_flow_ops.md#case)
   * [`check_numerics`](../../api_docs/python/control_flow_ops.md#check_numerics)
   * [`cond`](../../api_docs/python/control_flow_ops.md#cond)
   * [`count_up_to`](../../api_docs/python/control_flow_ops.md#count_up_to)
@@ -233,6 +236,7 @@
   * [`adjust_contrast`](../../api_docs/python/image.md#adjust_contrast)
   * [`adjust_hue`](../../api_docs/python/image.md#adjust_hue)
   * [`adjust_saturation`](../../api_docs/python/image.md#adjust_saturation)
+  * [`central_crop`](../../api_docs/python/image.md#central_crop)
   * [`convert_image_dtype`](../../api_docs/python/image.md#convert_image_dtype)
   * [`crop_to_bounding_box`](../../api_docs/python/image.md#crop_to_bounding_box)
   * [`decode_jpeg`](../../api_docs/python/image.md#decode_jpeg)
@@ -316,6 +320,7 @@
 
 * **[Neural Network](../../api_docs/python/nn.md)**:
   * [`avg_pool`](../../api_docs/python/nn.md#avg_pool)
+  * [`batch_normalization`](../../api_docs/python/nn.md#batch_normalization)
   * [`bias_add`](../../api_docs/python/nn.md#bias_add)
   * [`compute_accidental_hits`](../../api_docs/python/nn.md#compute_accidental_hits)
   * [`conv2d`](../../api_docs/python/nn.md#conv2d)
@@ -422,8 +427,10 @@
 
 * **[Layers (contrib)](../../api_docs/python/contrib.layers.md)**:
   * [`assert_same_float_dtype`](../../api_docs/python/contrib.layers.md#assert_same_float_dtype)
+  * [`assert_scalar_int`](../../api_docs/python/contrib.layers.md#assert_scalar_int)
   * [`convolution2d`](../../api_docs/python/contrib.layers.md#convolution2d)
   * [`fully_connected`](../../api_docs/python/contrib.layers.md#fully_connected)
+  * [`is_numeric_tensor`](../../api_docs/python/contrib.layers.md#is_numeric_tensor)
   * [`l1_regularizer`](../../api_docs/python/contrib.layers.md#l1_regularizer)
   * [`l2_regularizer`](../../api_docs/python/contrib.layers.md#l2_regularizer)
   * [`summarize_activation`](../../api_docs/python/contrib.layers.md#summarize_activation)
diff --git a/tensorflow/g3doc/api_docs/python/math_ops.md b/tensorflow/g3doc/api_docs/python/math_ops.md
index cdbb54aa6a..ca2ccbd232 100644
--- a/tensorflow/g3doc/api_docs/python/math_ops.md
+++ b/tensorflow/g3doc/api_docs/python/math_ops.md
@@ -628,6 +628,24 @@ Computes complementary error function of `x` element-wise.
     the return type is `quint8`.
 
 
+- - -
+
+### `tf.squared_difference(x, y, name=None)` {#squared_difference}
+
+Returns (x - y)(x - y) element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `complex64`, `int64`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
+
 
 ## Matrix Math Functions
 
diff --git a/tensorflow/g3doc/api_docs/python/state_ops.md b/tensorflow/g3doc/api_docs/python/state_ops.md
index 01988995bb..860087939c 100644
--- a/tensorflow/g3doc/api_docs/python/state_ops.md
+++ b/tensorflow/g3doc/api_docs/python/state_ops.md
@@ -104,7 +104,7 @@ Creating a variable.
 
 - - -
 
-#### `tf.Variable.__init__(initial_value=None, trainable=True, collections=None, validate_shape=True, name=None, variable_def=None)` {#Variable.__init__}
+#### `tf.Variable.__init__(initial_value=None, trainable=True, collections=None, validate_shape=True, caching_device=None, name=None, variable_def=None)` {#Variable.__init__}
 
 Creates a new variable with value `initial_value`.
 
@@ -131,6 +131,11 @@ variable to its initial value.
 *  <b>`validate_shape`</b>: If `False`, allows the variable to be initialized with a
     value of unknown shape. If `True`, the default, the shape of
     `initial_value` must be known.
+*  <b>`caching_device`</b>: Optional device string describing where the Variable
+    should be cached for reading.  Defaults to the Variable's device.
+    If not `None`, caches on another device.  Typical use is to cache
+    on the device where the Ops using the Variable reside, to deduplicate
+    copying through `Switch` and other conditional statements.
 *  <b>`name`</b>: Optional name for the variable. Defaults to `'Variable'` and gets
     uniquified automatically.
 *  <b>`variable_def`</b>: `VariableDef` protocol buffer. If not `None`, recreates
@@ -389,7 +394,7 @@ The `Operation` of this variable.
 
 #### `tf.Variable.from_proto(variable_def)` {#Variable.from_proto}
 
-
+Returns a `Variable` object created from `variable_def`.
 
 
 - - -
@@ -742,7 +747,7 @@ path can be passed directly to a call to `restore()`.
     kept in the same directory as the checkpoint files, is automatically
     managed by the saver to keep track of recent checkpoints.  Defaults to
     'checkpoint'.
-*  <b>`meta_graph_suffix`</b>: Suffix for MetaGraphDef file. Defaults to 'meta'.
+*  <b>`meta_graph_suffix`</b>: Suffix for `MetaGraphDef` file. Defaults to 'meta'.
 
 ##### Returns:
 
@@ -848,7 +853,7 @@ Writes `MetaGraphDef` to save_path/filename.
 
 #### `tf.train.Saver.from_proto(saver_def)` {#Saver.from_proto}
 
-
+Returns a `Saver` object created from `saver_def`.
 
 
 - - -
@@ -873,7 +878,11 @@ Sets the list of old checkpoint filenames and timestamps.
 
 #### `tf.train.Saver.to_proto()` {#Saver.to_proto}
 
-Returns a `SaverDef` protocol buffer.
+Converts this `Saver` to a `SaverDef` protocol buffer.
+
+##### Returns:
+
+  A `SaverDef` protocol buffer.
 
 
 
@@ -1022,17 +1031,26 @@ Attributes:
   initializer: default initializer passed to get_variable.
   regularizer: default regularizer passed to get_variable.
   reuse: Boolean or None, setting the reuse in get_variable.
+  caching_device: string, callable, or None: the caching device passed to
+    get_variable.
   name_scope: The name passed to tf.name_scope.
 - - -
 
-#### `tf.VariableScope.__init__(reuse, name='', initializer=None, regularizer=None, name_scope='')` {#VariableScope.__init__}
+#### `tf.VariableScope.__init__(reuse, name='', initializer=None, regularizer=None, caching_device=None, name_scope='')` {#VariableScope.__init__}
 
 Creates a new VariableScope with the given properties.
 
 
 - - -
 
-#### `tf.VariableScope.get_variable(var_store, name, shape=None, dtype=tf.float32, initializer=None, regularizer=None, trainable=True, collections=None)` {#VariableScope.get_variable}
+#### `tf.VariableScope.caching_device` {#VariableScope.caching_device}
+
+
+
+
+- - -
+
+#### `tf.VariableScope.get_variable(var_store, name, shape=None, dtype=tf.float32, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None)` {#VariableScope.get_variable}
 
 Gets an existing variable with this name or create a new one.
 
@@ -1074,6 +1092,13 @@ Reuse variables in this scope.
 
 - - -
 
+#### `tf.VariableScope.set_caching_device(caching_device)` {#VariableScope.set_caching_device}
+
+Set caching_device for this scope.
+
+
+- - -
+
 #### `tf.VariableScope.set_initializer(initializer)` {#VariableScope.set_initializer}
 
 Set initializer for this scope.
@@ -1089,7 +1114,7 @@ Set regularizer for this scope.
 
 - - -
 
-### `tf.variable_scope(name_or_scope, reuse=None, initializer=None, regularizer=None)` {#variable_scope}
+### `tf.variable_scope(name_or_scope, reuse=None, initializer=None, regularizer=None, caching_device=None)` {#variable_scope}
 
 Returns a context for variable scope.
 
@@ -1157,6 +1182,7 @@ then all its sub-scopes become reusing as well.
     well as all sub-scopes; if `None`, we just inherit the parent scope reuse.
 *  <b>`initializer`</b>: default initializer for variables within this scope.
 *  <b>`regularizer`</b>: default regularizer for variables within this scope.
+*  <b>`caching_device`</b>: default caching device for variables within this scope.
 
 ##### Returns:
 
@@ -1172,7 +1198,7 @@ then all its sub-scopes become reusing as well.
 
 - - -
 
-### `tf.variable_op_scope(values, name, default_name, initializer=None, regularizer=None)` {#variable_op_scope}
+### `tf.variable_op_scope(values, name, default_name, initializer=None, regularizer=None, caching_device=None)` {#variable_op_scope}
 
 Returns a context manager for defining an op that creates variables.
 
@@ -1208,8 +1234,10 @@ def my_op_with_vars(a, b, name=None):
     uniquified in the variable scope.
 *  <b>`default_name`</b>: The default name to use if the `name` argument is `None`, this
     name will be uniquified.
-*  <b>`initializer`</b>: A default initializer to pass to variable scope.
-*  <b>`regularizer`</b>: default regularizer for variables within this scope.
+*  <b>`initializer`</b>: The  default initializer to pass to variable scope.
+*  <b>`regularizer`</b>: The default regularizer for variables within this scope.
+*  <b>`caching_device`</b>: The default caching device for variables within this scope.
+
 
 ##### Returns:
 
diff --git a/tensorflow/g3doc/api_docs/python/train.md b/tensorflow/g3doc/api_docs/python/train.md
index 3e78ad3eac..ba5f270eca 100644
--- a/tensorflow/g3doc/api_docs/python/train.md
+++ b/tensorflow/g3doc/api_docs/python/train.md
@@ -410,36 +410,15 @@ current good choice is 1.0 or 0.1.
 
 Optimizer that implements the FTRL algorithm.
 
+See this [paper](
+https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf).
+
 - - -
 
 #### `tf.train.FtrlOptimizer.__init__(learning_rate, learning_rate_power=-0.5, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0, use_locking=False, name='Ftrl')` {#FtrlOptimizer.__init__}
 
 Construct a new FTRL optimizer.
 
-The Ftrl-proximal algorithm, abbreviated for Follow-the-regularized-leader,
-is described in the paper [Ad Click Prediction: a View from the Trenches](
-https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf).
-
-It can give a good performance vs. sparsity tradeoff.
-
-Ftrl-proximal uses its own global base learning rate and can behave like
-Adagrad with `learning_rate_power=-0.5`, or like gradient descent with
-`learning_rate_power=0.0`.
-
-The effective learning rate is adjusted per parameter, relative to this
-base learning rate as:
-
-```
-effective_learning_rate_i = (learning_rate /
-    pow(k + summed_squared_gradients_for_i, learning_rate_power));
-```
-
-where k is the small constant `initial_accumulator_value`.
-
-Note that the real regularization coefficient of `|w|^2` for objective
-function is `1 / lambda_2` if specifying `l2 = lambda_2` as argument when
-using this function.
-
 ##### Args:
 
 
@@ -1442,7 +1421,7 @@ depending on whether or not a `Coordinator` was passed to
 
 #### `tf.train.QueueRunner.from_proto(queue_runner_def)` {#QueueRunner.from_proto}
 
-
+Returns a `QueueRunner` object created from `queue_runner_def`.
 
 
 - - -
@@ -1936,7 +1915,7 @@ global_step: 10
 ##### Args:
 
 
-*  <b>`sess`</b>: A brain `Session` object.
+*  <b>`sess`</b>: A TensorFlow `Session` object.
 *  <b>`global_step_tensor`</b>: `Tensor` or the `name` of the operation that contains
     the global step.
 
@@ -2237,9 +2216,12 @@ Generates a checkpoint state proto.
 
 Recreates a Graph saved in a `MetaGraphDef` proto.
 
-This function reads from a file containing a `MetaGraphDef` proto,
-adds all the nodes from the graph_def proto to the current graph,
-recreates all the collections, and returns a saver from saver_def.
+This function takes a `MetaGraphDef` protocol buffer as input. If
+the argument is a file containing a `MetaGraphDef` protocol buffer ,
+it constructs a protocol buffer from the file content. The function
+then adds all the nodes from the `graph_def` field to the
+current graph, recreates all the collections, and returns a saver
+constructed from the `saver_def` field.
 
 In combination with `export_meta_graph()`, this function can be used to
 
@@ -2250,6 +2232,38 @@ In combination with `export_meta_graph()`, this function can be used to
 
 * Run inference from a saved graph and checkpoints.
 
+```Python
+...
+# Create a saver.
+saver = tf.train.Saver(...variables...)
+# Remember the training_op we want to run by adding it to a collection.
+tf.add_to_collection('train_op', train_op)
+sess = tf.Session()
+for step in xrange(1000000):
+    sess.run(train_op)
+    if step % 1000 == 0:
+        # Saves checkpoint, which by default also exports a meta_graph
+        # named 'my-model-global_step.meta'.
+        saver.save(sess, 'my-model', global_step=step)
+```
+
+Later we can continue training from this saved `meta_graph` without building
+the model from scratch.
+
+```Python
+with tf.Session() as sess:
+  new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')
+  new_saver.restore(sess, 'my-save-dir/my-model-10000')
+  # tf.get_collection() retrurns a list. In this example we only want the
+  # first one.
+  train_op = tf.get_collection('train_op')[0]
+  for step in xrange(1000000):
+    sess.run(train_op)
+```
+
+NOTE: Restarting training from saved `meta_graph` only works if the
+device assignments have not changed.
+
 ##### Args:
 
 
diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
index 6b84896bfa..c501682cc5 100644
--- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
+++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
@@ -1232,6 +1232,50 @@ class ControlFlowTest(tf.test.TestCase):
       self.assertAllClose(4.0, i.eval(feed_dict={d: 1}))
       self.assertAllClose(2.0 * math.sqrt(2), i.eval(feed_dict={d: 2}))
 
+  def testCase(self):
+    with self.test_session():
+      x = tf.constant(1)
+      y = tf.constant(2)
+      z = tf.constant(3)
+      f1 = lambda: tf.constant(17)
+      f2 = lambda: tf.constant(23)
+      f3 = lambda: tf.constant(-1)
+
+      r1 = tf.case({x < y: f1, x > z: f2}, default=f3, exclusive=True)
+      self.assertAllEqual(r1.eval(), 17)
+
+      r2 = tf.case([(y > z, f1), (y > x, f2)], default=f3)
+      self.assertAllEqual(r2.eval(), 23)
+
+      # Duplicate events can happen, first one is selected
+      r3 = tf.case([(x < y, f1), (x < y, f2)], default=f3)
+      self.assertAllEqual(r3.eval(), 17)
+
+      # Duplicate events cause an error if exclusive = True
+      r4 = tf.case([(x < y, f1), (x < y, f2)], default=f3, exclusive=True)
+      with self.assertRaisesOpError(
+          "More than one condition evaluated as True but exclusive=True."):
+        r4.eval()
+
+      # Check that the default is called if none of the others are
+      r5 = tf.case({x > y: f1}, default=f3)
+      self.assertAllEqual(r5.eval(), -1)
+
+      ran_once = [False, False, False]
+
+      def break_run_twice(ix):
+        def _break():
+          assert not ran_once[ix]
+          ran_once[ix] = True
+          return tf.constant(ix)
+        return _break
+
+      # Should not fail - each conditional gets called exactly once
+      r6 = tf.case([(x < y, break_run_twice(0)), (x > y, break_run_twice(1))],
+                   default=break_run_twice(2))
+
+      self.assertAllEqual(r6.eval(), 0)
+
   def testOneOpCond(self):
     with self.test_session():
       v = tf.Variable(0)
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 30918d84c9..112c1d9907 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -24,6 +24,7 @@ the execution of operations and add conditional dependencies to your graph.
 @@no_op
 @@count_up_to
 @@cond
+@@case
 
 ## Logical Operators
 
@@ -82,6 +83,7 @@ from tensorflow.python.ops import constant_op
 from tensorflow.python.ops import gen_array_ops
 from tensorflow.python.ops import gen_control_flow_ops
 from tensorflow.python.ops import gen_data_flow_ops
+from tensorflow.python.ops import logging_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import tensor_array_ops
 # pylint: disable=wildcard-import,undefined-variable
@@ -1974,6 +1976,9 @@ def case(pred_fn_pairs, default, exclusive=False, name="case"):
 
     Expressions:
     ```
+      x = tf.constant(0)
+      y = tf.constant(1)
+      z = tf.constant(2)
       def f1(): return tf.constant(17)
       def f2(): return tf.constant(23)
       def f3(): return tf.constant(-1)
@@ -2050,7 +2055,7 @@ def case(pred_fn_pairs, default, exclusive=False, name="case"):
     # and prev_case_seq will loop from case_sequence[0] to case_sequence[-1]
     if exclusive:
       # TODO(ebrevdo): Add Where() for DT_BOOL, replace with Size(Where(preds))
-      preds_c = array_ops.concat(0, preds, name="preds_c")
+      preds_c = array_ops.pack(preds, name="preds_c")
       num_true_conditions = math_ops.reduce_sum(
           math_ops.cast(preds_c, dtypes.int32), name="num_true_conds")
       at_most_one_true_condition = math_ops.less(
diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py
index 57ff78cdde..b8e6b26468 100644
--- a/tensorflow/python/ops/standard_ops.py
+++ b/tensorflow/python/ops/standard_ops.py
@@ -36,6 +36,7 @@ from tensorflow.python.ops.control_flow_ops import group
 from tensorflow.python.ops.control_flow_ops import no_op
 from tensorflow.python.ops.control_flow_ops import tuple
 from tensorflow.python.ops.control_flow_ops import cond
+from tensorflow.python.ops.control_flow_ops import case
 from tensorflow.python.ops.data_flow_ops import *
 from tensorflow.python.ops.gradients import *
 from tensorflow.python.ops.init_ops import *