Update generated Python Op docs.

Change: 120988643

Author: A. Unique TensorFlower

tensorflow/g3doc/api_docs/python/contrib.layers.md

@@ -109,7 +109,7 @@ The variable creation is compatible with `tf.variable_scope` and so can be
 reused with `tf.variable_scope` or `tf.make_template`.
 
 Most of the details of variable creation can be controlled by specifying the
-initializers (`weight_init` and `bias_init`) and which in collections to place
+initializers (`weight_init` and `bias_init`) and in which collections to place
 the created variables (`weight_collections` and `bias_collections`; note that
 the variables are always added to the `VARIABLES` collection). The output of
 the layer can be placed in custom collections using `output_collections`.

tensorflow/g3doc/api_docs/python/control_flow_ops.md

@@ -744,6 +744,14 @@ Asserts that the given condition is true.
 If `condition` evaluates to false, print the list of tensors in `data`.
 `summarize` determines how many entries of the tensors to print.
 
+NOTE: To ensure that Assert executes, one usually attaches a dependency:
+
+```python
+ # Ensure maximum element of x is smaller or equal to 1
+assert_op = tf.Assert(tf.less_equal(tf.reduce_max(x), 1.), [x])
+x = tf.with_dependencies([assert_op], x)
+```
+
 ##### Args:
 
 
@@ -752,6 +760,12 @@ If `condition` evaluates to false, print the list of tensors in `data`.
 *  <b>`summarize`</b>: Print this many entries of each tensor.
 *  <b>`name`</b>: A name for this operation (optional).
 
+##### Returns:
+
+
+*  <b>`assert_op`</b>: An `Operation` that, when executed, raises a
+  `tf.errors.InvalidArgumentError` if `condition` is not true.
+
 
 - - -
 

tensorflow/g3doc/api_docs/python/framework.md

@@ -719,6 +719,9 @@ a collection several times. This function makes sure that duplicates in
 `names` are ignored, but it will not check for pre-existing membership of
 `value` in any of the collections in `names`.
 
+`names` can be any iterable, but if `names` is a string, it is treated as a
+single collection name.
+
 ##### Args:
 
 
@@ -2498,6 +2501,157 @@ registered with a smaller priority than `G`.
 
 
 ## Other Functions and Classes
+- - -
+
+### `class tf.DeviceSpec` {#DeviceSpec}
+
+Represents a (possibly partial) specification for a TensorFlow device.
+
+`DeviceSpec`s are used throughout TensorFlow to describe where state is stored
+and computations occur. Using `DeviceSpec` allows you to parse device spec
+strings to verify their validity, merge them or compose them programmatically.
+
+Example:
+```python
+# Place the operations on device "GPU:0" in the "ps" job.
+device_spec = DeviceSpec(job="ps", device_type="GPU", device_index=0)
+with tf.device(device_spec):
+  # Both my_var and squared_var will be placed on /job:ps/device:GPU:0.
+  my_var = tf.Variable(..., name="my_variable")
+  squared_var = tf.square(my_var)
+```
+
+If a `DeviceSpec` is partially specified, it will be merged with other
+`DeviceSpec`s according to the scope in which it is defined. `DeviceSpec`
+components defined in inner scopes take precedence over those defined in
+outer scopes.
+
+```python
+with tf.device(DeviceSpec(job="train", )):
+  with tf.device(DeviceSpec(job="ps", device_type="GPU", device_index=0):
+    # Nodes created here will be assigned to /job:ps/device:GPU:0.
+  with tf.device(DeviceSpec(device_type="GPU", device_index=1):
+    # Nodes created here will be assigned to /job:train/device:GPU:1.
+```
+
+A `DeviceSpec` consists of 5 components -- each of
+which is optionally specified:
+
+* Job: The job name.
+* Replica: The replica index.
+* Task: The task index.
+* Device type: The device type string (e.g. "CPU" or "GPU").
+* Device index: The device index.
+- - -
+
+#### `tf.DeviceSpec.__init__(job=None, replica=None, task=None, device_type=None, device_index=None)` {#DeviceSpec.__init__}
+
+Create a new `DeviceSpec` object.
+
+##### Args:
+
+
+*  <b>`job`</b>: string.  Optional job name.
+*  <b>`replica`</b>: int.  Optional replica index.
+*  <b>`task`</b>: int.  Optional task index.
+*  <b>`device_type`</b>: Optional device type string (e.g. "CPU" or "GPU")
+*  <b>`device_index`</b>: int.  Optional device index.  If left
+    unspecified, device represents 'any' device_index.
+
+
+- - -
+
+#### `tf.DeviceSpec.from_string(spec)` {#DeviceSpec.from_string}
+
+Construct a `DeviceSpec` from a string.
+
+##### Args:
+
+
+*  <b>`spec`</b>: a string of the form
+   /job:<name>/replica:<id>/task:<id>/device:CPU:<id>
+  or
+   /job:<name>/replica:<id>/task:<id>/device:GPU:<id>
+  as cpu and gpu are mutually exclusive.
+  All entries are optional.
+
+##### Returns:
+
+  A DeviceSpec.
+
+
+- - -
+
+#### `tf.DeviceSpec.job` {#DeviceSpec.job}
+
+
+
+
+- - -
+
+#### `tf.DeviceSpec.merge_from(dev)` {#DeviceSpec.merge_from}
+
+Merge the properties of "dev" into this `DeviceSpec`.
+
+##### Args:
+
+
+*  <b>`dev`</b>: a `DeviceSpec`.
+
+
+- - -
+
+#### `tf.DeviceSpec.parse_from_string(spec)` {#DeviceSpec.parse_from_string}
+
+Parse a `DeviceSpec` name into its components.
+
+##### Args:
+
+
+*  <b>`spec`</b>: a string of the form
+   /job:<name>/replica:<id>/task:<id>/device:CPU:<id>
+  or
+   /job:<name>/replica:<id>/task:<id>/device:GPU:<id>
+  as cpu and gpu are mutually exclusive.
+  All entries are optional.
+
+##### Returns:
+
+  The `DeviceSpec`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the spec was not valid.
+
+
+- - -
+
+#### `tf.DeviceSpec.replica` {#DeviceSpec.replica}
+
+
+
+
+- - -
+
+#### `tf.DeviceSpec.task` {#DeviceSpec.task}
+
+
+
+
+- - -
+
+#### `tf.DeviceSpec.to_string()` {#DeviceSpec.to_string}
+
+Return a string representation of this `DeviceSpec`.
+
+##### Returns:
+
+  a string of the form
+  /job:<name>/replica:<id>/task:<id>/device:<device_type>:<id>.
+
+
+
 - - -
 
 ### `class tf.bytes` {#bytes}

tensorflow/g3doc/api_docs/python/index.md

@@ -10,6 +10,7 @@
   * [`convert_to_tensor`](../../api_docs/python/framework.md#convert_to_tensor)
   * [`convert_to_tensor_or_indexed_slices`](../../api_docs/python/framework.md#convert_to_tensor_or_indexed_slices)
   * [`device`](../../api_docs/python/framework.md#device)
+  * [`DeviceSpec`](../../api_docs/python/framework.md#DeviceSpec)
   * [`Dimension`](../../api_docs/python/framework.md#Dimension)
   * [`DType`](../../api_docs/python/framework.md#DType)
   * [`get_collection`](../../api_docs/python/framework.md#get_collection)
@@ -70,9 +71,11 @@
   * [`constant_initializer`](../../api_docs/python/state_ops.md#constant_initializer)
   * [`count_up_to`](../../api_docs/python/state_ops.md#count_up_to)
   * [`device`](../../api_docs/python/state_ops.md#device)
+  * [`export_meta_graph`](../../api_docs/python/state_ops.md#export_meta_graph)
   * [`get_checkpoint_state`](../../api_docs/python/state_ops.md#get_checkpoint_state)
   * [`get_variable`](../../api_docs/python/state_ops.md#get_variable)
   * [`get_variable_scope`](../../api_docs/python/state_ops.md#get_variable_scope)
+  * [`import_meta_graph`](../../api_docs/python/state_ops.md#import_meta_graph)
   * [`IndexedSlices`](../../api_docs/python/state_ops.md#IndexedSlices)
   * [`initialize_all_variables`](../../api_docs/python/state_ops.md#initialize_all_variables)
   * [`initialize_local_variables`](../../api_docs/python/state_ops.md#initialize_local_variables)
@@ -454,6 +457,7 @@
   * [`AdamOptimizer`](../../api_docs/python/train.md#AdamOptimizer)
   * [`add_queue_runner`](../../api_docs/python/train.md#add_queue_runner)
   * [`AggregationMethod`](../../api_docs/python/train.md#AggregationMethod)
+  * [`audio_summary`](../../api_docs/python/train.md#audio_summary)
   * [`clip_by_average_norm`](../../api_docs/python/train.md#clip_by_average_norm)
   * [`clip_by_global_norm`](../../api_docs/python/train.md#clip_by_global_norm)
   * [`clip_by_norm`](../../api_docs/python/train.md#clip_by_norm)
@@ -462,7 +466,6 @@
   * [`Coordinator`](../../api_docs/python/train.md#Coordinator)
   * [`exponential_decay`](../../api_docs/python/train.md#exponential_decay)
   * [`ExponentialMovingAverage`](../../api_docs/python/train.md#ExponentialMovingAverage)
-  * [`export_meta_graph`](../../api_docs/python/train.md#export_meta_graph)
   * [`FtrlOptimizer`](../../api_docs/python/train.md#FtrlOptimizer)
   * [`generate_checkpoint_state_proto`](../../api_docs/python/train.md#generate_checkpoint_state_proto)
   * [`global_norm`](../../api_docs/python/train.md#global_norm)
@@ -471,7 +474,6 @@
   * [`gradients`](../../api_docs/python/train.md#gradients)
   * [`histogram_summary`](../../api_docs/python/train.md#histogram_summary)
   * [`image_summary`](../../api_docs/python/train.md#image_summary)
-  * [`import_meta_graph`](../../api_docs/python/train.md#import_meta_graph)
   * [`LooperThread`](../../api_docs/python/train.md#LooperThread)
   * [`merge_all_summaries`](../../api_docs/python/train.md#merge_all_summaries)
   * [`merge_summary`](../../api_docs/python/train.md#merge_summary)

tensorflow/g3doc/api_docs/python/state_ops.md

@@ -1988,3 +1988,102 @@ The `Graph` that contains the values, indices, and shape tensors.
 
 
 
+
+
+## Exporting and Importing Meta Graphs
+
+- - -
+
+### `tf.train.export_meta_graph(filename=None, meta_info_def=None, graph_def=None, saver_def=None, collection_list=None, as_text=False)` {#export_meta_graph}
+
+Returns `MetaGraphDef` proto. Optionally writes it to filename.
+
+This function exports the graph, saver, and collection objects into
+`MetaGraphDef` protocol buffer with the intension of it being imported
+at a later time or location to restart training, run inference, or be
+a subgraph.
+
+##### Args:
+
+
+*  <b>`filename`</b>: Optional filename including the path for writing the
+    generated `MetaGraphDef` protocol buffer.
+*  <b>`meta_info_def`</b>: `MetaInfoDef` protocol buffer.
+*  <b>`graph_def`</b>: `GraphDef` protocol buffer.
+*  <b>`saver_def`</b>: `SaverDef` protocol buffer.
+*  <b>`collection_list`</b>: List of string keys to collect.
+*  <b>`as_text`</b>: If `True`, writes the `MetaGraphDef` as an ASCII proto.
+
+##### Returns:
+
+  A `MetaGraphDef` proto.
+
+
+- - -
+
+### `tf.train.import_meta_graph(meta_graph_or_file)` {#import_meta_graph}
+
+Recreates a Graph saved in a `MetaGraphDef` proto.
+
+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
+
+* Serialize a graph along with other Python objects such as `QueueRunner`,
+  `Variable` into a `MetaGraphDef`.
+
+* Restart training from a saved graph and checkpoints.
+
+* 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() returns 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:
+
+
+*  <b>`meta_graph_or_file`</b>: `MetaGraphDef` protocol buffer or filename (including
+    the path) containing a `MetaGraphDef`.
+
+##### Returns:
+
+  A saver constructed from `saver_def` in `MetaGraphDef` or None.
+
+  A None value is returned if no variables exist in the `MetaGraphDef`
+  (i.e., there are no variables to restore).
+
+