TensorFlow: Upstream latest changes to git.

Changes:
- Documentation updates.
- Specify numpy version in required packages.

Base CL: 107344010

tensorflow/g3doc/api_docs/python/state_ops.md

@@ -540,9 +540,9 @@ You number checkpoint filenames by passing a value to the optional
 `global_step` argument to `save()`:
 
 ```python
-saver.save('my-model', global_step=0) ==> filename: 'my-model-0'
+saver.save(sess, 'my-model', global_step=0) ==> filename: 'my-model-0'
 ...
-saver.save('my-model', global_step=1000) ==> filename: 'my-model-1000'
+saver.save(sess, 'my-model', global_step=1000) ==> filename: 'my-model-1000'
 ```
 
 Additionally, optional arguments to the `Saver()` constructor let you control
@@ -676,7 +676,7 @@ path can be passed directly to a call to `restore()`.
 ##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a>
 
 
-*  <b>sess</b>: A Session to use to save the variables..
+*  <b>sess</b>: A Session to use to save the variables.
 *  <b>save_path</b>: string.  Path to the checkpoint filename.  If the saver is
     `sharded`, this is the prefix of the sharded checkpoint filename.
 *  <b>global_step</b>: If provided the global step number is appended to

tensorflow/g3doc/get_started/basic_usage.md

@@ -286,7 +286,8 @@ with tf.Session() as sess:
 ```
 
 A `placeholder()` operation generates an error if you do not supply a feed for
-it. See the [MNIST fully-connected feed
-tutorial](../tutorials/mnist/fully_connected_feed.py) for a larger-scale
-example of feeds.
+it. See the
+[MNIST fully-connected feed tutorial](../tutorials/mnist/tf/index.md)
+([source code](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/g3doc/tutorials/mnist/fully_connected_feed.py))
+for a larger-scale example of feeds.
 

tensorflow/g3doc/get_started/os_setup.md

@@ -2,19 +2,21 @@
 
 ## Binary Installation <a class="md-anchor" id="AUTOGENERATED-binary-installation"></a>
 
+The TensorFlow Python API requires Python 2.7.
+
 ### Ubuntu/Linux <a class="md-anchor" id="AUTOGENERATED-ubuntu-linux"></a>
 
+**Note**: All the virtualenv-related instructions are optional, but we recommend
+using the virtualenv on any multi-user system.
+
 Make sure you have [pip](https://pypi.python.org/pypi/pip), the python headers,
 and (optionally) [virtualenv](https://pypi.python.org/pypi/virtualenv) installed:
 
 ```bash
 $ sudo apt-get install python-pip python-dev python-virtualenv
 ```
 
-**Note**: All the virtualenv-related instructions are optional, but we recommend
-using the virtualenv on any multi-user system.
-
-Set up a new virtualenv environment.  Assuming you want to set it up in the
+Set up a new virtualenv environment.  To set it up in the
 directory `~/tensorflow`, run:
 
 ```bash
@@ -39,18 +41,19 @@ Inside the virtualenv, install TensorFlow:
 # For GPU-enabled version (only install this version if you have the CUDA sdk installed)
 (tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
 
+# When you are done using TensorFlow:
 (tensorflow)$ deactivate  # Deactivate the virtualenv
 $  # Your prompt should change back
 ```
 
 ### Mac OS X <a class="md-anchor" id="AUTOGENERATED-mac-os-x"></a>
 
-Make sure you have [pip](https://pypi.python.org/pypi/pip) and
-(optionally) [virtualenv](https://pypi.python.org/pypi/virtualenv) installed:
-
 **Note**: All the virtualenv-related instructions are optional, but we recommend
 using the virtualenv on any multi-user system.
 
+Make sure you have [pip](https://pypi.python.org/pypi/pip) and
+(optionally) [virtualenv](https://pypi.python.org/pypi/virtualenv) installed:
+
 If using `easy_install`:
 
 ```bash
@@ -78,6 +81,8 @@ Install TensorFlow (only CPU binary version is currently available).
 
 ```bash
 (tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
+
+# When you are done using TensorFlow:
 (tensorflow)$ deactivate  # Deactivate the virtualenv
 $  # Your prompt should change back
 ```
@@ -184,7 +189,7 @@ Add the executable `output/bazel` to your `$PATH` environment variable.
 $ sudo apt-get install python-numpy swig python-dev
 ```
 
-#### <a name="install_cuda"></a>Optional: Install CUDA (GPUs on Linux) <a class="md-anchor" id="AUTOGENERATED--a-name--install_cuda----a-optional--install-cuda--gpus-on-linux-"></a>
+#### Optional: Install CUDA (GPUs on Linux) <a class="md-anchor" id="install_cuda"></a>
 
 In order to build or run TensorFlow with GPU support, both Cuda Toolkit 7.0 and
 CUDNN 6.5 V2 from NVIDIA need to be installed.

tensorflow/g3doc/how_tos/variables/index.md

@@ -1,26 +1,26 @@
 # Variables: Creation, Initialization, Saving, and Loading <a class="md-anchor" id="AUTOGENERATED-variables--creation--initialization--saving--and-loading"></a>
 
-When you train a model, you use [Variables](../../api_docs/python/state_ops.md)
+When you train a model, you use [variables](../../api_docs/python/state_ops.md)
 to hold and update parameters.  Variables are in-memory buffers containing
-tensors.  They need to be explicitly initialized and can be saved to disk during
+tensors.  They must be explicitly initialized and can be saved to disk during
 and after training. You can later restore saved values to exercise or analyse
 the model.
 
 This document references the following TensorFlow classes.  Follow the links to
 their reference manual for a complete description of their API:
 
-*  The `Variable` class [tf.Variable](../../api_docs/python/state_ops.md#Variable).
-*  The `Saver` class [tf.train.Saver](../../api_docs/python/state_ops.md#Saver).
+*  The [`tf.Variable`](../../api_docs/python/state_ops.md#Variable) class.
+*  The [`tf.train.Saver`](../../api_docs/python/state_ops.md#Saver) class.
 
 
 ## Creation <a class="md-anchor" id="AUTOGENERATED-creation"></a>
 
 When you create a [Variable](../../api_docs/python/state_ops.md) you pass a
 `Tensor` as its initial value to the `Variable()` constructor.  TensorFlow
-provides a collection of Ops that produce tensors often used for initialization
+provides a collection of ops that produce tensors often used for initialization
 from [constants or random values](../../api_docs/python/constant_op.md).
 
-Note that all these Ops require you to specify the shape of the tensors.  That
+Note that all these ops require you to specify the shape of the tensors.  That
 shape automatically becomes the shape of the variable.  Variables generally
 have a fixed shape, but TensorFlow provides advanced mechanisms to reshape
 variables.
@@ -32,28 +32,28 @@ weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
 biases = tf.Variable(tf.zeros([200]), name="biases")
 ```
 
-Calling `tf.Variable()` adds a few Ops to the graph:
+Calling `tf.Variable()` adds several ops to the graph:
 
-*  A `variable` Op that holds the variable value.
-*  An initializer Op that sets the variable to its initial value.  This is
-   actually a `tf.assign` Op.
-*  The Ops for the initial value, such as the `zeros` Op for the `biases`
+*  A `variable` op that holds the variable value.
+*  An initializer op that sets the variable to its initial value.  This is
+   actually a `tf.assign` op.
+*  The ops for the initial value, such as the `zeros` op for the `biases`
    variable in the example are also added to the graph.
 
 The value returned by `tf.Variable()` value is an instance of the Python class
 `tf.Variable`.
 
 ## Initialization <a class="md-anchor" id="AUTOGENERATED-initialization"></a>
 
-Variable initializers must be run explicitly before other Ops in your model can
-be run.  The easiest way to do that is to add an Op that runs all the variable
-initializers, and run that Op before using the model.
+Variable initializers must be run explicitly before other ops in your model can
+be run.  The easiest way to do that is to add an op that runs all the variable
+initializers, and run that op before using the model.
 
 You can alternatively restore variable values from a checkpoint file, see
 below.
 
-Use `tf.initialize_all_variables()` to add an Op to run variable initializers.
-Only run that Op after you have fully constructed your model and launched it in
+Use `tf.initialize_all_variables()` to add an op to run variable initializers.
+Only run that op after you have fully constructed your model and launched it in
 a session.
 
 ```python
@@ -62,13 +62,13 @@ weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
                       name="weights")
 biases = tf.Variable(tf.zeros([200]), name="biases")
 ...
-# Add an Op to initialize the variables.
+# Add an op to initialize the variables.
 init_op = tf.initialize_all_variables()
 
 # Later, when launching the model
 with tf.Session() as sess:
   # Run the init operation.
-  sess.Run(init_op)
+  sess.run(init_op)
   ...
   # Use the model
   ...
@@ -77,7 +77,7 @@ with tf.Session() as sess:
 ### Initialization from another Variable <a class="md-anchor" id="AUTOGENERATED-initialization-from-another-variable"></a>
 
 You sometimes need to initialize a variable from the initial value of another
-variable.  As the Op added by `tf.initialize_all_variables()` initializes all
+variable.  As the op added by `tf.initialize_all_variables()` initializes all
 variables in parallel you have to be careful when this is needed.
 
 To initialize a new variable from the value of another variable use the other
@@ -98,27 +98,29 @@ w_twice = tf.Variable(weights.initialized_value() * 0.2, name="w_twice")
 
 ### Custom Initialization <a class="md-anchor" id="AUTOGENERATED-custom-initialization"></a>
 
-The convenience function `tf.initialize_all_variables()` adds an Op to
+The convenience function `tf.initialize_all_variables()` adds an op to
 initialize *all variables* in the model.  You can also pass it an explicit list
 of variables to initialize.  See the
 [Variables Documentation](../../api_docs/python/state_ops.md) for more options,
 including checking if variables are initialized.
 
 ## Saving and Restoring <a class="md-anchor" id="AUTOGENERATED-saving-and-restoring"></a>
 
-The easiest way to save and restore a model is to use a `tf.train.Saver`
-object.  The constructor adds `save` and `restore` Ops to the graph for all, or
-a specified list, of variables.  The saver object provides methods to run these
-Ops, specifying paths for the checkpoint files to write to or read from.
+The easiest way to save and restore a model is to use a `tf.train.Saver` object.
+The constructor adds `save` and `restore` ops to the graph for all, or a
+specified list, of the variables in the graph.  The saver object provides
+methods to run these ops, specifying paths for the checkpoint files to write to
+or read from.
 
 ### Checkpoint Files <a class="md-anchor" id="AUTOGENERATED-checkpoint-files"></a>
 
-Variables are saved in binary files that, roughly, contains a map from variable
-names to tensors.
+Variables are saved in binary files that, roughly, contain a map from variable
+names to tensor values.
 
-When you create a `Saver` object, you can optionally chose names for the
-variables in the checkpoint files.  By default, it uses the names passed to the
-`tf.Variable()` call.
+When you create a `Saver` object, you can optionally choose names for the
+variables in the checkpoint files.  By default, it uses the value of the
+[`Variable.name`](../../api_docs/python/state_ops.md#Variable.name) property for
+each variable.
 
 ### Saving Variables <a class="md-anchor" id="AUTOGENERATED-saving-variables"></a>
 
@@ -130,20 +132,20 @@ the model.
 v1 = tf.Variable(..., name="v1")
 v2 = tf.Variable(..., name="v2")
 ...
-# Add an Op to initialize the variables.
+# Add an op to initialize the variables.
 init_op = tf.initialize_all_variables()
 
-# Add Ops to save and restore all the variables.
+# Add ops to save and restore all the variables.
 saver = tf.train.Saver()
 
 # Later, launch the model, initialize the variables, do some work, save the
 # variables to disk.
 with tf.Session() as sess:
-  sess.Run(init_op)
+  sess.run(init_op)
   # Do some work with the model.
   ..
   # Save the variables to disk.
-  save_path = saver.Save(sess, "/tmp/model.ckpt")
+  save_path = saver.save(sess, "/tmp/model.ckpt")
   print "Model saved in file: ", save_path
 ```
 
@@ -157,23 +159,23 @@ restore variables from a file you do not have to initialize them beforehand.
 v1 = tf.Variable(..., name="v1")
 v2 = tf.Variable(..., name="v2")
 ...
-# Add Ops to save and restore all the variables.
+# Add ops to save and restore all the variables.
 saver = tf.train.Saver()
 
 # Later, launch the model, use the saver to restore variables from disk, and
 # do some work with the model.
 with tf.Session() as sess:
   # Restore variables from disk.
-  saver.Restore(sess, "/tmp/model.ckpt")
+  saver.restore(sess, "/tmp/model.ckpt")
   print "Model restored."
   # Do some work with the model
   ...
 ```
 
-### Chosing which Variables to Save and Restore <a class="md-anchor" id="AUTOGENERATED-chosing-which-variables-to-save-and-restore"></a>
+### Choosing which Variables to Save and Restore <a class="md-anchor" id="AUTOGENERATED-choosing-which-variables-to-save-and-restore"></a>
 
-If you do not pass any argument to `tf.train.Saver()` the saver
-handles all variables.  Each one of them is saved under the name that was
+If you do not pass any argument to `tf.train.Saver()` the saver handles all
+variables in the graph.  Each one of them is saved under the name that was
 passed when the variable was created.
 
 It is sometimes useful to explicitly specify names for variables in the
@@ -196,10 +198,10 @@ Notes:
 *  You can create as many saver objects as you want if you need to save and
    restore different subsets of the model variables.  The same variable can be
    listed in multiple saver objects, its value is only changed when the saver
-   `Restore()` method is run.
+   `restore()` method is run.
 
 *  If you only restore a subset of the model variables at the start
-   of a session, you have to run an initialize Op for the other variables.  See
+   of a session, you have to run an initialize op for the other variables.  See
    [`tf.initialize_variables()`](../../api_docs/python/state_ops.md#initialize_variables)
    for more information.
 
@@ -208,7 +210,7 @@ Notes:
 v1 = tf.Variable(..., name="v1")
 v2 = tf.Variable(..., name="v2")
 ...
-# Add Ops to save and restore only 'v2' using the name "my_v2"
+# Add ops to save and restore only 'v2' using the name "my_v2"
 saver = tf.train.Saver({"my_v2": v2})
 # Use the saver object normally after that.
 ...

tensorflow/g3doc/tutorials/mandelbrot/index.md

@@ -6,18 +6,18 @@ general mathematics.  This is actually a pretty naive implementation of the
 visualization, but it makes the point.  (We may end up providing a more
 elaborate implementation down the line to produce more truly beautiful images.)
 
-Note: This tutorial was originally prepared as an iPython notebook.
+Note: This tutorial was originally prepared as an IPython notebook.
 
 ## Basic Setup <a class="md-anchor" id="AUTOGENERATED-basic-setup"></a>
 
 We'll need a few imports to get started.
 
 ```python
-#Import libraries for simulation
+# Import libraries for simulation
 import tensorflow as tf
 import numpy as np
 
-#Imports for visualization
+# Imports for visualization
 import PIL.Image
 from cStringIO import StringIO
 from IPython.display import clear_output, Image, display
@@ -45,7 +45,7 @@ def DisplayFractal(a, fmt='jpeg'):
 
 ## Session and Variable Initialization <a class="md-anchor" id="AUTOGENERATED-session-and-variable-initialization"></a>
 
-For playing around like this, we often us an interactive session, but a regular
+For playing around like this, we often use an interactive session, but a regular
 session would work as well.
 
 ```python
@@ -61,7 +61,7 @@ Y, X = np.mgrid[-1.3:1.3:0.005, -2:1:0.005]
 Z = X+1j*Y
 ```
 
-Now we define and initialize.
+Now we define and initialize TensorFlow tensors.
 
 ```python
 xs = tf.constant(Z.astype("complex64"))
@@ -72,7 +72,7 @@ ns = tf.Variable(tf.zeros_like(xs, "float32"))
 TensorFlow requires that you explicitly initialize variables before using them.
 
 ```python
-tf.InitializeAllVariables().run()
+tf.initialize_all_variables().run()
 ```
 
 ## Defining and Running the Computation <a class="md-anchor" id="AUTOGENERATED-defining-and-running-the-computation"></a>

tensorflow/g3doc/tutorials/mnist/beginners/index.md

@@ -34,10 +34,11 @@ work through the code.
 ## The MNIST Data <a class="md-anchor" id="AUTOGENERATED-the-mnist-data"></a>
 
 The MNIST data is hosted on
-[Yann LeCun's website](http://yann.lecun.com/exdb/mnist/).
-For your convenience, we've included some python code to download and install
-the data automatically. You can either download [the code](../input_data.py) and
-import it as below, or simply copy and paste it in.
+[Yann LeCun's website](http://yann.lecun.com/exdb/mnist/).  For your
+convenience, we've included some python code to download and install the data
+automatically. You can either download
+[the code](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/g3doc/tutorials/mnist/input_data.py)
+and import it as below, or simply copy and paste it in.
 
 ```python
 import input_data