Merge pull request jikexueyuanwiki#1 from jikexueyuanwiki/master

Sync fork with origin

README.md

@@ -40,6 +40,7 @@ PS: 想探讨TensorFlow技术的可以加"TensorFlow技术交流群"：495115006
 * fork主仓库（<https://github.com/jikexueyuanwiki/tensorflow-zh>）
 * 按照章节认领翻译(每次申请一个章节)，在下面这个`README.md`里找还没有被人申请的章节，写上（@你的github号），给主仓库的`master`分支提pull request；
 * 提的 pull request 被确认，合并到主仓库后，代表你申请的章节*认领*完成，开始翻译；
+* 翻译的文件为README.md或者TOC.md中对应的md文件，请不要翻译单独文件夹中的index.md
 * 翻译过程请参 *翻译协作规范* ，完成翻译后提交 pull request 给主仓库的`master`分支；
 * 校核完成后，从主仓库的`master`分支合并到主`publish`分支；
 * 全部翻译完成后，生成PDF/ePub文档，放在极客学院Wiki平台发布；
@@ -61,83 +62,47 @@ PS: 想探讨TensorFlow技术的可以加"TensorFlow技术交流群"：495115006
 
 ## 参与者（按认领章节排序）
 
-### 翻译
+### 翻译 & 校对
 
 - 起步
-  - [介绍](SOURCE/get_started/introduction.md) （[此处添加翻译者姓名及链接](https://github.com/xxx)）（[@PFZheng](https://github.com/PFZheng)）
-  - [下载及安装](SOURCE/get_started/os_setup.md) （[@PFZheng](https://github.com/PFZheng)）
-  - [基本用法](SOURCE/get_started/basic_usage.md) （[@PFZheng](https://github.com/PFZheng)）
+  - [介绍](SOURCE/get_started/introduction.md) 翻译：（[@PFZheng](https://github.com/PFZheng)）校对：（[@yangtze](https://github.com/sstruct)）
+  - [下载及安装](SOURCE/get_started/os_setup.md) 翻译：（[@PFZheng](https://github.com/PFZheng)）校对： （[@yangtze](https://github.com/sstruct)）
+  - [基本用法](SOURCE/get_started/basic_usage.md) 翻译：（[@PFZheng](https://github.com/PFZheng)）校对：（[@yangtze](https://github.com/sstruct)）
 - 教程
-  - [Overview](SOURCE/tutorials/overview.md) （[@PFZheng](https://github.com/PFZheng)）
-  - [MNIST For ML Beginners](SOURCE/tutorials/mnist_beginners.md) ([@Tony Jin](https://github.com/linbojin))
-  - [Deep MNIST for Expert](SOURCE/tutorials/mnist_pros.md)
-  - [TensorFlow Mechanics 101](SOURCE/tutorials/mnist_tf.md)
-  - [Convolutional Neural Networks](SOURCE/tutorials/deep_cnn.md)
-  - [Vector Representations of Words](SOURCE/tutorials/word2vec.md)
-  - [Vector Representations of Words](SOURCE/tutorials/word2vec.md)
-  - [Recurrent Neural Networks](SOURCE/tutorials/recurrent.md)(@[Warln](https://github.com/Warln))
-  - [Mandelbrot Set](SOURCE/tutorials/mandelbrot.md)
+  - [Overview](SOURCE/tutorials/overview.md) 翻译：（[@PFZheng](https://github.com/PFZheng)）校对： ([@ericxk](https://github.com/ericxk))
+  - [MNIST For ML Beginners](SOURCE/tutorials/mnist_beginners.md) 翻译：([@Tony Jin](https://github.com/linbojin)) 校对： ([@ericxk](https://github.com/ericxk))
+  - [Deep MNIST for Expert](SOURCE/tutorials/mnist_pros.md) 翻译：([@chenweican](https://github.com/chenweican))
+  - [TensorFlow Mechanics 101](SOURCE/tutorials/mnist_tf.md) 翻译：([@bingjin](https://github.com/bingjin))
+  - [Convolutional Neural Networks](SOURCE/tutorials/deep_cnn.md) 翻译： ([@oskycar](https://github.com/oskycar)) 校对： ([@zhyhooo](https://github.com/zhyhooo))
+  - [Vector Representations of Words](SOURCE/tutorials/word2vec.md)翻译：（[@xyang40](https://github.com/xyang40)）
+  - [Recurrent Neural Networks](SOURCE/tutorials/recurrent.md) 翻译：([@Warln](https://github.com/Warln))
+  - [Mandelbrot集合](SOURCE/tutorials/mandelbrot.md) 翻译：([@ericxk](https://github.com/ericxk))√
   - [Partial Differential Equations](SOURCE/tutorials/pdes.md) 
-  - [MNIST Data Download](SOURCE/tutorials/mnist_download.md)
+  - [MNIST Data Download](SOURCE/tutorials/mnist_download.md) 翻译： ([@JoyLiu](https://github.com/fengsehng))
 - 运作方式
   - [总览](SOURCE/how_tos/overview.md) 
-  - [变量:创建、初始化、保存和加载](SOURCE/how_tos/variables.md) 
-  - [TensorBoard:可视化学习](SOURCE/how_tos/summaries_and_tensorboard.md) 
+  - [变量:创建、初始化、保存和加载](SOURCE/how_tos/variables.md) 翻译： ([@zhyhooo](https://github.com/zhyhooo))
+  - [TensorBoard:可视化学习](SOURCE/how_tos/summaries_and_tensorboard.md) 翻译：([@thylaco1eo](https://github.com/thylaco1eo))
   - [TensorBoard:图表可视化](SOURCE/how_tos/graph_viz.md) 
   - [读取数据](SOURCE/how_tos/reading_data.md) 
   - [线程和队列](SOURCE/how_tos/threading_and_queues.md) 
   - [添加新的Op](SOURCE/how_tos/adding_an_op.md) 
   - [自定义数据读取](SOURCE/how_tos/new_data_formats.md) 
-  - [使用gpu](SOURCE/how_tos/using_gpu.md) 
+  - [使用gpu](SOURCE/how_tos/using_gpu.md)翻译：([@lianghyv](https://github.com/lianghyv))√ 
   - [共享变量](SOURCE/how_tos/variable_scope.md) 
 - 资源
   - [总览](SOURCE/resources/overview.md) 
   - [BibTex 引用](SOURCE/resources/bib.md) 
-  - [示例使用](SOURCE/resources/uses.md)
+  - [示例使用](SOURCE/resources/uses.md) 翻译：([@andyiac](https://github.com/andyiac))
   - [FAQ](SOURCE/resources/faq.md)
   - [术语表](SOURCE/resources/glossary.md)
-  - [Tensor排名、形状和类型](SOURCE/resources/dim_types.md)
+  - [Tensor排名、形状和类型](SOURCE/resources/dims_types.md)
 
-### 校对
-
-- 起步
-  - [介绍](SOURCE/get_started/introduction.md) （[此处添加审校者姓名及链接](https://github.com/xxx)）
-  - [下载及安装](SOURCE/get_started/os_setup.md)
-  - [基本用法](SOURCE/get_started/basic_usage.md)
-- 教程
-  - [Overview](SOURCE/tutorials/overview.md)
-  - [MNIST For ML Beginners](SOURCE/tutorials/mnist_beginners.md)
-  - [Deep MNIST for Expert](SOURCE/tutorials/mnist_pros.md)
-  - [TensorFlow Mechanics 101](SOURCE/tutorials/mnist_tf.md)
-  - [Convolutional Neural Networks](SOURCE/tutorials/deep_cnn.md)
-  - [Vector Representations of Words](SOURCE/tutorials/word2vec.md)
-  - [Vector Representations of Words](SOURCE/tutorials/word2vec.md)
-  - [Recurrent Neural Networks](SOURCE/tutorials/recurrent.md)
-  - [Mandelbrot Set](SOURCE/tutorials/mandelbrot.md)
-  - [Partial Differential Equations](SOURCE/tutorials/pdes.md) 
-  - [MNIST Data Download](SOURCE/tutorials/mnist_download.md)
-- 运作方式
-  - [总览](SOURCE/how_tos/overview.md) 
-  - [变量:创建、初始化、保存和加载](SOURCE/how_tos/variables.md) 
-  - [TensorBoard:可视化学习](SOURCE/how_tos/summaries_and_tensorboard.md) 
-  - [TensorBoard:图表可视化](SOURCE/how_tos/graph_viz.md) 
-  - [读取数据](SOURCE/how_tos/reading_data.md) 
-  - [线程和队列](SOURCE/how_tos/threading_and_queues.md) 
-  - [添加新的Op](SOURCE/how_tos/adding_an_op.md) 
-  - [自定义数据读取](SOURCE/how_tos/new_data_formats.md) 
-  - [使用gpu](SOURCE/how_tos/using_gpu.md) 
-  - [共享变量](SOURCE/how_tos/variable_scope.md) 
-- 资源
-  - [总览](SOURCE/resources/overview.md) 
-  - [BibTex 引用](SOURCE/resources/bib.md) 
-  - [示例使用](SOURCE/resources/uses.md)
-  - [FAQ](SOURCE/resources/faq.md)
-  - [术语表](SOURCE/resources/glossary.md)
-  - [Tensor排名、形状和类型](SOURCE/resources/dim_types.md)
 
 ## 进度记录
 
-- 2015-11-10, 谷歌发布全新人工智能系统TensorFlow并宣布开源, 极客学院Wiki启动协同翻译，创建 GitHub 仓库，制定协同规范  
+- 2015-11-10, 谷歌发布全新人工智能系统TensorFlow并宣布开源, 极客学院Wiki启动协同翻译，创建 GitHub 仓库，制定协同规范 
+
 
 
 ## 感谢支持   

SOURCE/how_tos/using_gpu.md

@@ -1,38 +1,33 @@
-# Using GPUs <a class="md-anchor" id="AUTOGENERATED-using-gpus"></a>
+# 使用GPUs <a class="md-anchor" id="AUTOGENERATED-using-gpus"></a>
 
-## Supported devices <a class="md-anchor" id="AUTOGENERATED-supported-devices"></a>
+## 支持的设备 <a class="md-anchor" id="AUTOGENERATED-supported-devices"></a>
 
-On a typical system, there are multiple computing devices. In TensorFlow, the
-supported device types are `CPU` and `GPU`.  They are represented as
-`strings`. For example:
+在一套标准的系统上通常有多个计算设备.TensorFlow中支持CPU和GPU两种.我们用指定字符串
+`strings`来标识这些设备.比如:
 
-*  `"/cpu:0"`: The CPU of your machine.
-*  `"/gpu:0"`: The GPU of your machine, if you have one.
-*  `"/gpu:1"`: The second GPU of your machine, etc.
+*  `"/cpu:0"`:机器的CPU
+*  `"/gpu:0"`:机器的GPU,如果你有一个的话.
+*  `"/gpu:1"`:机器的第二个GPU,以此类推...
 
-If a TensorFlow operation has both CPU and GPU implementations, the
-GPU devices will be given priority when the operation is assigned to
-a device. For example, `matmul` has both CPU and GPU kernels.  On a
-system with devices `cpu:0` and `gpu:0`, `gpu:0` will be selected to run
-`matmul`.
+如果一个TensorFlow系统中兼有CPU和GPU实现,当你指派设备时GPU有优先权.比如`matmul`中CPU
+和GPU核心都有.那么在`cpu:0`和`gpu:0`中,`gpu:0`会被选择运行`matmul`.
 
-## Logging Device placement <a class="md-anchor" id="AUTOGENERATED-logging-device-placement"></a>
+## 记录使用设备的位置 <a class="md-anchor" id="AUTOGENERATED-logging-device-placement"></a>
 
-To find out which devices your operations and tensors are assigned to, create
-the session with `log_device_placement` configuration option set to `True`.
+为了获取你的operations和Tensor被指派到哪个设备上运行，用`log_device_placement`新建一个`session`,并设置为`True`.
 
 ```python
-# Creates a graph.
+# 新建一个graph.
 a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
 b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
 c = tf.matmul(a, b)
-# Creates a session with log_device_placement set to True.
+# 新建session with log_device_placement并设置为True.
 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
-# Runs the op.
+# 运行这个op.
 print sess.run(c)
 ```
 
-You should see the following output:
+你应该能看见以下输出:
 
 ```
 Device mapping:
@@ -46,26 +41,24 @@ MatMul: /job:localhost/replica:0/task:0/gpu:0
 
 ```
 
-## Manual device placement <a class="md-anchor" id="AUTOGENERATED-manual-device-placement"></a>
+## 手工指定使用的设备 <a class="md-anchor" id="AUTOGENERATED-manual-device-placement"></a>
 
-If you would like a particular operation to run on a device of your
-choice instead of what's automatically selected for you, you can use
-`with tf.device` to create a device context such that all the operations
-within that context will have the same device assignment.
+如果你想让指定设备运行operation而不用系统自动为你分配的，你可以用`with tf.device`
+创建一个设备环境，这个环境下的操作都统一运行在环境指定的设备上.
 
 ```python
-# Creates a graph.
+# 新建一个graph.
 with tf.device('/cpu:0'):
   a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
   b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
 c = tf.matmul(a, b)
-# Creates a session with log_device_placement set to True.
+# 新建session with log_device_placement并设置为True.
 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
-# Runs the op.
+# 运行这个op.
 print sess.run(c)
 ```
 
-You will see that now `a` and `b` are assigned to `cpu:0`.
+你会发现现在`a`和`b`操作都被分配给了`cpu:0`.
 
 ```
 Device mapping:
@@ -78,26 +71,24 @@ MatMul: /job:localhost/replica:0/task:0/gpu:0
  [ 49.  64.]]
 ```
 
-## Using a single GPU on a multi-GPU system <a class="md-anchor" id="AUTOGENERATED-using-a-single-gpu-on-a-multi-gpu-system"></a>
+## 在多GPU系统里使用单一GPU<a class="md-anchor" id="AUTOGENERATED-using-a-single-gpu-on-a-multi-gpu-system"></a>
 
-If you have more than one GPU in your system, the GPU with the lowest ID will be
-selected by default. If you would like to run on a different GPU, you will need
-to specify the preference explicitly:
+如果你的系统里有多个GPU，ID最小的GPU会被默认选中.如果你想用别的GPU，
+可以这样指明你的偏好：
 
 ```python
-# Creates a graph.
+# 新建一个graph.
 with tf.device('/gpu:2'):
   a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
   b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
   c = tf.matmul(a, b)
-# Creates a session with log_device_placement set to True.
+# 新建session with log_device_placement并设置为True.
 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
-# Runs the op.
+# 运行这个op.
 print sess.run(c)
 ```
 
-If the device you have specified does not exist, you will get
-`InvalidArgumentError`:
+如果你指定的设备不存在，你会收到`InvalidArgumentError`提示：
 
 ```
 InvalidArgumentError: Invalid argument: Cannot assign a device to node 'b':
@@ -106,33 +97,29 @@ Could not satisfy explicit device specification '/gpu:2'
    values: 1 2 3...>, _device="/gpu:2"]()]]
 ```
 
-If you would like TensorFlow to automatically choose an existing and
-supported device to run the operations in case the specified one doesn't
-exist, you can set `allow_soft_placement` to `True` in the configuration
-option when creating the session.
+如果你希望TensorFlow自动选择一个存在的且被支持的设备以防你指定的设备不存在，
+你可以在创建的`session`里把参数`allow_soft_placement`设置为`True`
 
 ```python
-# Creates a graph.
+# 新建一个graph.
 with tf.device('/gpu:2'):
   a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
   b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
   c = tf.matmul(a, b)
-# Creates a session with allow_soft_placement and log_device_placement set
-# to True.
+# 新建session with log_device_placement并设置为True.
 sess = tf.Session(config=tf.ConfigProto(
       allow_soft_placement=True, log_device_placement=True))
-# Runs the op.
+# 运行这个op.
 print sess.run(c)
 ```
 
-## Using multiple GPUs <a class="md-anchor" id="AUTOGENERATED-using-multiple-gpus"></a>
+## 使用多个GPU <a class="md-anchor" id="AUTOGENERATED-using-multiple-gpus"></a>
 
-If you would like to run TensorFlow on multiple GPUs, you can construct your
-model in a multi-tower fashion where each tower is assigned to a different GPU.
-For example:
+如果你想让TensorFlow在多个GPU上运行，你可以建立multi-tower结构，在这个结构
+里每个tower分别被指配给不同的GPU运行.比如：
 
 ```
-# Creates a graph.
+# 新建一个graph.
 c = []
 for d in ['/gpu:2', '/gpu:3']:
   with tf.device(d):
@@ -141,13 +128,13 @@ for d in ['/gpu:2', '/gpu:3']:
     c.append(tf.matmul(a, b))
 with tf.device('/cpu:0'):
   sum = tf.add_n(c)
-# Creates a session with log_device_placement set to True.
+# 新建session with log_device_placement并设置为True.
 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
-# Runs the op.
+# 运行这个op.
 print sess.run(sum)
 ```
 
-You will see the following output.
+你会看到如下输出：
 
 ```
 Device mapping:
@@ -170,5 +157,7 @@ AddN: /job:localhost/replica:0/task:0/cpu:0
  [  98.  128.]]
 ```
 
-The [cifar10 tutorial](../../tutorials/deep_cnn/index.md) is a good example
-demonstrating how to do training with multiple GPUs.
+[cifar10 tutorial](../../tutorials/deep_cnn/index.md) 这个例子很好的演示了怎样用GPU集群训练.
+
+> 原文:[using_gpu](http://tensorflow.org/how_tos/using_gpu/index.md)
+翻译:[@lianghyv](https://github.com/lianghyv) 校对:[](https://github.com/)