Skip to content

Commit

Permalink
overview translation done
Browse files Browse the repository at this point in the history
  • Loading branch information
TerenceCooper authored and TerenceCooper committed Nov 19, 2015
1 parent 446a83d commit 6f4e2bb
Showing 1 changed file with 33 additions and 49 deletions.
82 changes: 33 additions & 49 deletions SOURCE/how_tos/overview.md
View file
Original file line number Diff line number Diff line change
@@ -1,94 +1,78 @@
# 综述 Overview


## Variables: 创建，初始化，保存，恢复 Variables: Creation, Initializing, Saving, and Restoring
## Variables: 创建，初始化，保存，和恢复

TensorFlow Variables 是内存中的容纳 tensor 的缓存。了解用它们在训练时(during training)保存和更新模型参数(model parameters) 的方法，[参看教程](../how_tos/variables/index.md)。
TensorFlow Variables are in-memory buffers containing tensors. Learn how to
use them to hold and update model parameters during training.
TensorFlow Variables 是内存中的容纳 tensor 的缓存。了解用它们在训练时(during training)保存和更新模型参数(model parameters) 的方法。

[View Tutorial](../how_tos/variables/index.md)
[参看教程](../how_tos/variables/index.md)


## TensorFlow 机制 101 TensorFlow Mechanics 101
## TensorFlow 机制 101

用 MNIST 手写数字识别作为一个小例子，一步一步的将使用 TensorFlow 基础架构(infrastructure)训练大规模模型的细节做详细介绍介绍，[参看教程](../tutorials/mnist/tf/index.md)。
A step-by-step walk through of the details of using TensorFlow infrastructure
to train models at scale, using MNIST handwritten digit recognition as a toy
example.
用 MNIST 手写数字识别作为一个小例子，一步一步的将使用 TensorFlow 基础架构(infrastructure)训练大规模模型的细节做详细介绍。

[View Tutorial](../tutorials/mnist/tf/index.md)
[参看教程](../tutorials/mnist/tf/index.md)


## TensorBoard: Visualizing Learning
## TensorBoard: 学习过程的视觉化

TensorBoard is a useful tool for visualizing the training and evaluation of
your model(s). This tutorial describes how to build and run TensorBoard as well
as how to add Summary ops to automatically output data to the Events files that
TensorBoard uses for display.
对模型进行训练和评估时，TensorBoard 是一个很有用的工具。此教程解释了创建和运行 TensorBoard 的方法， 和添加摘要操作(Summary ops)的方法，通过添加摘要操作(Summary ops)，可以自动把数据输出到 TensorBoard 在展示时所使用的事件文件。

[View Tutorial](../how_tos/summaries_and_tensorboard/index.md)
[参看教程](../how_tos/summaries_and_tensorboard/index.md)


## TensorBoard: Graph Visualization
## TensorBoard: 图的视觉化

This tutorial describes how to use the graph visualizer in TensorBoard to help
you understand the dataflow graph and debug it.
此教程介绍了在 TensorBoard 中使用图的视觉化工具的方法，它可以帮助你理解 the dataflow graph 并 debug。

[View Tutorial](../how_tos/graph_viz/index.md)
[参看教程](../how_tos/graph_viz/index.md)


## Reading Data
## 数据读入

This tutorial describes the three main methods of getting data into your
TensorFlow program: Feeding, Reading and Preloading.
此教程解释了把数据传入 TensorSlow 程序的三种主要的方法： Feeding, Reading 和 Preloading.

[View Tutorial](../how_tos/reading_data/index.md)
[参看教程](../how_tos/reading_data/index.md)


## Threading and Queues
## 线程和队列

This tutorial describes the various constructs implemented by TensorFlow
to facilitate asynchronous and concurrent training.
此教程解释了 TensorFlow 中为更容易的进行异步和并发训练而实现的各种不同的结构(constructs)。

[View Tutorial](../how_tos/threading_and_queues/index.md)
[参看教程](../how_tos/threading_and_queues/index.md)


## 添加新的 Op Adding a New Op
## 添加新的 Op

TensorFlow 已经提供一整套节点操作(operation)，你可以在你的 graph 中随意使用它们，不过这里有关于添加自定义操作(custom op)的细节，[参看教程](../how_tos/adding_an_op/index.md)。
TensorFlow already has a large suite of node operations from which you can
compose in your graph, but here are the details of how to add you own custom Op.
[View Tutorial](../how_tos/adding_an_op/index.md)
TensorFlow 已经提供一整套节点操作(operation)，你可以在你的 graph 中随意使用它们，不过这里有关于添加自定义操作(custom op)的细节。

[参看教程](../how_tos/adding_an_op/index.md)。

## 自定义数据的 Reader Custom Data Readers

如果你有相当大量的自定义数据集合，可能你想要对 TensorFlow 进行扩展，使它能直接以数据自身的格式将其读入，[参看教程](../how_tos/new_data_formats/index.md)。
If you have a sizable custom data set, you may want to consider extending
TensorFlow to read your data directly in it's native format. Here's how.
## 自定义数据的 Readers

[View Tutorial](../how_tos/new_data_formats/index.md)
如果你有相当大量的自定义数据集合，可能你想要对 TensorFlow 进行扩展，使它能直接以数据自身的格式将其读入。

[参看教程](../how_tos/new_data_formats/index.md)。

## 使用 GPU Using GPUs

此教程描述了 用 GPU 构建和运行模型的方法。
This tutorial describes how to construct and execute models on GPU(s).
## 使用 GPUs

[View Tutorial](../how_tos/using_gpu/index.md)
此教程描述了用 GPU 构建和运行模型的方法。

[参看教程](../how_tos/using_gpu/index.md)

## Sharing Variables

When deploying large models on multiple GPUs, or when unrolling complex LSTMs
or RNNs, it is often necessary to access the same Variable objects from
different locations in the model construction code.
## 共享变量 Sharing Variables

The "Variable Scope" mechanism is designed to facilitate that.
当在多 GPU 上部署大型的模型，或展开复杂的 LSTMs 或 RNNs 时，在模型构建代码的不同位置对许多相同的 Variable 对象(objects)进行读写常常是必须的。

[View Tutorial](../how_tos/variable_scope/index.md)
设计"Variable Scope" 机制的目的就是为了有助于上述任务的实现。

[参看教程](../how_tos/variable_scope/index.md)。

原文： [How-to](http://tensorflow.org/how_tos/index.html) 翻译：[Terence Cooper](https://github.com/TerenceCooper) 校对：
<div class='sections-order' style="display: none;">
<!--
<!-- variables/index.md -->
Expand Down

0 comments on commit 6f4e2bb

Please sign in to comment.