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# Glossary <a class="md-anchor" id="AUTOGENERATED-glossary"></a>

**Broadcasting operation**

An operation that uses [numpy-style broadcasting](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
to make the shapes of its tensor arguments compatible.

**Device**

A piece of hardware that can run computation and has its own address space,
like a GPU or CPU.

**eval**

A method of `Tensor` that returns the value of the `Tensor`, triggering any
graph computation required to determine the value. You may only call `eval()`
on a `Tensor` in a graph that has been launched in a session.

**Feed**

TensorFlow's mechanism for patching a tensor directly into any node in a graph
launched in a session. You apply feeds when you trigger the execution of a
graph, not when you build the graph. A feed temporarily replaces a node with a
tensor value. You supply feed data as an argument to a `run()` or `eval()` call
that initiates computation. After the run the feed disappears and the original
node definition remains. You usually designate specific nodes to be "feed"
nodes by using `tf.placeholder()` to create them. See
[Basic Usage](../get_started/basic_usage.md) for more information.

**Fetch**

TensorFlow's mechanism for retrieving tensors from a graph launched in a
session. You retrieve fetches when you trigger the execution of a graph, not
when you build the graph. To fetch the tensor value of a node or nodes,
execute the graph with a `run()` call on the `Session` object and pass a list of
names of nodes to retrieve. See [Basic Usage](../get_started/basic_usage.md)
for more information.

**Graph**

Describes a computation as a directed acyclic
graph. Nodes in the graph represent operations that must be
performed. Edges in the graph represent either data or control
dependencies. `GraphDef` is the proto used to describe a graph to the
system (it is the API), and consists of a collection of `NodeDefs` (see
below). A `GraphDef` may be converted to a (C++) `Graph` object which is
easier to operate on.

**IndexedSlices**

In the Python API, TensorFlow's representation of a tensor that is sparse
along only its first dimension. If the tensor is `k`-dimensional, an
`IndexedSlices` instance logically represents a collection of
`(k-1)`-dimensional slices along the tensor's first dimension. The indices of
the slices are stored concatenated into a single 1-dimensional vector, and the
corresponding slices are concatenated to form a single `k`-dimensional tensor. Use
`SparseTensor` if the sparsity is not restricted to the first dimension.

**Node**

An element of a graph.

Describes how to invoke a specific operation as one node in a specific
computation `Graph`, including the values for any `attrs` needed to configure
the operation. For operations that are polymorphic, the `attrs` include
sufficient information to completely determine the signature of the `Node`.
See `graph.proto` for details.

**Op (operation)**

In the TensorFlow runtime: A type of computation such as `add` or `matmul` or
`concat`. You can add new ops to the runtime as described [how to add an
op](../how_tos/adding_an_op/index.md).

In the Python API: A node in the graph. Ops are represented by instances of
the class [`tf.Operation`](../api_docs/python/framework.md#Operation). The
`type` property of an `Operation` indicates the run operation for the node,
such as `add` or `matmul`.

**Run**

The action of executing ops in a launched graph. Requires that the graph be
launched in a `Session`.

In the Python API: A method of the `Session` class:
[`tf.Session.run`](../api_docs/python/client.md#Session). You can pass tensors
to feed and fetch to the `run()` call.

In the C++ API: A method of the [`tensorflow::Session`](../api_docs/cc/ClassSession.md).

**Session**

A runtime object representing a launched graph. Provides methods to execute
ops in the graph.

In the Python API: [`tf.Session`](../api_docs/python/client.md#Session)

In the C++ API: class used to launch a graph and run operations
[`tensorflow::Session`](../api_docs/cc/ClassSession.md).

**Shape**

The number of dimensions of a tensor and their sizes.

In a launched graph: Property of the tensors that flow between nodes. Some ops
have strong requirements on the shape of their inputs and report errors at
runtime if these are not met.

In the Python API: Attribute of a Python `Tensor` in the graph construction
API. During constructions the shape of tensors can be only partially known, or
even unknown. See
[`tf.TensorShape`](../api_docs/python/framework.md#TensorShape)

In the C++ API: class used to represent the shape of tensors
[`tensorflow::TensorShape`](../api_docs/cc/ClassTensorShape.md).

**SparseTensor**

In the Python API, TensorFlow's representation of a tensor that is sparse in
arbitrary positions. A `SparseTensor` stores only the non-empty values along
with their indices, using a dictionary-of-keys format. In other words, if
there are `m` non-empty values, it maintains a length-`m` vector of values and
a matrix with m rows of indices. For efficiency, `SparseTensor` requires the
indices to be sorted along increasing dimension number, i.e. in row-major
order. Use `IndexedSlices` if the sparsity is only along the first dimension.

**Tensor**

A `Tensor` is a typed multi-dimensional array. For example, a 4-D
array of floating point numbers representing a mini-batch of images with
dimensions `[batch, height, width, channel]`.

In a launched graph: Type of the data that flow between nodes.

In the Python API: class used to represent the output and inputs of ops added
to the graph [`tf.Tensor`](../api_docs/python/framework.md#Tensor). Instances of
this class do not hold data.

In the C++ API: class used to represent tensors returned from a
[`Session::Run()`](../api_docs/cc/ClassSession.md) call
[`tensorflow::Tensor`](../api_docs/cc/ClassTensor.md).
Instances of this class hold data.

#术语表

###广播操作(Broadcasting operation)
一种用[numpy-style broadcasting](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)来保证tensor参数的形态兼容的操作。

###Devices
一块可以用来运算并且拥有自己的地址空间的硬件，比如GPU和CPU。
###eval
Tensor的一个方法，返回Tensor的值。触发任意一个图表计算都需要计算出这个值。只能在一个会话图表中的Tensor上调用。
###Feed
TensorFlow的一个概念：把一个tensor直接连接到一个会话图表中的任意节点。feed不是在构建图表(graph)的时候创建，而是在触发图表的执行操作时去申请。一个feed临时替代一个带有tensor值的节点。把feed数据作为run()方法和eval()方法的参数来初始化运算。方法运行结束后，feed就会消失，而最初的节点定义仍然还在。可以通过tf.placeholder()把特定的节点指定为feed节点来创建它们。详见[Basic Usage](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/get_started/basic_usage.md).
###Fetch
TensorFlow中的一个概念：从一个会话图表中取回tensor。取回fetches的申请发生在触发执行图表操作的时候，而不是发生在建立图表的时候。如果要取回一个或多个节点（node）的tensor值，可以通过在Session对象上调用run()方法并将待取回节点（node）的列表作为参数来执行图表(graph)。详见[Basic Usage](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/get_started/basic_usage.md)。
###Graph(图表)
把运算描述成一个直接的无环图形（DAG），图表中的节点（node）代表必须要实现的一些操作。图表中的边代表数据或者可控的依赖。GratheDef是系统中描述一个图表的协议(api)，它由一个NodeDefs集合组成。一个GraphDef可以转化成一个更容易操作的图表对象。
###IndexedSlices（索引化切片）
在Python API中，TensorFlow仅仅在第一维上对tensor有所体现。如果一个tensor有k维，那么一个IndexedSlices实例在逻辑上代表一个沿着这个tensor第一维的(k-1)维切片的集合。切片的索引被连续储存在一个单独的一维向量中，而对应的切片则被拼接成一个单独的k维tensor。如果sparsity不是受限于第一维空间，请用SparseTensor。

###Node（节点）
图表中的一个元素。
把启动一个特定操作的方式称为特定运算图表中的一个节点，包括任何用来配置这个操作的属性的值。对于那些多形态的操作，这些属性包括能完全决定这个节点（Node）签名的充分信息。详见graph.proto。
###操作（Op/operation）
在TensorFlow的运行时中，它是一种类似add或matmul或concat的运算。可以用[how to add an op](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/how_tos/adding_an_op/index.md)中的方法来向运行时添加新的操作。

在Python的API中，它是图表中的一个节点。在[tf.Operation](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/api_docs/python/framework.md#Operation)类中列举出了这些操作。一个操作(Operation)的type属性决定这个节点（node）的操作类型，比如add和matmul。
###Run
在一个运行的图表中执行某种操作的行为。要求这个图表必须运行在一次会话中。

在Python的API中，它是Session类的一个方法[tf.Session.run](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/api_docs/python/client.md#Session)。可以通过tensors来订阅或获取run()操作。

在C++的API中，[tensorflow::Session](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/api_docs/python/client.md#Session)的一个方法。
###Session(会话)
一个已经启动的图表(graph)的运行时对象。提供在图表中执行操作的一些方法。

在Python API中，[tf.Session](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/api_docs/python/client.md#Session)。

在C++API中，它是一个用来开启一个图表并运行操作的类：[tensorflow::Session](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/api_docs/cc/ClassSession.md)
###Shape
Tensor的维度和他们的大小。

在一个已经启动的图表中，它表示建立在节点（node）之间的Tensor的属性。一些操作强烈要求shape不能在运行时出现未知的输入和输出错误。

在Python API中，是图表构造API中Tensor的属性。在Tensor的Shape的构建中，要么只有部分已知，要么全部未知。见[tf.TensroShape](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/api_docs/python/framework.md#TensorShape)

在C++中，Shape类用来表现Tensor的外形[tensorflow::TensorShape](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/api_docs/cc/ClassTensorShape.md)。
###SparseTensor
在Python API中，TensorFlow对tensor的表现很散落在任意地方。SparseTensor以字典值格式来储存那些沿着索引的非空值。换言之，m个非空值，就包含一个长度为m的值向量和一个由m列索引(indices)组成的矩阵。为了提升效率，SparseTensor需要将indice（索引）按维度的增加来按序存储，比如行主序。如果稀疏值仅沿着第一维度，就用IndexedSlices。
###Tensor
Tensor是一种特定的多维数组。比如，一个浮点型的四维数组表示一小批由[batch,height，width，channel]组成的图片。

在一个运行的图表(graph)中，它是一种连接在节点（node）之间的数据。
在Python中，Tensor类表示添加到图表的操作中的输入和输出，见[tf.Tensor](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/api_docs/python/framework.md#Tensor)，这样的类不持有数据。

在C++中，Tensor是方法[Session::Run()](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/api_docs/cc/ClassSession.md)的返回值，见[tensorflow::Tensor](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/api_docs/cc/ClassTensor.md)，这样的Tensor持有数据。

原文：[Glossary](https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/resources/glossary.md) 翻译：[leege100](https://github.com/leege100)

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