开发者社区> 知与谁同> 正文
阿里云
为了无法计算的价值
打开APP
阿里云APP内打开

TensorFlow教程之API DOC 6.3.4. CONTROL FLOW OPS

简介:
+关注继续查看

本文档为TensorFlow参考文档,本转载已得到TensorFlow中文社区授权。


Control Flow

Note: Functions taking Tensor arguments can also take anything accepted by tf.convert_to_tensor.

Contents

Control Flow

Control Flow Operations

TensorFlow provides several operations and classes that you can use to control the execution of operations and add conditional dependencies to your graph.


tf.identity(input, name=None)

Return a tensor with the same shape and contents as the input tensor or value.

Args:
  • input: A Tensor.
  • name: A name for the operation (optional).
Returns:

Tensor. Has the same type as input.


tf.tuple(tensors, name=None, control_inputs=None)

Group tensors together.

This creates a tuple of tensors with the same values as the tensors argument, except that the value of each tensor is only returned after the values of all tensors have been computed.

control_inputs contains additional ops that have to finish before this op finishes, but whose outputs are not returned.

This can be used as a "join" mechanism for parallel computations: all the argument tensors can be computed in parallel, but the values of any tensor returned by tuple are only available after all the parallel computations are done.

See also group and with_dependencies.

Args:
  • tensors: A list of Tensors or IndexedSlices, some entries can be None.
  • name: (optional) A name to use as a name_scope for the operation.
  • control_inputs: List of additional ops to finish before returning.
Returns:

Same as tensors.

Raises:
  • ValueError: If tensors does not contain any Tensor or IndexedSlices.

tf.group(*inputs, **kwargs)

Create an op that groups multiple operations.

When this op finishes, all ops in input have finished. This op has no output.

See also tuple and with_dependencies.

Args:
  • *inputs: One or more tensors to group.
  • **kwargs: Optional parameters to pass when constructing the NodeDef.
  • name: A name for this operation (optional).
Returns:

An Operation that executes all its inputs.

Raises:
  • ValueError: If an unknown keyword argument is provided, or if there are
           no inputs.
    

tf.no_op(name=None)

Does nothing. Only useful as a placeholder for control edges.

Args:
  • name: A name for the operation (optional).
Returns:

The created Operation.


tf.count_up_to(ref, limit, name=None)

Increments 'ref' until it reaches 'limit'.

This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the updated value.

Args:
  • ref: A mutable Tensor. Must be one of the following types: int32int64. Should be from a scalar Variable node.
  • limit: An int. If incrementing ref would bring it above limit, instead generates an 'OutOfRange' error.
  • name: A name for the operation (optional).
Returns:

Tensor. Has the same type as ref. A copy of the input before increment. If nothing else modifies the input, the values produced will all be distinct.

Logical Operators

TensorFlow provides several operations that you can use to add logical operators to your graph.


tf.logical_and(x, y, name=None)

Returns the truth value of x AND y element-wise.

Args:
  • x: A Tensor of type bool.
  • y: A Tensor of type bool.
  • name: A name for the operation (optional).
Returns:

Tensor of type bool.


tf.logical_not(x, name=None)

Returns the truth value of NOT x element-wise.

Args:
  • x: A Tensor of type bool.
  • name: A name for the operation (optional).
Returns:

Tensor of type bool.


tf.logical_or(x, y, name=None)

Returns the truth value of x OR y element-wise.

Args:
  • x: A Tensor of type bool.
  • y: A Tensor of type bool.
  • name: A name for the operation (optional).
Returns:

Tensor of type bool.


tf.logical_xor(x, y, name='LogicalXor')

x ^ y = (x | y) & ~(x & y).

Comparison Operators

TensorFlow provides several operations that you can use to add comparison operators to your graph.


tf.equal(x, y, name=None)

Returns the truth value of (x == y) element-wise.

Args:
  • x: A Tensor. Must be one of the following types: float32float64int32int64complex64quint8qint8qint32.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).
Returns:

Tensor of type bool.


tf.not_equal(x, y, name=None)

Returns the truth value of (x != y) element-wise.

Args:
  • x: A Tensor. Must be one of the following types: float32float64int32int64complex64quint8qint8qint32.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).
Returns:

Tensor of type bool.


tf.less(x, y, name=None)

Returns the truth value of (x < y) element-wise.

Args:
  • x: A Tensor. Must be one of the following types: float32float64int32int64.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).
Returns:

Tensor of type bool.


tf.less_equal(x, y, name=None)

Returns the truth value of (x <= y) element-wise.

Args:
  • x: A Tensor. Must be one of the following types: float32float64int32int64.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).
Returns:

Tensor of type bool.


tf.greater(x, y, name=None)

Returns the truth value of (x > y) element-wise.

Args:
  • x: A Tensor. Must be one of the following types: float32float64int32int64.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).
Returns:

Tensor of type bool.


tf.greater_equal(x, y, name=None)

Returns the truth value of (x >= y) element-wise.

Args:
  • x: A Tensor. Must be one of the following types: float32float64int32int64.
  • y: A Tensor. Must have the same type as x.
  • name: A name for the operation (optional).
Returns:

Tensor of type bool.


tf.select(condition, t, e, name=None)

Selects elements from t or e, depending on condition.

The conditiont, and e tensors must all have the same shape, and the output will also have that shape. The condition tensor acts as an element-wise mask that chooses, based on the value at each element, whether the corresponding element in the output should be taken from t (if true) or e (if false). For example:

For example:

# 'condition' tensor is [[True, False]
#                        [True, False]]
# 't' is [[1, 1],
#         [1, 1]]
# 'e' is [[2, 2],
#         [2, 2]]
select(condition, t, e) ==> [[1, 2],
                             [1, 2]]
Args:
  • condition: A Tensor of type bool.
  • t: A Tensor with the same shape as condition.
  • e: A Tensor with the same type and shape as t.
  • name: A name for the operation (optional).
Returns:

Tensor with the same type and shape as t and e.


tf.where(input, name=None)

Returns locations of true values in a boolean tensor.

This operation returns the coordinates of true elements in input. The coordinates are returned in a 2-D tensor where the first dimension (rows) represents the number of true elements, and the second dimension (columns) represents the coordinates of the true elements. Keep in mind, the shape of the output tensor can vary depending on how many true values there are in input. Indices are output in row-major order.

For example:

# 'input' tensor is [[True, False]
#                    [True, False]]
# 'input' has two true values, so output has two coordinates.
# 'input' has rank of 2, so coordinates have two indices.
where(input) ==> [[0, 0],
                  [1, 0]]

# `input` tensor is [[[True, False]
#                     [True, False]]
#                    [[False, True]
#                     [False, True]]
#                    [[False, False]
#                     [False, True]]]
# 'input' has 5 true values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
                  [0, 1, 0],
                  [1, 0, 1],
                  [1, 1, 1],
                  [2, 1, 1]]
Args:
  • input: A Tensor of type bool.
  • name: A name for the operation (optional).
Returns:

Tensor of type int64.

Debugging Operations

TensorFlow provides several operations that you can use to validate values and debug your graph.


tf.is_finite(x, name=None)

Returns which elements of x are finite.

Args:
  • x: A Tensor. Must be one of the following types: float32float64.
  • name: A name for the operation (optional).
Returns:

Tensor of type bool.


tf.is_inf(x, name=None)

Returns which elements of x are Inf.

Args:
  • x: A Tensor. Must be one of the following types: float32float64.
  • name: A name for the operation (optional).
Returns:

Tensor of type bool.


tf.is_nan(x, name=None)

Returns which elements of x are NaN.

Args:
  • x: A Tensor. Must be one of the following types: float32float64.
  • name: A name for the operation (optional).
Returns:

Tensor of type bool.


tf.verify_tensor_all_finite(t, msg, name=None)

Assert that the tensor does not contain any NaN's or Inf's.

Args:
  • t: Tensor to check.
  • msg: Message to log on failure.
  • name: A name for this operation (optional).
Returns:

Same tensor as t.


tf.check_numerics(tensor, message, name=None)

Checks a tensor for NaN and Inf values.

When run, reports an InvalidArgument error if tensor has any values that are not a number (NaN) or infinity (Inf). Otherwise, passes tensor as-is.

Args:
  • tensor: A Tensor. Must be one of the following types: float32float64.
  • message: A string. Prefix of the error message.
  • name: A name for the operation (optional).
Returns:

Tensor. Has the same type as tensor.


tf.add_check_numerics_ops()

Connect a check_numerics to every floating point tensor.

check_numerics operations themselves are added for each float or double tensor in the graph. For all ops in the graph, the check_numerics op for all of its (float or double) inputs is guaranteed to run before the check_numerics op on any of its outputs.

Returns:

group op depending on all check_numerics ops added.


tf.Assert(condition, data, summarize=None, name=None)

Asserts that the given condition is true.

If condition evaluates to false, print the list of tensors in datasummarize determines how many entries of the tensors to print.

Args:
  • condition: The condition to evaluate.
  • data: The tensors to print out when condition is false.
  • summarize: Print this many entries of each tensor.
  • name: A name for this operation (optional).

tf.Print(input_, data, message=None, first_n=None, summarize=None, name=None)

Prints a list of tensors.

This is an identity op with the side effect of printing data when evaluating.

Args:
  • input_: A tensor passed through this op.
  • data: A list of tensors to print out when op is evaluated.
  • message: A string, prefix of the error message.
  • first_n: Only log first_n number of times. Negative numbers log always;
        this is the default.
    
  • summarize: Only print this many entries of each tensor.
  • name: A name for the operation (optional).
Returns:

Same tensor as input_.

版权声明:本文内容由阿里云实名注册用户自发贡献,版权归原作者所有,阿里云开发者社区不拥有其著作权,亦不承担相应法律责任。具体规则请查看《阿里云开发者社区用户服务协议》和《阿里云开发者社区知识产权保护指引》。如果您发现本社区中有涉嫌抄袭的内容,填写侵权投诉表单进行举报,一经查实,本社区将立刻删除涉嫌侵权内容。

相关文章
原生开发移动web单页面(step by step)6——history api应用
以上几篇的内容成功的将多页面合成到单页面上,然而还是有很多区别的, 多页面切换的时候,通过浏览器的自带后退前进键可以进行导航,然而到目前为止,是没有办法进行的导航的,这一篇主要是引入了这个功能。
1220 0
关于java web restful api文档的重新探索
一款零注解、零侵入的java web接口文档生成工具
2277 0
利用Java编码测试CSRF令牌验证的Web API
前一篇拙文是利用了Jmeter来测试带有CSRF令牌验证的Web API;最近几天趁着项目不忙,练习了用编码的方式实现。 有了之前Jmeter脚本的基础,基本上难点也就在两个地方:获取CSRF令牌、Cookie的传递。
980 0
利用Jmeter测试CSRF令牌验证的Web API
事情的起因是最近收到的一批测试需求,要测试公司HR系统的接口性能。这个是需要测试的接口列表: 所有的接口请求,都基于登录验证成功,否则将无法获得正确的应答。 首先想到的是在浏览器上捕捉请求。打开Chrome浏览器,调出开发者工具栏,在地址栏输入登录模块的地址,访问登录页面:   输入账号和密码,录制登录过程;然后定位到开发工具的Network页面,找到登录的事务。
1549 0
Asp.Net Core Web Api图片上传(一)集成MongoDB存储实例教程
Asp.Net Core Web Api图片上传及MongoDB存储实例教程(一) 图片或者文件上传相信大家在开发中应该都会用到吧,有的时候还要对图片生成缩略图。那么如何在Asp.Net Core Web Api实现图片上传存储以及生成缩略图呢?今天我就使用MongoDB作为图片存储,然后使用SixLabors作为图片处理,通过一个Asp.
1235 0
[译]ASP.NET Core Web API 中使用Oracle数据库和Dapper看这篇就够了
[译]ASP.NET Core Web API 中使用Oracle数据库和Dapper看这篇就够了 本文首发自:博客园 文章地址: https://www.cnblogs.com/yilezhu/p/9276565.html 园子里关于ASP.NET Core Web API的教程很多,但大多都是使用EF+Mysql或者EF+MSSQL的文章。
2908 0
ASP.NET Core Web API 与 SSL
SSL 一直没有真正研究过SSL,不知道下面的理解是否正确。 SSL是Secure Sockets Layer的缩写,它用来保护服务器和客户端之前的通信。它是基于信任+加密的概念。 在介绍SSL的原理之前,首先介绍一下加密(Encryption)的概念。
1626 0
K/3 Cloud Web API接口说明文
K/3 Cloud Web API接口说明文 目的 三方集成,提供第三方系统与Cloud集成调用接口。   技术实现 HTTP + Json   提供标准接口   编号 名称 说明 1 Kingdee.
1630 0
+关注
文章
问答
文章排行榜
最热
最新
相关电子书
更多
阿里云 API 精选手册(Alibaba Cloud API Playbook)
立即下载
ACE 区域技术发展峰会:Flink Python Table API入门及实践
立即下载
Time Series Analysis
立即下载