博客
关于我
tf.map_fn
阅读量:700 次
发布时间:2019-03-17

本文共 4417 字,大约阅读时间需要 14 分钟。

map on the list of tensors unpacked from elems on dimension 0.

tf.map_fn(    fn,    elems,    dtype=None,    parallel_iterations=None,    back_prop=True,    swap_memory=False,    infer_shape=True,    name=None)

The simplest version of map_fn repeatedly applies the callable fn to a sequence of elements from first to last. The elements are made of the tensors unpacked from elems. dtype is the data type of the return value of fn. Users must provide dtype if it is different from the data type of elems.

Suppose that elems is unpacked into values, a list of tensors. The shape of the result tensor is [values.shape[0]] + fn(values[0]).shape.

This method also allows multi-arity elems and output of fn. If elems is a (possibly nested) list or tuple of tensors, then each of these tensors must have a matching first (unpack) dimension. The signature of fn may match the structure of elems. That is, if elems is (t1, [t2, t3, [t4, t5]]), then an appropriate signature for fn is: fn = lambda (t1, [t2, t3, [t4, t5]]):.

Furthermore, fn may emit a different structure than its input. For example, fn may look like: fn = lambda t1: return (t1 + 1, t1 - 1). In this case, the dtype parameter is not optional: dtype must be a type or (possibly nested) tuple of types matching the output of fn.

To apply a functional operation to the nonzero elements of a SparseTensor one of the following methods is recommended. First, if the function is expressible as TensorFlow ops, use

  result = SparseTensor(input.indices, fn(input.values), input.dense_shape)

If, however, the function is not expressible as a TensorFlow op, then use

result = SparseTensor(  input.indices, map_fn(fn, input.values), input.dense_shape)

instead.

When executing eagerly, map_fn does not execute in parallel even if parallel_iterations is set to a value > 1. You can still get the performance benefits of running a function in parallel by using the tf.contrib.eager.defun decorator,

# Assume the function being used in map_fn is fn.# To ensure map_fn calls fn in parallel, use the defun decorator.@tf.contrib.eager.defundef func(tensor):  return tf.map_fn(fn, tensor)

Note that if you use the defun decorator, any non-TensorFlow Python code that you may have written in your function won't get executed. See tf.contrib.eager.defun for more details. The recommendation would be to debug without defun but switch to defun to get performance benefits of running map_fn in parallel.

Args:

  • fn: The callable to be performed. It accepts one argument, which will have the same (possibly nested) structure as elems. Its output must have the same structure as dtype if one is provided, otherwise it must have the same structure as elems.
  • elems: A tensor or (possibly nested) sequence of tensors, each of which will be unpacked along their first dimension. The nested sequence of the resulting slices will be applied to fn.
  • dtype: (optional) The output type(s) of fn. If fn returns a structure of Tensors differing from the structure of elems, then dtype is not optional and must have the same structure as the output of fn.
  • parallel_iterations: (optional) The number of iterations allowed to run in parallel. When graph building, the default value is 10. While executing eagerly, the default value is set to 1.
  • back_prop: (optional) True enables support for back propagation.
  • swap_memory: (optional) True enables GPU-CPU memory swapping.
  • infer_shape: (optional) False disables tests for consistent output shapes.
  • name: (optional) Name prefix for the returned tensors.

Returns:

  • A tensor or (possibly nested) sequence of tensors. Each tensor packs the results of applying fn to tensors unpacked from elems along the first dimension, from first to last.

Raises:

  • TypeError: if fn is not callable or the structure of the output of fn and dtype do not match, or if elems is a SparseTensor.
  • ValueError: if the lengths of the output of fn and dtype do not match.

Examples:

elems = np.array([1, 2, 3, 4, 5, 6])squares = map_fn(lambda x: x * x, elems)# squares == [1, 4, 9, 16, 25, 36]
elems = (np.array([1, 2, 3]), np.array([-1, 1, -1]))alternate = map_fn(lambda x: x[0] * x[1], elems, dtype=tf.int64)# alternate == [-1, 2, -3]
elems = np.array([1, 2, 3])alternates = map_fn(lambda x: (x, -x), elems, dtype=(tf.int64, tf.int64))# alternates[0] == [1, 2, 3]# alternates[1] == [-1, -2, -3]

 

 

承接Matlab、Python和C++的编程,机器学习、计算机视觉的理论实现及辅导,本科和硕士的均可,咸鱼交易,专业回答请走知乎,详谈请联系QQ号757160542,非诚勿扰。

 

转载地址:http://qwqhz.baihongyu.com/

你可能感兴趣的文章
MySql创建数据表
查看>>
MySQL创建新用户以及ERROR 1396 (HY000)问题解决
查看>>
MySQL创建用户与授权
查看>>
MySQL创建用户报错:ERROR 1396 (HY000): Operation CREATE USER failed for 'slave'@'%'
查看>>
MySQL创建索引时提示“Specified key was too long; max key length is 767 bytes”
查看>>
mysql初始密码错误问题
查看>>
mysql判断某一张表是否存在的sql语句以及方法
查看>>
mysql加入安装策略_一键安装mysql5.7及密码策略修改方法
查看>>
mysql加强(1)~用户权限介绍、分别使用客户端工具和命令来创建用户和分配权限
查看>>
mysql加强(3)~分组(统计)查询
查看>>
mysql加强(4)~多表查询:笛卡尔积、消除笛卡尔积操作(等值、非等值连接),内连接(隐式连接、显示连接)、外连接、自连接
查看>>
mysql加强(5)~DML 增删改操作和 DQL 查询操作
查看>>
mysql加强(6)~子查询简单介绍、子查询分类
查看>>
mysql加强(7)~事务、事务并发、解决事务并发的方法
查看>>
mysql千万级大数据SQL查询优化
查看>>
MySQL千万级大表优化策略
查看>>
MySQL单实例或多实例启动脚本
查看>>
MySQL压缩包方式安装,傻瓜式教学
查看>>
MySQL原理、设计与应用全面解析
查看>>
MySQL原理简介—1.SQL的执行流程
查看>>