使用tensorflow的Dataset管道，如何“命名" map操作的结果? [英] Using tensorflow's Dataset pipeline, how do I *name* the results of a `map` operation?

问题描述

我在下面有一个map函数(可运行的示例)，该函数输入一个string并输出一个string和integer.

I have the map function below (runnable example), which inputs a string and outputs a string and an integer.

我将原始输入命名为'filenames'.但是，当我从map函数map_element_counts返回值时，我只能返回一个元组(返回字典会产生异常).

in tf.data.Dataset.from_tensor_slices I named the original input 'filenames'. But when I return the values from the map function map_element_counts I can only return a tuple (returning a dictionary generates an exception).

是否可以命名从我的map_element_counts函数返回的2个元素?

Is there a way to name the 2 elements returned from my map_element_counts function?

import tensorflow as tf

filelist = ['fileA_6', 'fileB_10', 'fileC_7']

def map_element_counts(fname):
  # perform operations outside of tensorflow
  return 'test', 10

ds = tf.data.Dataset.from_tensor_slices({'filenames': filelist})
ds = ds.map(map_func=lambda x: tf.py_func(
  func=map_element_counts, inp=[x['filenames']], Tout=[tf.string, tf.int64]
))
element = ds.make_one_shot_iterator().get_next()

with tf.Session() as sess:
  print(sess.run(element))

结果:

(b'test', 10)

所需结果:

{'elementA': b'test', 'elementB': 10)

---

添加的详细信息:

当我执行return {'elementA': 'test', 'elementB': 10}时，出现此异常:

When I do return {'elementA': 'test', 'elementB': 10} I get this exception:

tensorflow.python.framework.errors_impl.UnimplementedError: Unsupported object type dict

推荐答案

在ds.map内部应用tf.py_func可行.

我创建了一个非常简单的文件作为示例.我在里面写10的地方.

I created a very simple file as example. Where I just write 10 inside.

dummy_file.txt:

dummy_file.txt:

10

此处是脚本:

import tensorflow as tf

filelist = ['dummy_file.txt', 'dummy_file.txt', 'dummy_file.txt']


def py_func(input):
    # perform operations outside of tensorflow
    parsed_txt_file = int(input)
    return 'test', parsed_txt_file


def map_element_counts(fname):
    # let tensorflow read the text file
    file_string = tf.read_file(fname['filenames'])
    # then use python function on the extracted string
    a, b = tf.py_func(
                    func=py_func, inp=[file_string], Tout=[tf.string, tf.int64]
                    )
    return {'elementA': a, 'elementB': b, 'file': fname['filenames']}

ds = tf.data.Dataset.from_tensor_slices({'filenames': filelist})
ds = ds.map(map_element_counts)
element = ds.make_one_shot_iterator().get_next()

with tf.Session() as sess:
    print(sess.run(element))
    print(sess.run(element))
    print(sess.run(element))

输出:

{'file': b'dummy_file.txt', 'elementA': b'test', 'elementB': 10}
{'file': b'dummy_file.txt', 'elementA': b'test', 'elementB': 10}
{'file': b'dummy_file.txt', 'elementA': b'test', 'elementB': 10}

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