如何读取数据到Tensorflow？ [英] How to read data into Tensorflow?

问题描述

我尝试从CSV文件读取数据以张量流，

I'm trying to read data from CSV files to tensorflow,

https://www.tensorflow.org/versions/r0.7/how_tos/reading_data/index.html ＃filenames-shuffling-and-epoch-limits

官方文档中的示例代码如下：

The sample code in official document is like this:

col1, col2, col3, col4, col5 = tf.decode_csv(value, record_defaults=record_defaults)

要读取文件，我需要知道文件中有多少列和行，如果有1000列，我需要定义1000个变量，如 col1，col2，col3，col4，col5，...，col1000，这看起来不像是一种有效的读取数据的方式。

To read the file, I need to know how many columns and lines in the file beforehand, and if there are 1000 columns, I need to define 1000 variables like col1, col2, col3, col4, col5,..., col1000 , this doesn't look like an efficient way to read data.

我的问题

1. 将CSV文件读入Tensorflow的最佳方法是什么？

1. What is the best way to read CSV files into Tensorflow ?

有没有办法在Tensorflow中读取数据库（如mongoDB）？

Is there any way to read Database (such as mongoDB) in Tensorflow ?

推荐答案

1. 你肯定不需要定义col1，col2到col1000 ...

1. You definitely don't need to define col1, col2, to col1000...

一般来说，您可以这样做：

generally, you might do things like this:

columns = tf.decode_csv(value, record_defaults=record_defaults)
features = tf.pack(columns)
do_whatever_you_want_to_play_with_features(features)

我不知道从MongoDB直接读取数据的任何现成的方式。也许你可以写一个简短的脚本来转换MongoDB中的数据，Tensorflow支持的格式，我建议二进制形式 TFRecord ，这比csv记录要快得多。 这是关于此主题的一个很好的博客文章。或者，您也可以自行选择实施自定义数据阅读器，请参阅此处的官方文档。

I do not know any off-the-shelf way to directly read data from MongoDB. Maybe you can just write a short script to convert data from MongoDB in a format that Tensorflow supports, I would recommend binary form TFRecord, which is much faster to read than csv record. This is a good blog post about this topic. Or you can choose to implement a customized data reader by yourself, see the official doc here.

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