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A Google Tensorflow MongoDB connector

Project description

TFMongoDB is a C++ implemented dataset op for google’s tensorflow that allows you to connect to your MongoDatabase natively. Hence you can access your mongodb stored documents more efficiently.

Currently only MacOS X is supported while Tensorflow >= 1.5 is required.


Installation

In order to use the dataset you need to install it with pip:

pip install tfmongodb

Usage

TFMongoDB can be accessed through the MongoDBDataset:

dataset = MongoDBDataset(<database_name>, <collection_name>)

example:

from tfmongodb import MongoDBDataset
from tensorflow.python.framework import ops
from tensorflow.python.data.ops import iterator_ops
import tensorflow as tf

CSV_TYPES = [[""], [""], [0]]

def _parse_line(line):
    fields = tf.decode_csv(line, record_defaults=CSV_TYPES)
    return fields

dataset = MongoDBDataset("eccounting", "creditors")
dataset = dataset.map(_parse_line)
repeat_dataset2 = dataset.repeat()
batch_dataset = repeat_dataset2.batch(20)

iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types)
#init_op = iterator.make_initializer(dataset)
init_batch_op = iterator.make_initializer(batch_dataset)
get_next = iterator.get_next()


with tf.Session() as sess:
    # Basic test: read from topic 0.
    sess.run(init_batch_op, feed_dict={})

    for i in range(5):
        print(sess.run(get_next))

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