semvec/pypi-packages huggingface dataset:
TFMongoDB 0.1.3
A Tensorflow MongoDB connector
Keywords: tensorflow mongodb datascience

TFMongoDB
=========

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.

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

	pip install tfmongodb

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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))