A 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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