Simple helper library to convert a collection of numpy data to tfrecord, and build a tensorflow dataset from the tfrecord.

Related tags

Deep Learningtensorflownumpytfrecord
Overview

numpy2tfrecord

Simple helper library to convert a collection of numpy data to tfrecord, and build a tensorflow dataset from the tfrecord.

Installation

$ git clone [email protected]:yonetaniryo/numpy2tfrecord.git
$ cd numpy2tfrecord
$ pip install .

Test

$ pytest -v

How to use

Convert a collection of numpy data to tfrecord

import numpy as np
from numpy2tfrecord import Numpy2Tfrecord

converter = Numpy2Tfrecord()
x = np.arange(100).reshape(10, 10).astype(np.float32)  # float array
y = np.arange(100).reshape(10, 10).astype(np.int64)  # int array
a = 5  # int
b = 0.3  # float
entry = {"x": x, "y": y, "a": a, "b": b}
converter.add(entry)  # add data entry
...

converter.export_to_tfrecord("test.tfrecord")  # export to tfrecord

Build a tensorflow dataset from tfrecord

dataset = build_dataset_from_tfrecord("test.tfrecord")  # load tfrecord and build tf.data.Dataset
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Comments
  • Add some more choices for adding samples

    Add some more choices for adding samples

    • add -> add_sample
    • add_batch: takes a dictionary whose elements each have the batch axis
    • add_list_of_samples: takes a list of dictionaries as input
    • add_list_of_batches
    enhancement 
    opened by yonetaniryo 0
Releases(0.0.2)
Owner
Ryo Yonetani
Ryo Yonetani
GitHub Repository
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