python-mnist 0.7

Description:

pythonmnist 0.7

Simple MNIST and EMNIST data parser written in pure Python.
MNIST is a database of handwritten digits available on
http://yann.lecun.com/exdb/mnist/. EMNIST is an extended MNIST database
https://www.nist.gov/itl/iad/image-group/emnist-dataset.

Requirements

Python 2 or Python 3



Usage

git clone https://github.com/sorki/python-mnist
cd python-mnist
Get MNIST data:
./bin/mnist_get_data.sh

Check preview with:
PYTHONPATH=. ./bin/mnist_preview




Installation
Get the package from PyPi:
pip install python-mnist
or install with setup.py:
python setup.py install
Code sample:
from mnist import MNIST
mndata = MNIST('./dir_with_mnist_data_files')
images, labels = mndata.load_training()
To enable loading of gzip-ed files use:
mndata.gz = True
Library tries to load files named t10k-images-idx3-ubyte
train-labels-idx1-ubyte train-images-idx3-ubyte and
t10k-labels-idx1-ubyte. If loading throws an exception check if these
names match.


EMNIST

Get EMNIST data:
./bin/emnist_get_data.sh

Check preview with:
PYTHONPATH=. ./bin/emnist_preview


To use EMNIST datasets you need to call:
mndata.select_emnist('digits')
Where digits is one of the available EMNIST datasets. You can choose
from


balanced
byclass
bymerge
digits
letters
mnist


EMNIST loader uses gziped files by default, this can be disabled by by
setting:
mndata.gz = False
You also need to unpack EMNIST files as bin/emnist_get_data.sh script
won’t do it for you. EMNIST loader also needs to mirror and rotate
images so it is a bit slower (If this is an issue for you, you should
repack the data to avoid mirroring and rotation on each load).


Notes
This package doesn’t use numpy by design as when I’ve tried to find a
working implementation all of them were based on some archaic version of
numpy and none of them worked. This loads data files with struct.unpack
instead.


Example
$ PYTHONPATH=. ./bin/mnist_preview
Showing num: 3

............................
............................
............................
............................
............................
............................
.............@@@@@..........
..........@@@@@@@@@@........
.......@@@@@@......@@.......
.......@@@........@@@.......
.................@@.........
................@@@.........
...............@@@@@........
.............@@@............
.............@.......@......
.....................@......
.....................@@.....
....................@@......
...................@@@......
.................@@@@.......
................@@@@........
....@........@@@@@..........
....@@@@@@@@@@@@............
......@@@@@@................
............................
............................
............................
............................