mnist.py

This python module provides a function to download, extract and configure the MNIST database of handwritten digits that was created by Yann LeCun et al for training and testing neural-networks. The original dataset in http://yann.lecun.com has been removed. Fortunately, they can still be obtained from https://web.archive.org/ and Keras.

Function:

load_MNIST(path=None, normalise=True, flatten=True, onehot=True)

kwarg:

path - str: MNIST datasets directory. Default to current directory/MNIST.
            Create if nonexistant. Download any missing MNIST files.
normalise - boolean: yes -> pixel RGB values [0,255] divided by 255.
                     no  -> pixel RGB values [0,255].
flatten   - boolean: yes -> pixels of all images stored as 2D numpy array.
                     no  -> pixels of all images stored as 3D numpy array.
onehot    - boolean: yes -> labels stored as one-hot encoded numpy array.
                     no  -> labels values used.

Returns:

A dataklass called 'Mnist' with numpy.ndarray attributes called 
'train_images', 'train_labels', 'test_images' and 'test_labels'.

If normalise, the dtype of Mnist.train_images and Mnist.test_images are
numpy.float64, else they will be numpy.uint8'

If flatten, the shape of Mnist.train_images and Mnist.test_images is
(60000, 784) and (10000, 784), respectively, else they will be
(60000, 28, 28) and (10000, 28, 28), respectively.

If onehot, the shape of Mnist.train_labels and Mnist.test_labels are
(60000, 10) and (10000, 10), respectively, else they will be
(60000,) and (10000,), respectively.

How to use?

Step 1: Use either one of these import statements.

from mnist_from_lecun import load_MNIST     # Import function from module
from mnist_from_keras import load_MNIST     # Import function from module

Step 2: Run the function to get MNIST database, e.g.

mdb = load_MNIST()                          # Default

Step 3: Access individual dataset like so:

train_images = mdb.train_images             # A 60000x784 numpy array with float64 values    
train_labels = mdb.train.labels             # A 60000x10 numpy array with uint8 values
test_images = mdb.test_images               # A 10000x784 numpy array with float64 values    
test_labels = mdb.test_labels               # A 10000x10 numpy array with uint8 values

Dependencies:

1. numpy==2.2.2
2. requests==2.32.3

Python Interpreter:

Python 3.13