The MNIST database is available at http://yann.lecun.com/exdb/mnist/
The MNIST database is a dataset of handwritten digits. It has 60,000 training samples, and 10,000 test samples. Each image is represented by 28x28 pixels, each containing a value 0 - 255 with its grayscale value.
It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.
It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.
There are four files available, which contain separately train and test, and images and labels.
Thanks to Yann LeCun, Corinna Cortes, Christopher J.C. Burges.
mnist makes it easier to download and parse MNIST files.
To automatically download the train files, and display the first image in the dataset, you can simply use:
import mnist
import scipy.misc
images = mnist.train_images()
scipy.misc.toimage(scipy.misc.imresize(images[0,:,:] * -1 + 256, 10.))
Test files and labels can be downloaded in a similar way:
import mnist
train_images = mnist.train_images()
train_labels = mnist.train_labels()
test_images = mnist.test_images()
test_labels = mnist.test_labels()
The dataset is downloaded and cached in your temporary directory, so, calling the functions again, is much faster and doesn't hit the server.
Images are returned as a 3D numpy array (samples * rows * columns). To train machine learning models, usually a 2D array is used (samples * features). To get it, simply use:
import mnist
train_images = mnist.train_images()
x = images.reshape((images.shape[0], images.shape[1] * images.shape[2]))
It supports Python 2.7 and Python >= 3.5.