The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. 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.
Download the image and label files. Have Python decompress and read them byte by byte into appropriate data structures in memory.
Output the third image in the training set to the console. Do this by representing any pixel value less than 128 as a full stop and any other pixel value as a hash symbol.
Use Python to output the image files as PNGs, saving them in a subfolder in your repository.
Name the images in the format train-XXXXX-Y.png or test-XXXXX-Y.png where XXXXX is the image number (where it occurs in the data file) and Y is its label.
For instance, the five-thousandth training image is labelled 2, so its file name should be train-04999-2.png.
Note the images are indexed from 0, so the five-thousandth image is indexed as 4999.
See below for an example of it.
Commit these image files to GitHub.
