This repository contains solutions to a series of problems relating to the famous MNIST data set. The data sets are in a bespoke format and can be obtained from the website found HERE!
The repository contains a basic python script which uses a series of functions written to decompress and read the .gz files.
The functions are listed as follows:
- read_labels_from_file
- read_images_from_file
- save_image
- print_image
1. If you are using OSX - Install Python 3 using Homebrew. Alternatively check out Anaconda - An open source distribution of Python.
brew install python3
git clone https://github.com/damiannolan/mnist-data-problemsheet.git
3. Download the MNIST data files from the link provided above and place the .gz files in a subfolder named /data
python3 mnist-data.py
The original problem sheet can be found HERE!
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.
