MNIST

A crate for parsing the MNIST and Fashion MNIST data set into vectors to be used by Rust programs.

• Crate
• Documentation

Example

```rust extern crate mnist; extern crate rulinalg;

use mnist::{Mnist, MnistBuilder}; use rulinalg::matrix::{BaseMatrix, BaseMatrixMut, Matrix};

fn main() { let (trnsize, rows, cols) = (50000, 28, 28);

// Deconstruct the returned Mnist struct.
let Mnist { trn_img, trn_lbl, .. } = MnistBuilder::new()
    .label_format_digit()
    .training_set_length(trn_size)
    .validation_set_length(10_000)
    .test_set_length(10_000)
    .finalize();

// Call `normalize()` on the return value of `finalize()` to get
// Vec<f32> normalized values for the pixels instead of grayscale (bytes):
// let NormalizedMnist { trn_img, trn_lbl, .. } = MnistBuilder::new()
//     .label_format_digit()
//     .training_set_length(trn_size)
//     .validation_set_length(10_000)
//     .test_set_length(10_000)
//     .finalize()
//     .normalize();

// Get the label of the first digit.
let first_label = trn_lbl[0];
println!("The first digit is a {}.", first_label);

// Convert the flattened training images vector to a matrix.
let trn_img = Matrix::new((trn_size * rows) as usize, cols as usize, trn_img);

// Get the image of the first digit.
let row_indexes = (0..27).collect::<Vec<_>>();
let first_image = trn_img.select_rows(&row_indexes);
println!("The image looks like... \n{}", first_image);

// Convert the training images to f32 values scaled between 0 and 1.
let trn_img: Matrix<f32> = trn_img.try_into().unwrap() / 255.0;

// Get the image of the first digit and round the values to the nearest tenth.
let first_image = trn_img.select_rows(&row_indexes)
    .apply(&|p| (p * 10.0).round() / 10.0);
println!("The image looks like... \n{}", first_image);

} ```

Fashion MNIST

The Fasion MNIST dataset offers a similarly-formatted drop-in replacement dataset for the original MNIST set, but typically poses a more difficult classification challenge that handwritten numbers.

An example of downloading this dataset may be found by running: sh $ cargo run --features download --example fashion_mnist This example uses the minifb library to display the parsed images, and may require the installation of certain dependencies. On an Ubuntu-like system, this may be done via: sh $ sudo apt install libxkbcommon-dev libwayland-cursor0 libwayland-dev