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//! A crate for parsing the [MNIST](http://yann.lecun.com/exdb/mnist/) data set into vectors to be
//! used by Rust programs.
//!
//! ## About the MNIST Database
//! > The MNIST database (Mixed National Institute of Standards and Technology database) is a large
//! database of handwritten digits that is commonly used for training various image processing
//! systems. The database is also widely used for training and testing in the field of machine
//! learning. <sup><a href="https://en.wikipedia.org/wiki/MNIST_database">wikipedia</a></sup>
//!
//! The MNIST data set contains 70,000 images of handwritten digits and their
//! corresponding labels. The images are 28x28 with pixel values from 0 to 255. The labels are the
//! digits from 0 to 9. By default 60,000 of these images belong to a training set and 10,000 of
//! these images belong to a test set.
//!
//! ## Setup
//! The MNIST data set is a collection of four gzip files and can be found [here]
//! (http://yann.lecun.com/exdb/mnist/). There is one file for each of the following: the training
//! set images, the training set labels, the test set images, and the test set labels. Because of
//! space limitations, the files themselves could not be included in this crate. The four files must
//! be downloaded and extracted. By default, they will be looked for in a "data" directory at the
//! top of level of your crate.
//!
//! ## Usage
//! A [Mnist](struct.Mnist.html) struct is used to represent the various sets of data. In machine
//! learning, it is common to have three sets of data:
//!
//! * Training Set - Used to train a classifier.
//! * Validation Set - Used to regulate the training process (this set is not included in the
//! default MNIST data set partitioning).
//! * Test Set - Used after the training process to determine if the classifier has actually learned
//! something.
//!
//! Each set of data contains a vector representing the image and a vector representing the label.
//! The vectors are always completely flattened. For example, the default image test set contains
//! 60,000 images. Therefore the vector size will be
//! 60,000 images x 28 rows x 28 cols = 47,040,000 elements in the vector.
//!
//! A [MnistBuilder](struct.MnistBuilder.html) struct is used to configure how to format the MNIST
//! data, retrieves the data, and returns the [Mnist](struct.Mnist.html) struct. Configuration
//! options include:
//!
//! * where to look for the MNIST data files.
//! * how to format the label matricies.
//! * how to partition the data between the training, validation, and test sets.
//!
//! ## Examples
//! ```rust,no_run
//!
//! use mnist::*;
//! use ndarray::prelude::*;
//!fn main() {
//!
//! // Deconstruct the returned Mnist struct.
//! let Mnist {
//! trn_img,
//! trn_lbl,
//! tst_img,
//! tst_lbl,
//! ..
//! } = MnistBuilder::new()
//! .label_format_digit()
//! .training_set_length(50_000)
//! .validation_set_length(10_000)
//! .test_set_length(10_000)
//! .finalize();
//!
//! let image_num = 0;
//! // Can use an Array2 or Array3 here (Array3 for visualization)
//! let train_data = Array3::from_shape_vec((50_000, 28, 28), trn_img)
//! .expect("Error converting images to Array3 struct")
//! .map(|x| *x as f32 / 256.0);
//! println!("{:#.1?}\n",train_data.slice(s![image_num, .., ..]));
//!
//! // Convert the returned Mnist struct to Array2 format
//! let train_labels: Array2<f32> = Array2::from_shape_vec((50_000, 1), trn_lbl)
//! .expect("Error converting training labels to Array2 struct")
//! .map(|x| *x as f32);
//! println!("The first digit is a {:?}",train_labels.slice(s![image_num, ..]) );
//!
//! let _test_data = Array3::from_shape_vec((10_000, 28, 28), tst_img)
//! .expect("Error converting images to Array3 struct")
//! .map(|x| *x as f32 / 256.);
//!
//! let _test_labels: Array2<f32> = Array2::from_shape_vec((10_000, 1), tst_lbl)
//! .expect("Error converting testing labels to Array2 struct")
//! .map(|x| *x as f32);
//!
//!}
//! ```
#![doc(test(attr(allow(unused_variables), deny(warnings))))]
extern crate byteorder;
mod download;
mod tests;
use byteorder::{BigEndian, ReadBytesExt};
use std::fs::File;
use std::io::prelude::*;
use std::path::Path;
static BASE_PATH: &str = "data/";
static BASE_URL: &str = "http://yann.lecun.com/exdb/mnist";
static FASHION_BASE_URL: &str = "http://fashion-mnist.s3-website.eu-central-1.amazonaws.com";
static TRN_IMG_FILENAME: &str = "train-images-idx3-ubyte";
static TRN_LBL_FILENAME: &str = "train-labels-idx1-ubyte";
static TST_IMG_FILENAME: &str = "t10k-images-idx3-ubyte";
static TST_LBL_FILENAME: &str = "t10k-labels-idx1-ubyte";
static IMG_MAGIC_NUMBER: u32 = 0x0000_0803;
static LBL_MAGIC_NUMBER: u32 = 0x0000_0801;
static TRN_LEN: u32 = 60000;
static TST_LEN: u32 = 10000;
static CLASSES: usize = 10;
static ROWS: usize = 28;
static COLS: usize = 28;
#[derive(Debug)]
/// Struct containing image and label vectors for the training, validation, and test sets.
pub struct Mnist {
/// The training images vector.
pub trn_img: Vec<u8>,
/// The training labels vector.
pub trn_lbl: Vec<u8>,
/// The validation images vector.
pub val_img: Vec<u8>,
/// The validation labels vector.
pub val_lbl: Vec<u8>,
/// The test images vector.
pub tst_img: Vec<u8>,
/// The test labels vector.
pub tst_lbl: Vec<u8>,
}
#[derive(Debug)]
/// Struct used for configuring how to load the MNIST data.
///
/// * lbl_format - Specify how to format the label vectors. Options include:
/// * Digit (default) - a single number from 0-9 representing the corresponding digit.
/// * OneHotVector - a 1x10 one-hot vector of all 0's except for a 1 at the index of the digit.
/// * ex.) `3 -> [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]`
/// * trn_len - the length of the training set `(default = 60,000)`
/// * val_len - the length of the validation set `(default = 0)`
/// * tst_len - the length of the test set `(default = 10,000)`
/// * base_path - the path to the directory in which to look for the MNIST data files.
/// `(default = "data/")`
/// * trn_img_filename - the filename of the training images data file.
/// `(default = "train-images-idx3-ubyte")`
/// * trn_lbl_filename - the filename of the training labels data file.
/// `(default = "train-labels-idx1-ubyte")`
/// * tst_img_filename - the filename of the test images data file.
/// `(default = "10k-images-idx3-ubyte")`
/// * tst_lbl_filename - the filename of the test labels data file.
/// `(default = "t10k-labels-idx1-ubyte")`
pub struct MnistBuilder<'a> {
lbl_format: LabelFormat,
trn_len: u32,
val_len: u32,
tst_len: u32,
base_path: &'a str,
trn_img_filename: &'a str,
trn_lbl_filename: &'a str,
tst_img_filename: &'a str,
tst_lbl_filename: &'a str,
download_and_extract: bool,
base_url: &'a str,
use_fashion_data: bool,
}
impl<'a> MnistBuilder<'a> {
/// Create a new MnistBuilder with defaults set.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .finalize();
/// ```
pub fn new() -> MnistBuilder<'a> {
MnistBuilder {
lbl_format: LabelFormat::Digit,
trn_len: TRN_LEN,
val_len: 0,
tst_len: TST_LEN,
base_path: BASE_PATH,
trn_img_filename: TRN_IMG_FILENAME,
trn_lbl_filename: TRN_LBL_FILENAME,
tst_img_filename: TST_IMG_FILENAME,
tst_lbl_filename: TST_LBL_FILENAME,
download_and_extract: false,
base_url: BASE_URL,
use_fashion_data: false,
}
}
/// Set the labels format to scalar.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .label_format_digit()
/// .finalize();
/// ```
pub fn label_format_digit(&mut self) -> &mut MnistBuilder<'a> {
self.lbl_format = LabelFormat::Digit;
self
}
/// Set the labels format to vector.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .label_format_one_hot()
/// .finalize();
/// ```
pub fn label_format_one_hot(&mut self) -> &mut MnistBuilder<'a> {
self.lbl_format = LabelFormat::OneHotVector;
self
}
/// Set the training set length.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .training_set_length(40_000)
/// .finalize();
/// ```
pub fn training_set_length(&mut self, length: u32) -> &mut MnistBuilder<'a> {
self.trn_len = length;
self
}
/// Set the validation set length.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .validation_set_length(10_000)
/// .finalize();
/// ```
pub fn validation_set_length(&mut self, length: u32) -> &mut MnistBuilder<'a> {
self.val_len = length;
self
}
/// Set the test set length.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .test_set_length(10_000)
/// .finalize();
/// ```
pub fn test_set_length(&mut self, length: u32) -> &mut MnistBuilder<'a> {
self.tst_len = length;
self
}
/// Set the base path to look for the MNIST data files.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .base_path("data_sets/mnist")
/// .finalize();
/// ```
pub fn base_path(&mut self, base_path: &'a str) -> &mut MnistBuilder<'a> {
self.base_path = base_path;
self
}
/// Set the training images data set filename.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .training_images_filename("training_images")
/// .finalize();
/// ```
pub fn training_images_filename(&mut self, trn_img_filename: &'a str) -> &mut MnistBuilder<'a> {
self.trn_img_filename = trn_img_filename;
self
}
/// Set the training labels data set filename.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .training_labels_filename("training_labels")
/// .finalize();
/// ```
pub fn training_labels_filename(&mut self, trn_lbl_filename: &'a str) -> &mut MnistBuilder<'a> {
self.trn_lbl_filename = trn_lbl_filename;
self
}
/// Set the test images data set filename.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .test_images_filename("test_images")
/// .finalize();
/// ```
pub fn test_images_filename(&mut self, tst_img_filename: &'a str) -> &mut MnistBuilder<'a> {
self.tst_img_filename = tst_img_filename;
self
}
/// Set the test labels data set filename.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .test_labels_filename("test_labels")
/// .finalize();
/// ```
pub fn test_labels_filename(&mut self, tst_lbl_filename: &'a str) -> &mut MnistBuilder<'a> {
self.tst_lbl_filename = tst_lbl_filename;
self
}
// #[cfg(feature = "download")]
/// Download and extract MNIST dataset if not present.
///
/// If archives are already present, they will not be downloaded.
///
/// If datasets are already present, they will not be extracted.
///
/// Note that this requires the 'download' feature to be enabled
/// (disabled by default).
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .download_and_extract()
/// .finalize();
/// ```
pub fn download_and_extract(&mut self) -> &mut MnistBuilder<'a> {
self.download_and_extract = true;
self
}
/// Uses the Fashion MNIST dataset rather than the original
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .use_fashion_data()
/// .finalize();
/// ```
pub fn use_fashion_data(&mut self) -> &mut MnistBuilder<'a> {
self.use_fashion_data = true;
self
}
/// Download the .gz files from the specified url rather than the standard one
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .base_url("<desired_url_here>")
/// .download_and_extract()
/// .finalize();
/// ```
pub fn base_url(&mut self, base_url: &'a str) -> &mut MnistBuilder<'a> {
self.base_url = base_url;
self
}
/// Get the data according to the specified configuration.
///
///
/// # Examples
/// ```rust,no_run
/// # use mnist::MnistBuilder;
/// let mnist = MnistBuilder::new()
/// .finalize();
/// ```
///
/// # Panics
/// If `trn_len + val_len + tst_len > 70,000`.
pub fn finalize(&self) -> Mnist {
if self.download_and_extract {
let base_url = if self.use_fashion_data {
FASHION_BASE_URL
} else if self.base_url != BASE_URL {
self.base_url
} else {
BASE_URL
};
#[cfg(feature = "download")]
download::download_and_extract(base_url, &self.base_path, self.use_fashion_data)
.unwrap();
#[cfg(not(feature = "download"))]
{
println!("WARNING: Download disabled.");
println!(" Please use the mnist crate's 'download' feature to enable.");
}
}
let &MnistBuilder {
trn_len,
val_len,
tst_len,
..
} = self;
let (trn_len, val_len, tst_len) = (trn_len as usize, val_len as usize, tst_len as usize);
let total_length = trn_len + val_len + tst_len;
let available_length = (TRN_LEN + TST_LEN) as usize;
assert!(
total_length <= available_length,
format!(
"Total data set length ({}) greater than maximum possible length ({}).",
total_length, available_length
)
);
let mut trn_img = images(
&Path::new(self.base_path).join(self.trn_img_filename),
TRN_LEN,
);
let mut trn_lbl = labels(
&Path::new(self.base_path).join(self.trn_lbl_filename),
TRN_LEN,
);
let mut tst_img = images(
&Path::new(self.base_path).join(self.tst_img_filename),
TST_LEN,
);
let mut tst_lbl = labels(
&Path::new(self.base_path).join(self.tst_lbl_filename),
TST_LEN,
);
trn_img.append(&mut tst_img);
trn_lbl.append(&mut tst_lbl);
let mut val_img = trn_img.split_off(trn_len * ROWS * COLS);
let mut val_lbl = trn_lbl.split_off(trn_len);
let mut tst_img = val_img.split_off(val_len * ROWS * COLS);
let mut tst_lbl = val_lbl.split_off(val_len);
tst_img.split_off(tst_len * ROWS * COLS);
tst_lbl.split_off(tst_len);
if self.lbl_format == LabelFormat::OneHotVector {
fn digit2one_hot(v: Vec<u8>) -> Vec<u8> {
v.iter()
.map(|&i| {
let mut v = vec![0; CLASSES as usize];
v[i as usize] = 1;
v
})
.flatten()
.collect()
}
trn_lbl = digit2one_hot(trn_lbl);
val_lbl = digit2one_hot(val_lbl);
tst_lbl = digit2one_hot(tst_lbl);
}
Mnist {
trn_img,
trn_lbl,
val_img,
val_lbl,
tst_img,
tst_lbl,
}
}
}
impl Default for MnistBuilder<'_> {
fn default() -> Self {
Self::new()
}
}
impl Mnist {
/// Create a `NormalizedMnist` by consuming an `Mnist`.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::{MnistBuilder, NormalizedMnist};
/// let normalized_mnist: NormalizedMnist = MnistBuilder::new()
/// .finalize()
/// .normalize();
/// ```
pub fn normalize(self) -> NormalizedMnist {
NormalizedMnist::new(self)
}
}
#[derive(Debug)]
/// Struct containing (normalized) image and label vectors for the training, validation, and test sets.
pub struct NormalizedMnist {
pub trn_img: Vec<f32>,
pub trn_lbl: Vec<u8>,
pub val_img: Vec<f32>,
pub val_lbl: Vec<u8>,
pub tst_img: Vec<f32>,
pub tst_lbl: Vec<u8>,
}
impl NormalizedMnist {
/// Create a `NormalizedMnist` by consuming an `Mnist`.
///
/// # Examples
/// ```rust,no_run
/// # use mnist::{MnistBuilder, NormalizedMnist};
/// let normalized_mnist: NormalizedMnist = MnistBuilder::new()
/// .finalize()
/// .normalize();
/// ```
pub fn new(mnist: Mnist) -> NormalizedMnist {
NormalizedMnist {
trn_img: normalize_vector(&mnist.trn_img),
trn_lbl: mnist.trn_lbl,
val_img: normalize_vector(&mnist.val_img),
val_lbl: mnist.val_lbl,
tst_img: normalize_vector(&mnist.tst_img),
tst_lbl: mnist.tst_lbl,
}
}
}
/// Normalize a vector of bytes as 32-bit floats.
fn normalize_vector(v: &[u8]) -> Vec<f32> {
v.iter().map(|&pixel| (pixel as f32) / 255.0_f32).collect()
}
#[derive(Debug, PartialEq)]
enum LabelFormat {
Digit,
OneHotVector,
}
fn labels(path: &Path, expected_length: u32) -> Vec<u8> {
let mut file =
File::open(path).unwrap_or_else(|_| panic!("Unable to find path to labels at {:?}.", path));
let magic_number = file
.read_u32::<BigEndian>()
.unwrap_or_else(|_| panic!("Unable to read magic number from {:?}.", path));
assert!(
LBL_MAGIC_NUMBER == magic_number,
format!(
"Expected magic number {} got {}.",
LBL_MAGIC_NUMBER, magic_number
)
);
let length = file
.read_u32::<BigEndian>()
.unwrap_or_else(|_| panic!("Unable to length from {:?}.", path));
assert!(
expected_length == length,
format!(
"Expected data set length of {} got {}.",
expected_length, length
)
);
file.bytes().map(|b| b.unwrap()).collect()
}
fn images(path: &Path, expected_length: u32) -> Vec<u8> {
// Read whole file in memory
let mut content: Vec<u8> = Vec::new();
let mut file = {
let mut fh = File::open(path)
.unwrap_or_else(|_| panic!("Unable to find path to images at {:?}.", path));
let _ = fh
.read_to_end(&mut content)
.unwrap_or_else(|_| panic!("Unable to read whole file in memory ({})", path.display()));
// The read_u32() method, coming from the byteorder crate's ReadBytesExt trait, cannot be
// used with a `Vec` directly, it requires a slice.
&content[..]
};
let magic_number = file
.read_u32::<BigEndian>()
.unwrap_or_else(|_| panic!("Unable to read magic number from {:?}.", path));
assert!(
IMG_MAGIC_NUMBER == magic_number,
format!(
"Expected magic number {} got {}.",
IMG_MAGIC_NUMBER, magic_number
)
);
let length = file
.read_u32::<BigEndian>()
.unwrap_or_else(|_| panic!("Unable to length from {:?}.", path));
assert!(
expected_length == length,
format!(
"Expected data set length of {} got {}.",
expected_length, length
)
);
let rows = file
.read_u32::<BigEndian>()
.unwrap_or_else(|_| panic!("Unable to number of rows from {:?}.", path))
as usize;
assert!(
ROWS == rows,
format!("Expected rows length of {} got {}.", ROWS, rows)
);
let cols = file
.read_u32::<BigEndian>()
.unwrap_or_else(|_| panic!("Unable to number of columns from {:?}.", path))
as usize;
assert!(
COLS == cols,
format!("Expected cols length of {} got {}.", COLS, cols)
);
// Convert `file` from a Vec to a slice.
file.to_vec()
}