octave:1> load('') Įach digit can be visualized in Octave very easily. The result is a file with a size of roughly 15MB.įinally, this file can be loaded via the load function in Octave. Because the size of the file is roughly 120MB and Octave can also handle gzip compressed files we compress the text file with the command gzip mnist.txt to reduce its size. The result is written into the file mnist.txt in the current working directory. Now, the program can be build and executed as follows: You can also download the sources directly: git clone blogdata/mnist2octave/ Let s = train_x.to_octave_string('trainX') + Let (test_x, test_y) = MnistDigits::default_test_set().unwrap() Let (train_x, train_y) = MnistDigits::default_training_set().unwrap() Put the following content into the file main.rs which is located in the directory src: extern crate rustml If Rust was installed successfully we create a new project with cargo:Īppend the following two lines (which specify the required dependencies and where to search for them) to Cargo.toml which is located in the directory created by the command above. # add the rust directory to the search path # execute the install script with the destination directory Tar xzf ~/Downloads/rust-VERSION-x86_Ĭd rust-VERSION-x86_64-unknown-linux-gnu/ # unpack the archive (the name of the archive may depend on your system configuration) Skip the instructions in the box below if Rust is already installed. If not already installed, download the latest version from and execute the following commands on the command line. Converting the datasetīefore you can perform the following steps you have to install the Rust programming language. The installer script will download the datasets into the directory ~/.rustml/. As described here we execute the following commands on the command line: # download the installer script Download the datasetįirst, we need to download the original dataset. īecause the database from the link above is in a format that cannot be directly processed with Octave we will use rustml to convert it into a format that can be read with Octave. Each digit is represented as a grayscale image each with a width and height of 28 pixels. It contains 60,000 labeled training examples of handwritten digits between 0 and 9 (both including) and 10,000 labeled examples for testing. The MNIST database of handwritten digits (see here) is a very popular dataset used by the machine learning research community for testing the performance of classification algorithms. By this post you will see how to convert the MNIST database of handwritten digits via rustml into a format that can be read with Octave.

Gnu octave pce function