MatDL: A Lightweight Deep Learning Library in MATLAB

Abstract

MatDL (Fayek 2017) is an open-source lightweight deep learning (LeCun, Bengio, and Hinton 2015; Goodfellow, Bengio, and Courville 2016) library native in MATLAB that implements some most commonly used deep learning algorithms. The library comprises functions that implement the following: (1) basic building blocks of modern neural networks such as affine transformations, convolutions, nonlinear operations, dropout, batch normalization, etc.; (2) popular architectures such as deep neural networks (DNNs), convolutional neural networks (ConvNets), and recurrent neural networks (RNNs) and their variant, the long short-term memory (LSTM) RNNs; (3) optimizers such stochastic gradient descent (SGD), RMSProp and ADAM; as well as (4) boilerplate functions for training, gradients checking, etc. Most of these functions can run on a CPU or a MATLABcompatible CUDA-enabled GPU. It is straight forward to use the low-level functions to experiment with or test new architectures or training algorithms, or alternatively use the provided models for applied deep learning research.