sL_QN_TR

Stochastic Quasi-Newton Methods in a Trust Region Framework (MATLAB Implementation)

The repository "sL_QN_TR" contains programs of s-LBFGS-TR and s-LSR1-TR which are stochastic algorithms based on two well-known quasi-Newton updates, i.e., limited memory BFGS and limited memory SR1, in a Trust Region Framework. These algorithms and their performance in training deep neural networks for image classification tasks are described in the following article:

Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods

Mahsa Yousefi and Angeles Martinez Calomardo

Download and Read the Paper.

Read the supplementary material of the paper for more numerical results.

A MATLAB-based tutorial on implementing training loops for a deep neural network

Mahsa Yousefi and Angeles Martinez Calomardo

This tutorial shows how to define a convolutional neural network (CNN) and how to create and customize your training loops. If you are a MATLAB user who would like to implement your training algorithm for which the MATLAB built-in function does not exist, read this tutorial.

Download and Read the Tutorial.

In this repository,

• all programs have been written in MATLAB using the DL toolbox.
• there are four main programs as below:
  • sL_BFGS_TR.mlx
  • sL_SR1_TR.mlx
  • sL_BFGS_TR_noBN.mlx
  • sL_SR1_TR_noBN.mlx
• every single program in .mlx format (live script of MATLAB) provides a step-by-step guideline for users.
• the folder Subroutines includes required functions (.m files) for running programs.
• the folder Datasets consists of three folders associated with standard benchmarks MNIST, Fashion-MNIST, and CIFAR10 with their loading file.
• three considered main architectures with and without batch normalization layers are:
  • LeNet-like
  • ResNet-20
  • ConvNet3FC2
• the networks without batch normalization layers are included in sL_BFGS_TR_noBN.mlx and sL_SR1_TR_noBN.mlx programs.

Remark: For executing, put together main programs with all .m files and required files from Subroutines and Datasets, respectively.

Please contact me if you have any questions, suggestions, requests, or bug reports.

mahsa.yousefi@phd.units.it