#Adaptive Learning Rate Algorithm (AdLR)
This is an adaptive learning rate algorithm for Convolutional Neural Networks training, presented on 18th International Conference on Engineering Applications of Neural Networks 2017 (EANN 2017). The algorithm's code has take some modifications in comparison with the prototype one, making it more stable. The gamma_1, gamma_2 and gamma_3 parameters values are fixed according to the paper. The algorithm is implemented with the help of the Caffe framework.
Please cite AdLR in your publications if it helps your research:
@Inbook{Georgakopoulos2017,
author="Georgakopoulos, S. V.
and Plagianakos, V. P.",
title="A Novel Adaptive Learning Rate Algorithm for Convolutional Neural Network Training",
bookTitle="Engineering Applications of Neural Networks: 18th International Conference, EANN 2017, Athens, Greece, August 25--27, 2017, Proceedings",
year="2017",
publisher="Springer International Publishing",
address="Cham",
pages="327--336",
}
As well as the following:
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors.
Check out the project site for all the details like
- DIY Deep Learning for Vision with Caffe
- Tutorial Documentation
- BVLC reference models and the community model zoo
- Installation instructions
and step-by-step examples.
Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.
Happy brewing!
Caffe is released under the BSD 2-Clause license. The BVLC reference models are released for unrestricted use.
Please cite Caffe in your publications if it helps your research:
@article{jia2014caffe,
Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
Journal = {arXiv preprint arXiv:1408.5093},
Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
Year = {2014}
}