Recently sequential model based optimization (SMBO) has emerged as a promising hyper-parameter optimization strategy in machine learning. In this work, we investigate SMBO to identify architecture hyper-parameters of deep convolution networks (DCNs) object recognition. We propose a simple SMBO strategy that starts from a set of random initial DCN architectures to generate new architectures, which on training perform well on a given dataset. Using the proposed SMBO strategy we are able to identify a number of DCN architectures that produce results that are comparable to state-of-the-art results on object recognition benchmarks.
@article{arxiv.1501.07645,
title = {Hyper-parameter optimization of Deep Convolutional Networks for object recognition},
author = {Sachin S. Talathi},
journal= {arXiv preprint arXiv:1501.07645},
year = {2015}
}
Comments
4 pages, 1 figure, 3 tables, Submitted to ICIP 2015