Parametric Learning of Deep Convolutional Neural Network
Rui Zhong, Taro Tezuka · 2014
Deep neural networks have recently been showing great potential on visual recognition tasks. However, it is also considered difficult to tune its parameters, and it has high training cost. This work focuses on analysis of several learning methods and properties of multinomial logistic regression deep convolutional network. We implemented a scalable deep neural network, compared the efficiency of different methods and how parameters affect the learning process. We propose an efficient method of performing back-propagation with limited kernel functions on GPU and achieved better efficiency. Our conclusions can be applied to train deep networks more efficiently. We achieved the recognition rate of over 0.95 without image preprocessing and fine tuning, within 10 minutes on a single machine.