关键词: 深度卷积神经网络, CUDA-cuDNN方法, 批量正则化, 图像分类, 深度学习

Abstract: In order to solve large amount of images classification issues, a method is introduced by combining with CUDA-cuDNN and Deep Convolutional Neural Network（DCNN）. This method makes advantages of DCNNs to learn features automatically, which makes up the incapability of hand-crafted features. Meanwhile, a cuDNN parallel computing method based on CUDA is employed to improve the speed of training and validation. DCNN is susceptible to parameter perturbation, which employs Batch Normalization（BN） to enhance the robustness. Experimental results indicate that the proposed method not only reduces training time substantially and accelerates validation speed, but also obtains lower classification error rate.

Key words: Deep Convolutional Neural Network（DCNN）, CUDA-cuDNN, batch normalization, image classification, deep learning