自适应深度卷积神经网络模型构建方法

针对传统卷积神经网络(CNN)模型构建过度依赖经验知识、参数多、训练难度大等缺点,同时鉴于复杂多类问题的CNN模型构建策略的重要价值,提出一种自适应深度CNN模型构建方法.首先,将初始网络模型的卷积层和池化层设置为仅含一幅特征图;然后,以网络收敛速度为评价指标,对网络进行全局扩展,全局扩展后,根据交叉验证样本识别率控制网络展开局部扩展,直到识别率达到预设期望值后停止局部网络学习;最后,针对新增训练样本,通过拓展新支路实现网络结构的自适应增量学习.通过图像识别实验验证了所提算法在网络训练时间和识别效果上的优越性.

Construction Method of Adaptive Deep Convolutional Neural Network Model

The construction process of traditional convolutional neural network (CNN) model has many shortcomings,such as over reliance on experience knowledge,a lot of parameters and difficult to training.At the same time,in view of the important value of constructing strategy of CNN model in complex multi-class problems,a new construction method of adaptive deep CNN model was proposed.First,the convolution layer and pooling layer of the initial CNN model are set only to include one feature map;and then,the convergence rate of CNN is used as evaluation index,the global expansion of network is carried out.After global expansion,the CNN is controlled to local expansion according to the recognition rate of cross validation samples.The local network learning is stopped until the recognition rate reaches the expected value.Finally,the training process for new samples,the adaptive incremental learning of network structure is realized by expanding some new branches.The superiority of proposed algorithm in network training time and recognition effect is verified through some image recognition experiments.