基于组合ResNet和InceptionNet的神经网络分类研究

Research on Neural Network Classification Based on Combined ResNet and InceptionNet

针对基础的图像分类问题,我们分别使用主流的卷积神经网络进行训练,之后将ResNet网络和InceptionNet网络模块化,提取出ResNetBlock和InceptionBlock,建立新的卷积神经网络组合两个独立模块,并使用一些列的调参方法,和经典的卷积神经网络进行比较,验证的识别率高于原来的卷积神经网络,并且损失函数能降至更低的水平。

For the basic image classification problem, we use the mainstream convolutional neural networks for training, and then modularize the ResNet network and the InceptionNet network, extract the ResNetBlock and InceptionBlock, build a new convolutional neural network to combine two independent modules, and use a series of parameter tuning methods, compared with the classic convolutional neural network, to verify that the recognition rate is higher than the original convolutional neural network, and the loss function can be reduced to a lower level.