Resnets, highwaynets, densenets using tensorflow to achieve ultra deep neural network – Sohu Technol pppd-175

ResNets, HighwayNets, DenseNets: the realization of ultra deep neural network – 1 new technology Sohu Zhi Yuan compiled by TensorFlow: Arthur Juliani source: chatbotslife compiler: Liu Xiaoqin Zhi Yuan to start a new round of recruitment: COO, executive editor, senior editor, compiler, pen, operation director, customer manager, consulting and administrative director assistant 9 position overall opening. Resume: jobs@aiera HR WeChat: new Zhi Yuan COO and executive editor of the highest salary offer over 1 million; to provide the most complete training system, higher than the industry average wages and bonuses for the backbone of the staff. Join the new wisdom yuan, and artificial intelligence industry leaders to join hands to change the world. [introduction] new Zhi Yuan in many tasks, the neural network is more deep, the better the performance. In recent years, the trend of neural network is getting deeper and deeper. A few years ago the most advanced neural network is only 12 deep, and now hundreds of layers of deep neural network is not a strange thing. In this paper, we introduce three very deep neural networks, namely ResNet, HighwayNet and DenseNet, and their implementation on Tensorflow. The authors use the CIFAR10 data set to train these networks for image classification, and achieve more than 90% accuracy in an hour or so. The trend of neural network design: when Deeper comes to the design of neural networks, the trend in recent years has pointed to one direction: deeper. A few years ago the most advanced neural network is only 12 deep, and now hundreds of layers of deep neural network is not a strange thing. For many applications, the deeper the neural network, the better the performance, which has the most significant impact on object classification tasks. Of course, the premise is that they can be properly trained. In this article, I will describe the logic behind three recent deep learning networks, namely ResNet, HighwayNet and DenseNet. They can overcome the limitations of traditional network design, make the depth of the network easier to train. I will also provide code to implement these networks on Tensorflow. ImageNet contest winner’s network layer. More and more deep network trend is very obvious. Why simply deepening the network does not work? The first intuition design depth network may be simply a lot of basic building blocks (such as roll or laminated connecting layer stacked together). To some extent this can work, but as the traditional network becomes deeper, the network performance will decline rapidly. This is because the neural network is trained in a reverse direction. In training, the gradient signal must be propagated back from the top layer of the network to the lowest level to ensure that the network itself can be updated correctly. In traditional networks, when the gradient signal passes through each layer of the network, the gradient will decrease slightly. For only a few layers of the network, this is not a problem. But for a network of dozens of layers, when the signal finally arrives.相关的主题文章: