resnet

design purpose of residual networkWith the increase of network depth, there will be a degradation problem, that is, when the network becomes more and more deep, the accuracy of training will tend to moderate, but the training error will become larger, which is obviously not over-fitting, because over-fitting means that the network training error will continue to be small, but the test error will become larger. To address this degradation, ResNet was p

is [A1, A2, A3], the value of Begin is [B1, B2, b3],size value is [S1, S2, S3], then Tf.slice () The value returned is INPUT[B1: B1+S1, B2:b2+s2, B3:B3+S3].If si=?1, then the return value is input[b1:b1+s1,..., bi:,...]Tf.contrib.framework.get_or_create_global_step () Gets the current model training to reach the global steps.Bottleneck residual error module allows the residual network to go deeper, because the same channel number, the bottleneck residual module to save a lot of parameters than

Deep residual Learning for Image recognition this paper is famous After reading the views of everyone http://www.jianshu.com/p/e58437f39f65, also want to talk about their reading after the understanding Network depth is a major factor affecting the performance of deep convolution neural networks, but the researchers found that when the network deepened, the results of the training were not good. This is not because of the fitting, because the fitting words should be the result of the training

First, the structureSecond, the roleAs the network continues to deepen, the effect on the training set decreases, and this is not caused by overfitting, because overfitting results in a good effect on the training set. By introducing identity

ResNet, AlexNet, Vgg, Inception: Understanding the various CNN architecturesThis article is translated from ResNet, AlexNet, Vgg, inception:understanding various architectures of convolutional Networks, original author retains copyrightConvolution neural network is an amazing performance in visual recognition task. A good CNN network is a "pang monster" with millions of parameters and many hidden layers. In

Preface CIFAR-10 datasets are a common data set in the field of deep learning. The Cifar-10 consists of 60000 32*32 RGB color images, all of which include aircraft, cars, birds, fur, deer, dogs, frogs, horses, boats and trucks in 10 categories. 50000 training, 10000 tests. is often used as a classification task to evaluate the merits and demerits of deep learning frameworks and models. More well-known models such as Alexnet, NIN, ResNet, etc. have al

characteristics of each channel through a Scale operation. In addition, SE modules can be embedded in modules that contain skip-connections. The upper right diagram is an example of embedding SE in the ResNet module, which is essentially the same as se-inception, except that the feature of the residual on the branch is calibrated before the addition. If the feature on the rear branch of addition is calibrated, because of the 0~1 scale operation in th

Image recognition is the mainstream application of deep learning today, and Keras is the easiest and most convenient deep learning framework for getting started, so you have to emphasize the speed of the image recognition and not grind it. This article allows you to break through five popular network structures in the shortest time, and quickly reach the forefront of image recognition technology. Author | Adrian RosebrockTranslator | Guo HongguangEdit | PigeonsTranslation Address: https://c

(factorization), the masterpiece is Inceptionv3 version of Googlenet.The highlights of Inception V3 are summarized below:(1) Decomposition of the 7*7 into two one-dimensional convolution (1*7,7*1), 3*3 is the same (1*3,3*1), such a benefit, both can accelerate the calculation (redundant computing power can be used to deepen the network), and 1 conv can be split into 2 conv, so that the network depth is further increased, It increases the nonlinearity of the network and designs the 35*35/17*17/8

Paper: Non-local Neural Networks for Video classificationPaper Link: https://arxiv.org/abs/1711.07971Code Link: https://github.com/facebookresearch/video-nonlocal-net The official code is based on the CAFFE2 implementation, this blog introduces the project's main code, through the code to deepen the understanding of the algorithm.Suppose ~video-nonlocal-net is a project directory pulled down from Https://github.com/facebookresearch/video-nonlocal-net. Because the code is based on the video class

CNN began in the 90 's lenet, the early 21st century silent 10 years, until 12 Alexnet began again the second spring, from the ZF net to Vgg,googlenet to ResNet and the recent densenet, the network is more and more deep, architecture more and more complex, The method of vanishing gradient disappears in reverse propagation is also becoming more and more ingenious.　　　　 LeNet AlexNet Zf Vgg Googlenet

second 11%. From this deep learning to fame Sparrow.2. EvolutionSince 2012, the depth of the network has been increasing year in, is the ILSVRC competition champion's Network layer trend chart:In 2014, Vgg and Googlenet reached 19 and 22, respectively, and the accuracy was also increased by an unprecedented level. By 2015, Highway Networks reported that 900 layers could converge. Microsoft Research launched the ResNet, so that network depth of 152-la

has surpassed the human eye. The models in Figure 1 are also a landmark representation of the deep learning vision development.Figure 1. ILSVRC Top-5 Error rate over the yearsBefore we look at the model structures in Figure 1, we need to look at one of the deep-learning Troika ———— LeCun's lenet network structure. Why to mention LeCun and lenet, because now visually these artifacts are based on convolutional neural Network (CNN), and LeCun is CNN Huang, Lenet is lecun to create the CNN Classic.

, so people found that can extend the information of the transmission path and form, such as: Inception series starting from the module, based on each module to establish a number of different channels, and then connect the module, but from the overall perspective of the model is also a road; The ResNet series connects the output of different layers directly to the input of the back layer through the way of quick connection, so tha

difficult to train. (Alexnet only 5 convolution layers,)Because the gradient slows down and the gradient disappears the phenomenon becomes very serious, because the gradient propagates to the earlier layer, the repetition multiplication may make the gradient infinitely big. As a result, as the network deepened, its performance saturation even began to deteriorate rapidly.To solve this problem, Ms tries to construct a shortcut (shortcut connections) to pass the gradient.ResNet is not the first t

Recently in the task of doing a classification, the input is a 3-channel model picture, the output requires these images to classify the model, the final category of the total is 30.The beginning is a trial of the laboratory of the vggnet model of the models to classify models, according to the experimental results before the training can reach the highest 92% of the correct rate, after the use of imagenet trained datalayer, can achieve 97% of the correct rate, Since I didn't run the test for a

', batch_size = batch_size, data_shape = Data_shape, Shuffle = True, rand_crop = True, Rand_mirror = True) val = mx.io.ImageRecordIter ( path_imgrec = './ld_train/my_images_val.rec ', data_name = ' data ', label_name = ' Softmax_label ', batch_size = batch_size, Data_shape = data_shape, rand_crop = Fals

advantage of a smaller filter is to increase the depth of the network, increasing the nonlinearity and less parameters.3. Googlenet (Szegedy et al., 2014)The Inception module uses different filter (1*1,3*3,5*5,pooling) at the same time and stacks up the results. The disadvantage of this is that the computational changes are large. The solution is to first use the 1*1 convolution compression channel number (refer to "Deeplearning.ai convolutional neural network Week 2 Lectures notes").4.

1 Introduction The process of Alphago Zero (hereinafter referred to as zero) is shown in Figure A, B, in each state s, through MCTs search, to obtain the probability p of each possible move, where MCTs search adopts Self-play and executes the fθ strategy. Fθ mainly uses Microsoft's ResNet, that is, based on the residual learning. After using MCTs to obtain the probability p of each possible move, update the fθ weight. Finally, use this fθ to evaluate

training is that the discriminant focuses only on the most recent refined images, which can cause two problems-the dispersion of confrontation training and the introduction of the refiner network to the artifacts that the discriminant has long forgotten. The classifier is therefore updated by using refined's historical picture as a buffer rather than just the current mini-batch. The specific method is that in each round of classifier training, we first sampled from the current batch B/2 picture