convolutional neural network filters

convolutional Neural Network Primer (1) Original address : http://blog.csdn.net/hjimce/article/details/47323463 Author : HJIMCE convolutional Neural Network algorithm is an n-year-old algorithm, only in recent years because of dee

Deep learning veteran Yann LeCun detailed convolutional neural network The author of this article: Li Zun 2016-08-23 18:39 This article co-compiles: Blake, Ms Fenny Gao Lei Feng Net (public number: Lei Feng net) Note: convolutional Neural Networks

Welcome reprint, Reprint Please specify: This article from Bin column Blog.csdn.net/xbinworld.Technical Exchange QQ Group: 433250724, Welcome to the algorithm, technology interested students to join.Recently, the next few posts will go back to the discussion of neural network structure, before I in "deep learning Method (V): convolutional

, STL-10), 224 (for example, ImageNet convolutional Neural Network), 384, and 512. Convolution LayerSmall-size filters (such as 3x3 or a maximum of 5x5) should be used, with step size S = 1. It is also very important to fill the input data with zero fill, so that the convolution layer will not change the size of the in

improvement extended to V4: The parameter space is large, easy to fit, and the training data set is limited; Complex network structure, insufficient computing resources, resulting in difficult to apply; The deep network structure is prone to gradient dispersion, and the model performance decreases. 2. InceptionGooglenet has modified the traditional

CNN (convolutional neural Network)Convolutional Neural Networks (CNN) dating back to the the 1960s, Hubel and others through the study of the cat's visual cortex cells show that the brain's access to information from the outside world is stimulated by a multi-layered recepti

After figuring out the fundamentals of convolutional Neural Networks (CNN), in this post we will discuss the algorithm implementation techniques based on Theano. We will also use mnist handwritten numeral recognition as an example to create a convolutional neural network (CN

Preface This article first introduces the build model, and then focuses on the generation of the generative Models in the build-up model (generative Adversarial Network) research and development. According to Gan main thesis, gan applied paper and gan related papers, the author sorted out 45 papers in recent two years, focused on combing the links and differences between the main papers, and revealing the research context of the generative antagoni

, each location by 0 or 1 classification (to determine whether the interception of the image of the object to be detected). Select a larger window to repeat the above actions.Sliding window for convolution (improvements to the algorithm above)Convert the fully connected layer into a convolution layer:Principle: The video is that, from a mathematical point of view, the conversion of the convolution layer and the full join layer, each node in 400 nodes have a 5x5x16 dimension of the filter, these

convolutional Neural Network (convolutional neural network,cnn), weighted sharing (weight sharing) network structure reduces the complexity of the model and reduces the number of weight

picture is (n-f + 1) x (n-f + 1) x n ' C n ' c is the number of filters7.One layer of a convolutional network The single-layer structure of convolutional neural networks is as follows: The parameters in the total (3x3x3+1) x2, the number of parameters independent of the size of the picture, only with the filter Summa

1. Target positioning 1.1 Introduction to classification, positioning and testing -Image classificationImage classification, is to give you a picture, you determine the target category, such as cars, cats and so on.-Classification with localizationPositioning classification, not only to determine the target category, but also to output the position of the target object, such as the box up.-DetectionDetection, there may be multiple objects in the picture, you need to find them out. 1.2 Position

The accuracy of the mnist test set is about 90% and 96%, respectively, for single-layer neural networks and multilayer neural networks in the previous two essays. The correct rate has been greatly improved after the multi-layer neural network has been swapped. This time the convolu