alexnet

Squeezenet from 2016 theses squeezenet:alexnet-level accuracy with 50X fewer PARAMETERS and Squeezenet mainly presents the concept of Firemodule, as shown in the above picture, a firemodule consists of a squeeze and a expand, squeeze contains the convolution nucleus of S 1*1, expand contains the e1 of the convolution kernel, 1*1 3 *3, and satisfies the s After such a substitution, the model is reduced by about 50 times times, while ensuring accuracy. Test program: typedef std::p air Model S

Original URL: http://blog.csdn.net/sunbaigui/article/details/39938097 On the Imagenet Image Classification challenge the Alexnet network structure model which Alex proposed has won the 2012 championship. To study the application of the CNN type DL network model to the image classification, we can't escape the research alexnet, which is CNN's classic model on image classification (after DL fire). In the mo

On the Imagenet Image Classification Challenge, Alex proposed the Alexnet network structure model won the 2012-term championship. In order to study the application of the CNN type DL network model in image classification, we can not escape the research alexnet, which is the classic model of CNN in image classification (after the DL fires up).In the DL open source Implementation Caffe Model sample, it also g

In 2012, Geoffrey and his student Alex, in order to respond to the doubters, in the imagenet contest shot, refreshing the imageclassification record, laid a deep learning in computer vision status. The story behind us all know, deeplearning eminence, invincible. The structure Alex used in this competition is known as alexnet. In this part, we first introduce the basic architecture of alexnet, and then analy

so, a single classifier often can not accurately describe a sub-interface, then we combine, each calculation. From the methodological point of view, for things are often local, so will make the wrong to generalize, if you can get the "partial" ensambling, then the relative "full", so that the larger probability of approximation to the overall distribution. This thought is manifested in many aspects, such as cross-validation, classical Ransac,random Tree (forest), Adaboost and other methods.Here

/ classifier123 " param { lr_mult:1 decay_mult:1 } param { lr_mult:2 decay_mult:0 } Inner_product_param { num_output:2# originally was weight_filler { type: ' Xavier ' } bias_filler { type: "Constant" value:0 } }}layer { name: "Prob" type: "Softmax" Bottom: " Loss3/classifier123 " top:" Prob "}3.2.5 Run F:\caffe-master170309\train-TrafficJamBigData03301009.bat file and start training1200 Images (1200 training + 200 tests), iteration 50,000 time

], y:mnist.test.labels[:256], keep_prob:1.})View CodeAlexNet:Won the 2012-year champion of the ILSVRC Competition classification project (top-5 error rate 16.4%, using additional data to reach 15.3%, 8 layer neural Network 3 conv+2 pooling+3 FC)The first successful application of Relu dropout and LRN trick in CNN:Successfully use Relu as the activation function of CNN, and verify its effect in the deeper to the network over the sigmoid, successfully solve the sigmoid in the deep network to the g

First, IntroductionAlexnet the last 2 fully connected layers are used with dropout because the fully connected layer is easy to fit, and the convolution layer is not easy to fit.1. Randomly delete some hidden neurons in the network, keep the input and output neurons unchanged;2. Forward propagation of the input through the modified network, and then reverse propagation of the error through the modified network;3. Repeat the above operation for another batch of training samples 1Second, the funct

First, IntroductionUsing ReLU instead of the sigmoid activation function in alexnet, it is found that the convergence rate of SGD obtained using ReLU is much faster than Sigmoid/tanhSecond, the role1.sigmoid and Tanh have saturation zone, Relu at x>0 time derivative is always 1, help to alleviate the gradient disappear, thus speeding up the training speed2. Whether it is forward propagation or reverse propagation, the computational amount is significa

First, IntroductionPartial response normalization of LRNLRN is used for results after convolution and pooling. Due to the use of multiple convolution cores, the resulting feature map has multiple "channels".The direction of the summation is the

More than two weeks of efforts to finally write out the code of RCNN, this code is very interesting, and incidentally reviewed a few tensorflow application of knowledge points, so summarize, take everyone to share the experience. Theoretically, there are a lot of theoretical tutorials in rcnn, here I do not elaborate, interested friends can look at this blog to understand the approximate.System OverviewThe logic of RCNN is based on the alexnet model.

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 Vg

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

Original linkDeep Neural Network (DNN) training is a computationally intensive project that takes days or weeks to complete on a modern computing platform. In a recent article on Intel? Xeon? In single-node Caffe scoring and training for the E5 product family, we demonstrated a 10 times-fold performance improvement in the caffe* framework based on the AlexNet topology and reduced the single-node training time to 5 days. Intel continues to fulfill the

OverviewAlthough the CNN deep convolution network in the field of image recognition has achieved significant results, but so far people to why CNN can achieve such a good effect is unable to explain, and can not put forward an effective network promotion strategy. Using the method of Deconvolution visualization in this paper, the author discovers some problems of alexnet, and makes some improvements on the basis of

recognition> The method of selective search, using hierarchical clustering, generates a specified number of candidate regions that are most likely to contain a target. Similarly, this article is also working in this regard, It is proposed to use CNN to generate candidate regions, and named "Deepmultibox";The second step: Using CNN to classify the generated candidate regions ; After generating the candidate regions, extracting the features and then classifying them by using classifiers to achiev

evolution of deep neural networks in image recognition applications"Minibatch" You use a data point to calculate to modify the network, may be very unstable, because you this point of the lable may be wrong. At this point you may need a Minibatch method that averages the results of a batch of data and modifies it in their direction. During the modification process, the change intensity (learning rate) can be adjusted. At the beginning of the time, not in doubt to learn quickly a slow, slowly hav

Directory I. Basic knowledge Ii. Early attempts 1. Neocognitron, 1980 2. LeCun, 1989 A. Overview B. Feature Maps Weight Sharing C. Network Design D. Experiments 3. LeNet, 1998 Iii. Historic Breakthrough: AlexNet, 2012 1. Historic 2. The difficult point 3. Select CNN 4. This article contributes 5. Network Design