alexnet in keras

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 Neural network CNN Classic model finishing Lenet,alexnet,googlenet,vgg,deep residual learning" The article describ

http://blog.csdn.net/diamonjoy_zone/article/details/70576775Reference:1. inception[V1]: going deeper with convolutions2. inception[V2]: Batch normalization:accelerating deep Network Training by reducing Internal covariate Shift3. inception[V3]: Rethinking the Inception Architecture for computer Vision4. inception[V4]: inception-v4, Inception-resnet and the Impact of residual Connections on learning1. PrefaceThe NIN presented in the previous article made a notable contribution to the transformati

correlated outputs are clustered to build an optimal network on a per-layer basis. This indicates that a bloated sparse network can be simplified without sacrificing performance. while mathematical proofs have strict conditionality, the Hebbian guidelines strongly support this: fire together,wire together.Earlier, in order to break the network symmetry and improve learning ability, the traditional network has used random sparse connection. However, the computational efficiency of the computer s

correlated outputs are clustered to build an optimal network on a per-layer basis. This indicates that a bloated sparse network can be simplified without sacrificing performance. while mathematical proofs have strict conditionality, the Hebbian guidelines strongly support this: fire together,wire together.Earlier, in order to break the network symmetry and improve learning ability, the traditional network has used random sparse connection. However, the computational efficiency of the computer s

Matlab section: xMAP = Repmat (Linspace (-REGIONW/2, REGIONW/2, regionw), Regionh, 1);%linspace [x1,x2,n] arithmetic progression ymap = Repmat (li Nspace (-REGIONH/2, REGIONH/2, Regionh) ', 1, regionw); % transpose%compute the angle of the vector p1-->p2vecp1p2 = labeldata (2,:)-labeldata (1,:); angle =-atan2 (VECP1P2 (2), VECP1P2 (1)); The% angle calculates the four-quadrant inverse tangent widthoftherealregion = norm (vecp1p2) + offset; % seek p1p2 point distance, open radical +offsetmidpoin

Squeezenet is in the thesis Iandola F N, Han S, Moskewicz M W, et al squeezenet:alexnet-level accuracy with 50x fewer parameters ANDL T 0.5 MB model Size[j]. ArXiv preprint arxiv:1602.07360, 2016. A network model which is not focused on improving the classification accuracy and reducing the model parameters is proposed in this paper. In general, the deeper the layer number of convolution neural network, the stronger the expressive ability, the better parameters and structure can be found to solv

proposed in this paper, E1=E3=4S1 Next look at squeezenet. Network structure: First after Conv1, then is fire2-9, finally is a conv10, finally uses the global Avgpool to replace the FC layer to carry on the output; The more detailed parameters are shown in the following figure: look at the contrast between Squeezenet and alexnet: The deep compression technology is used to compress the squeezenet, and the 0.5M model is finally obtained, and the model

core code are under $caffe/src/caffe, mainly in the following sections: NET, blob, layer, solver. Net.cpp:NET definition network, the entire network contains a lot of layers, net.cpp is responsible for the calculation of the entire network in the training of the forward, backward process, that is, Forward/backward layer. LayersLayer in $caffe/src/caffe/layers, define message type in Protobuffer (. proto file,. prototxt or. binaryproto file to define the value of message) Contains property name,

In the words of Russian MYC although is engaged in computer vision, but in school never contact neural network, let alone deep learning. When he was looking for a job, Deep learning was just beginning to get into people's eyes. But now if you are lucky enough to be interviewed by Myc, he will ask you this question Deep Learning why call Deep Learning. What's the difference between a regular machine learning? If you can't answer it, it doesn't matter, because as an engineer, we just know how to u

Ty of data with neural networks. [J]. Science, 2006, 313 (5786): 504-7. (Self-encoder dimensionality reduction) [PDF] Ng A. Sparse Autoencoder[j]. CS294A Lecture Notes, 2011, 72 (2011): 1-19. (Sparse self-encoder) [PDF] Vincent P, Larochelle H, Lajoie I, et al. stacked denoising Autoe Ncoders:learning useful representations in a deep network with a local denoising criterion[j]. Journal of machine Learning (DEC): 3371-3408. (Stacked self-encoder, SAE) [PDF] deep learning outbreaks: from

only 2 neural cells. and the input and output layers are called hidden layers (Hidden layer), the figure has 3 hidden layers, as mentioned earlier, the image classification of the hidden layer is done by convolution operations, so the hidden layer is also a convolutional layer (convolutional layer). Therefore, the structure of input layer, convolutional layer, output layer and its corresponding parameters constitute a typical convolutional neural network. Of course, the convolution neural netwo

Weight sharing the word was first introduced by the LENET5 model, in 1998, LeCun released the Lenet network architecture, which is the following:Although most of the talk now is that the 2012 Alexnet network is the beginning of deep learning, the beginning of CNN can be traced back to the LENET5 model, and its features are widely used in the study of convolutional neural networks in the early 2010--one of which is the sharing of weights. . In fact, th

unit uses C or Cuda to do a good job of optimization. Based on this, a common model is built using LUA. The disadvantage is that the interface is LUA language and takes a little time to learn. (5) Theano2008 was born in Montreal Polytechnic Institute, the main development language is Python. Theano derives a lot of deep learning python packages, most notably the blocks and Keras.Theano is very flexible, suitable for academic research experiments, and the recursive network and language modeling

Nets LeNet AlexNet Overfeat NIN Googlenet Inception-v1 Inception-v2 Inception-v3 Inception-v4 Inception-resnet-v2 ResNet 50 ResNet 101 ResNet 152 Vgg 16 Vgg 19 Note: Image from Github:tensorflow-slim image classification Library)Additional references: [ILSVRC] Image classification, positioning, detection based on overfeat[convolutional neural networks-evolutionary history]

and so on.In general, selective search is a relatively naïve area nomination method, which is widely used by the early method of target detection based on deep learning (including overfeat and R-CNN, etc.), but is deprecated by the current new method.2, OverfeatOVERFEAT[7][9] is a common use of CNN for classification, positioning and testing of the classic, the author is one of the deep learning of the great God ———— Yann LeCun in the New York University team. [10] Overfeat is also the winner o

the training and test samples are constructed according to these region proposal, noting that the size of these region proposal is different, and the sample category is 21 (including the background).Then there is the pre-training, which is trained with alexnet under the Imagenet data set. And then on our data set fine-tuning, the network structure is unchanged (except the last layer of output from 1000 to 21), the input is the front of the region pro

multi-branched convolutional neural network. The multi-branch network is initially visible in Google's inception structure.The cardinality is defined in the paper as the size of the conversion set. This definition may not be well understood, so let's take a look at the group convolution.The group convolution can be traced back to alexnet[6] as early as possible. Krizhevsky The purpose of using the group convolution is to distribute the model to two G

LRN (local Response normalization) partial response normalized notes This Note Records Learning LRN (local Response normalization), if there are errors, please criticize and learn to communicate. 1. Side inhibition (lateral inhibition) 2. Calculation Formula Hinton in the 2012 Alexnet network, its specific formula is given as follows: The formula looks more complicated, but it's very simple to understand. I represents the first kernel at positio

yarn spark. Each executor is assigned to a HDFS-based training data partition, and then multiple caffe-based training threads are opened. Each training thread is handled by a specific GPU. After a batch of training samples is processed using the reverse propagation algorithm, the gradient values of the model parameters are exchanged between the training threads. These gradient values are exchanged in MPI allreduce form between the GPU of multiple servers. We upgraded the Caffe to support the us

Cnn CV Tasks Classification Classification + Localization CLASSIFICATION:C classesInput:imageOutput:class LabelEvaluation Metric:accuracyLocalizationInput:imageOutput:box in the image (X,y,w,h)Evaluation Metric:intersection over Union method one: Positioning as a regression problem Mark Y value as box position, neural network output as box position, use L2 distance as loss functionSimple method:1. Download existing classified network alexnet or VGG ne