alexnet in keras

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

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 are the two aspects of data and models to learn some of the techniques in

Original page: Visualizing parts of convolutional neural Networks using Keras and CatsTranslation: convolutional neural network Combat (Visualization section)--using Keras to identify cats It is well known, that convolutional neural networks (CNNs or Convnets) has been the source of many major breakthroughs in The field of deep learning in the last few years, but they is rather unintuitive to reason on for

notesThe 1th and penultimate 2nd levels are as follows:The 1th floor is as follows:Name: "googlenet" layer { name: "Data" type: "Input" Top: "Data" Input_param {shape: {dim:10 Dim:3 Dim: 480 dim:480}}# Input_param {shape: {dim:10 dim:3 dim:224 dim:224}}}The penultimate layer is as follows:Layer { name: "loss3/classifier123" type: "Innerproduct" Bottom: "pool5/7x7_s1" Top: "loss3/ classifier123 " param { lr_mult:1 decay_mult:1 } param { lr_mult:2 decay_mult:0 } Inner_pr

ifStep% Display_step = =0: the #Calculation Accuracy theACC = sess.run (accuracy, FEED_DICT={X:BATCH_XS, Y:batch_ys, keep_prob:1.}) the #Calculate loss Value117Loss = Sess.run (cost, Feed_dict={x:batch_xs, Y:batch_ys, keep_prob:1.})118 Print "Iter"+ STR (step*batch_size) +", Minibatch loss="+"{:. 6f}". Format (loss) +", Training accuracy="+"{:. 5f}". Format (ACC)119Step + = 1 - 121 Print "optimization finished!"122 #Calculate test Accura

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

Developing a complex depth learning model using Keras + TensorFlow This post was last edited by Oner at 2017-5-25 19:37Question guide: 1. Why Choose Keras. 2. How to install Keras and TensorFlow as the back end. 3. What is the Keras sequence model? 4. How to use the Keras to

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

We strongly recommend that you pick either Keras or Pytorch. These is powerful tools that is enjoyable to learn and experiment with. We know them both from the teacher ' s and the student ' s perspective. Piotr have delivered corporate workshops on both, while Rafa? is currently learning them. (see the discussion on Hacker News and Reddit).IntroductionKeras and Pytorch is Open-source frameworks for deep learning gaining much popularity among data scie

Keras Introduction?? Keras is an open-source, high-level neural network API written by pure Python that can be based on TensorFlow, Theano, Mxnet, and CNTK. Keras is born to support rapid experimentation and can quickly turn your idea into a result. The Python version for Keras is: Python 2.7-3.6.??

Keras is a python library for deep learning that contains efficient numerical libraries Theano and TensorFlow. The purpose of this article is to learn how to load data from CSV and make it available for keras use, how to model the data of multi-class classification using neural network, and how to use Scikit-learn to evaluate Keras neural network models.Preface,

It is best to compare lasagne, keras, pylearn2, and nolearn. I have already selected theano for tensor and symbolic computing frameworks. Which of the above databases is better? First, the document should be as detailed as possible. Second, the architecture should be clear, and the Inheritance and call should be convenient. It is best to compare lasagne, keras, pylearn2, and nolearn. I have already selected