稀疏自编码深度学习的Matlab实现
稀疏自编码深度学习的Matlab实现,sparse Auto coding,Matlab codetrain, m/7% CS294A/CS294W Programming Assignment Starter CodeInstructions%%%This file contains code that helps you get started ontheprogramming assignment. You will need to complete thecode in sampleIMAgEsml sparseAutoencoder Cost m and computeNumericalGradientml For the purpose of completing the assignment, you domot need tochange the code in this filecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencodtrain.m∥%%========%6% STEP 0: Here we provide the relevant parameters valuesthat willl allow your sparse autoencoder to get good filters; youdo not need to9 change the parameters belowvisibleSize =8*8; number of input unitshiddensize 25number of hidden unitssparsity Param =0.01; desired average activation ofthe hidden units7 (This was denoted by the greek alpharho, which looks like a lower-case pcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod4/57train.,m∥in the lecture notes)1 ambda=0.0001%o weight decay parameterbeta 3%o weight of sparsity penalty term%%==:79 STEP 1: Implement sampleIMAGESAfter implementing sampleIMAGES, the display_networkcommand shouldfo display a random sample of 200 patches from the datasetpatches sampleIMAgES;display_network(patches(:, randi(size(patches, 2), 204, 1)), 8)%为产生一个204维的列向量,每一维的值为0~10000curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod5/57train.m/v%中的随机数,说明是随机取204个 patch来显示%o Obtain random parameters thetatheta= initializeParameters ( hiddenSize, visibleSize)%%=============三三三三====================================97 STEP 2: Implement sparseAutoencoder CostYou can implement all of the components (squared errorcost, weight decay termsparsity penalty) in the cost function at once, butit may be easier to do%o it step-by-step and run gradient checking (see STEP3 after each stepWecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod6/57train. m vb suggest implementing the sparseAutoencoder Cost functionusing the following steps(a) Implement forward propagation in your neural networland implement the%squared error term of the cost function. Implementbackpropagation tocompute the derivatives. Then (using lambda=beta=(run gradient Checking%to verify that the calculations corresponding tothe squared error costterm are correctcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod7/57train. m vl(b) Add in the weight decay term (in both the cost funcand the derivativecalculations), then re-run Gradient Checking toverify correctnessl (c) Add in the sparsity penalty term, then re-run gradiChecking toverify correctnessFeel free to change the training settings when debuggingyour%o code. (For example, reducing the training set sizecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod8/57train m vl/number of hidden units may make your code run fasterand setting betaand/or lambda to zero may be helpful for debuggingHowever, in yourfinal submission of the visualized weights, please useparameters web gave in Step 0 abovecoS七grad]sparseAutoencoderCost(theta, visibleSize,hiddensize, lambda,sparsityParam, beta,patches)二〓二二二二二二二〓二〓二〓二〓=二====〓=curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod9/57train.m vlll96% STeP 3: Gradient CheckingHint: If you are debugging your code, performing gradienchecking on smaller modelsand smaller training sets (e. g, using only 10 trainingexamples and 1-2 hiddenunits) may speed things upl First, lets make sure your numerical gradient computationis correct for a%o simple function. After you have implemented computeNumerun the followingcheckNumericalGradientocurer:YiBinYUyuyibintony@163.com,WuYiUniversityDeep Learning, MATLAB Code for Sparse Autoencode10/57
- 2020-12-05下载
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PCIE SPEC 4.0 规格书
PCIE SPEC 4.0 规格书PCI-SIG disclaims all warranties and liability for the use of this document and the information contained herein andassumes no responsibility for any errors that may appear in this document, nor does PCi-Sig make a commitment toupdate the intormation contained hereinContact the Pci-sig office to obtain the latest revision of this specificationQuestions regarding the PCI Express Base Specification or membership in PCI-SIG may be forwarded toMembership serviceswww.pcisg.comadministration(apcisig.comPhor503-619-0569503-644-6708Technical Supporttechsupp( apcisig comDISCLAIMERThis PCI Express Base Specification is provided "as is" with no warranties whatsoever, including any warranty olmerchantability, noninfringement, fitness for any particular purpose, or any warranty otherwise arising out of anyproposal, specification, or sample. PCT-SIG disclaims all liability for infringement of proprietary rights, relating touse of information in this specification. No license, express or implied, by estoppel or otherwise, to any intellectualproperty rights is granted hereinPCI, PCI Express, PCle, and PCi-Sig are trademarks or registered trademarks of PCI-SIgAll other product names are trademarks, registered trademarks, or servicemarks of their respective ownersCopyright O 2002-2014 PCI-SIG
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