UFLDL Exercise: Stacked autoencoder 栈式自编码

上传者: daniel_djf | 上传时间: 2019-12-21 19:38:01 | 文件大小: 11.15MB | 文件类型: zip
UFLDL Exercise: Stacked autoencoder(栈式自编码算法)matlab实验代码 可以直接运行

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[{"title":"( 66 个子文件 11.15MB ) UFLDL Exercise: Stacked autoencoder 栈式自编码","children":[{"title":"stackedae_exercise","children":[{"title":"softmaxTrain.m <span style='color:#111;'> 1.85KB </span>","children":null,"spread":false},{"title":"mnist","children":[{"title":"train-images.idx3-ubyte <span style='color:#111;'> 44.86MB </span>","children":null,"spread":false},{"title":"t10k-labels.idx1-ubyte <span style='color:#111;'> 9.77KB </span>","children":null,"spread":false},{"title":"t10k-images.idx3-ubyte <span style='color:#111;'> 7.48MB </span>","children":null,"spread":false},{"title":"train-labels.idx1-ubyte <span style='color:#111;'> 58.60KB </span>","children":null,"spread":false}],"spread":true},{"title":"minFunc","children":[{"title":"logistic","children":[{"title":"repmatC.mexglx <span style='color:#111;'> 20.20KB </span>","children":null,"spread":false},{"title":"mexutil.h <span style='color:#111;'> 317B </span>","children":null,"spread":false},{"title":"LogisticLoss.m <span style='color:#111;'> 659B </span>","children":null,"spread":false},{"title":"mylogsumexp.m <span style='color:#111;'> 227B </span>","children":null,"spread":false},{"title":"mexutil.c <span style='color:#111;'> 1.13KB </span>","children":null,"spread":false},{"title":"LogisticHv.m <span style='color:#111;'> 216B </span>","children":null,"spread":false},{"title":"repmatC.c <span style='color:#111;'> 3.87KB </span>","children":null,"spread":false},{"title":"repmatC.dll <span style='color:#111;'> 7.50KB </span>","children":null,"spread":false},{"title":"repmatC.mexmac <span style='color:#111;'> 9.77KB </span>","children":null,"spread":false},{"title":"LogisticDiagPrecond.m <span style='color:#111;'> 417B </span>","children":null,"spread":false}],"spread":true},{"title":"example_minFunc.m <span style='color:#111;'> 2.36KB </span>","children":null,"spread":false},{"title":"mcholC.mexw64 <span style='color:#111;'> 12.00KB </span>","children":null,"spread":false},{"title":"ArmijoBacktrack.m <span style='color:#111;'> 3.17KB </span>","children":null,"spread":false},{"title":"lbfgsC.mexw32 <span style='color:#111;'> 7.00KB </span>","children":null,"spread":false},{"title":"lbfgsC.mexglx <span style='color:#111;'> 7.55KB </span>","children":null,"spread":false},{"title":"mcholC.c <span style='color:#111;'> 4.09KB </span>","children":null,"spread":false},{"title":"autoHess.m <span style='color:#111;'> 901B </span>","children":null,"spread":false},{"title":"autoTensor.m <span style='color:#111;'> 870B </span>","children":null,"spread":false},{"title":"lbfgs.m <span style='color:#111;'> 924B </span>","children":null,"spread":false},{"title":"precondTriu.m <span style='color:#111;'> 51B </span>","children":null,"spread":false},{"title":"dampedUpdate.m <span style='color:#111;'> 995B </span>","children":null,"spread":false},{"title":"precondTriuDiag.m <span style='color:#111;'> 60B </span>","children":null,"spread":false},{"title":"lbfgsC.mexw64 <span style='color:#111;'> 9.50KB </span>","children":null,"spread":false},{"title":"minFunc_processInputOptions.m <span style='color:#111;'> 3.62KB </span>","children":null,"spread":false},{"title":"autoHv.m <span style='color:#111;'> 317B </span>","children":null,"spread":false},{"title":"conjGrad.m <span style='color:#111;'> 1.80KB </span>","children":null,"spread":false},{"title":"mcholC.mexmaci64 <span style='color:#111;'> 12.88KB </span>","children":null,"spread":false},{"title":"precondDiag.m <span style='color:#111;'> 42B </span>","children":null,"spread":false},{"title":"lbfgsC.c <span style='color:#111;'> 2.35KB </span>","children":null,"spread":false},{"title":"example_minFunc_LR.m <span style='color:#111;'> 1.57KB </span>","children":null,"spread":false},{"title":"rosenbrock.m <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"mchol.m <span style='color:#111;'> 1.26KB </span>","children":null,"spread":false},{"title":"lbfgsC.mexmac <span style='color:#111;'> 9.28KB </span>","children":null,"spread":false},{"title":"lbfgsUpdate.m <span style='color:#111;'> 614B </span>","children":null,"spread":false},{"title":"lbfgsC.mexa64 <span style='color:#111;'> 7.53KB </span>","children":null,"spread":false},{"title":"callOutput.m <span style='color:#111;'> 385B </span>","children":null,"spread":false},{"title":"mcholinc.m <span style='color:#111;'> 564B </span>","children":null,"spread":false},{"title":"minFunc.m <span style='color:#111;'> 42.61KB </span>","children":null,"spread":false},{"title":"WolfeLineSearch.m <span style='color:#111;'> 11.21KB </span>","children":null,"spread":false},{"title":"taylorModel.m <span style='color:#111;'> 677B </span>","children":null,"spread":false},{"title":"mcholC.mexw32 <span style='color:#111;'> 8.00KB </span>","children":null,"spread":false},{"title":"autoGrad.m <span style='color:#111;'> 807B </span>","children":null,"spread":false},{"title":"lbfgsC.mexmaci <span style='color:#111;'> 12.36KB </span>","children":null,"spread":false},{"title":"isLegal.m <span style='color:#111;'> 107B </span>","children":null,"spread":false},{"title":"polyinterp.m <span style='color:#111;'> 4.12KB </span>","children":null,"spread":false},{"title":"lbfgsC.mexmaci64 <span style='color:#111;'> 8.59KB </span>","children":null,"spread":false}],"spread":false},{"title":"loadMNISTImages.m <span style='color:#111;'> 811B </span>","children":null,"spread":false},{"title":"initializeParameters.m <span style='color:#111;'> 622B </span>","children":null,"spread":false},{"title":"stack2params.m <span style='color:#111;'> 1.61KB </span>","children":null,"spread":false},{"title":"stackedAECost.m <span style='color:#111;'> 4.09KB </span>","children":null,"spread":false},{"title":"checkStackedAECost.m <span style='color:#111;'> 1.70KB </span>","children":null,"spread":false},{"title":"softmaxCost.m <span style='color:#111;'> 1.25KB </span>","children":null,"spread":false},{"title":"stackedAEExercise.asv <span style='color:#111;'> 9.06KB </span>","children":null,"spread":false},{"title":"params2stack.m <span style='color:#111;'> 1.15KB </span>","children":null,"spread":false},{"title":"sparseAutoencoderCost.m <span style='color:#111;'> 3.79KB </span>","children":null,"spread":false},{"title":"feedForwardAutoencoder.m <span style='color:#111;'> 1.27KB </span>","children":null,"spread":false},{"title":"stackedAEExercise.m <span style='color:#111;'> 9.08KB </span>","children":null,"spread":false},{"title":"display_network.m <span style='color:#111;'> 2.58KB </span>","children":null,"spread":false},{"title":"stackedAECost.asv <span style='color:#111;'> 4.06KB </span>","children":null,"spread":false},{"title":"loadMNISTLabels.m <span style='color:#111;'> 516B </span>","children":null,"spread":false},{"title":"stackedAEPredict.m <span style='color:#111;'> 1.60KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]

评论信息

  • watchmanDDX :
    这个真的非常好,借鉴意义蛮大.
    2018-04-10
  • xiesivan :
    很好的资料,好好学习一下。。
    2017-12-19
  • watch_00 :
    好东西,学习深度学习的可以参考
    2016-12-05
  • secess :
    正在学习深度学习的内容,刚好可以参考本代码,运行后结果与权威结论一致,注释较为简单,再完备一些更好。
    2016-05-10
  • 小商1989 :
    很好用,通过它进行了学习
    2016-03-02

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