文件名称:stackedAE
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2015-09-28
- 文件大小：
- 10.35mb
- 下载次数：
- 0次
- 提 供 者：
- 单**
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
堆栈自编码，通过两个稀疏自编码的堆叠和softmax分类模型，实现手写体的分类。-Stack self-encoding, since encoding by two sparse stack and softmax classification model to classify handwriting.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
stackedAE
.........\checkStackedAECost.m
.........\display_network.m
.........\feedForwardAutoencoder.asv
.........\feedForwardAutoencoder.m
.........\initializeParameters.m
.........\loadMNISTImages.m
.........\loadMNISTLabels.m
.........\minFunc
.........\.......\ArmijoBacktrack.m
.........\.......\WolfeLineSearch.m
.........\.......\autoGrad.m
.........\.......\autoHess.m
.........\.......\autoHv.m
.........\.......\autoTensor.m
.........\.......\callOutput.m
.........\.......\conjGrad.m
.........\.......\dampedUpdate.m
.........\.......\example_minFunc.m
.........\.......\example_minFunc_LR.m
.........\.......\isLegal.m
.........\.......\lbfgs.m
.........\.......\lbfgsC.c
.........\.......\lbfgsC.mexa64
.........\.......\lbfgsC.mexglx
.........\.......\lbfgsC.mexmac
.........\.......\lbfgsC.mexmaci
.........\.......\lbfgsC.mexmaci64
.........\.......\lbfgsC.mexw32
.........\.......\lbfgsC.mexw64
.........\.......\lbfgsUpdate.m
.........\.......\logistic
.........\.......\........\LogisticDiagPrecond.m
.........\.......\........\LogisticHv.m
.........\.......\........\LogisticLoss.m
.........\.......\........\mexutil.c
.........\.......\........\mexutil.h
.........\.......\........\mylogsumexp.m
.........\.......\........\repmatC.c
.........\.......\........\repmatC.dll
.........\.......\........\repmatC.mexglx
.........\.......\........\repmatC.mexmac
.........\.......\mchol.m
.........\.......\mcholC.c
.........\.......\mcholC.mexmaci64
.........\.......\mcholC.mexw32
.........\.......\mcholC.mexw64
.........\.......\mcholinc.m
.........\.......\minFunc.m
.........\.......\minFunc_processInputOptions.m
.........\.......\polyinterp.m
.........\.......\precondDiag.m
.........\.......\precondTriu.m
.........\.......\precondTriuDiag.m
.........\.......\rosenbrock.m
.........\.......\taylorModel.m
.........\params2stack.m
.........\softmaxCost.m
.........\softmaxTrain.m
.........\sparseAutoencoderCost.m
.........\stack2params.m
.........\stackedAECost.asv
.........\stackedAECost.m
.........\stackedAEExercise.asv
.........\stackedAEExercise.m
.........\stackedAEPredict.asv
.........\stackedAEPredict.m
.........\t10k-images.idx3-ubyte
.........\t10k-labels.idx1-ubyte
.........\train-images.idx3-ubyte
.........\train-labels.idx1-ubyte