RBM.m hinton下载

PIPI_333 2018-08-14 05:33:25

基于 Geoff Hinton and Ruslan Salakhutdinov的RBM程序
相关下载链接：//download.csdn.net/download/yuhui0727/10603609?utm_source=bbsseo

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基于 Geoff Hinton and Ruslan Salakhutdinov的RBM程序

Code provided by Ruslan Salakhutdinov and Geoff Hinton Permission is granted for anyone to copy, use, modify, or distribute this program and accompanying programs and documents for any purpose, provided this copyright notice is retained and prominently displayed, along with a note saying that the original programs are available from our web page. The programs and documents are distributed without any warranty, express or implied. As the programs were written for research purposes only, they have not been tested to the degree that would be advisable in any important application. All use of these programs is entirely at the user's own risk. How to make it work: 1. Create a separate directory and download all these files into the same directory 2. Download from http://yann.lecun.com/exdb/mnist the following 4 files: o train-images-idx3-ubyte.gz o train-labels-idx1-ubyte.gz o t10k-images-idx3-ubyte.gz o t10k-labels-idx1-ubyte.gz 3. Unzip these 4 files by executing: o gunzip train-images-idx3-ubyte.gz o gunzip train-labels-idx1-ubyte.gz o gunzip t10k-images-idx3-ubyte.gz o gunzip t10k-labels-idx1-ubyte.gz If unzipping with WinZip, make sure the file names have not been changed by Winzip. 4. Download Conjugate Gradient code minimize.m 5. Download Autoencoder_Code.tar which contains 13 files OR download each of the following 13 files separately for training an autoencoder and a classification model: o mnistdeepauto.m Main file for training deep autoencoder o mnistclassify.m Main file for training classification model o converter.m Converts raw MNIST digits into matlab format o rbm.m Training RBM with binary hidden and binary visible units o rbmhidlinear.m Training RBM with Gaussian hidden and binary visible units o backprop.m Backpropagation for fine-tuning an autoencoder o backpropclassify.m Backpropagation for classification using "encoder" network o CG_MNIST.m Conjugate Gradient optimization for fine-tuning an autoencoder o CG_CLASSIFY_INIT.m Co

神经网络用于数据降维Matlab代码

运行 testDNN 试试！每个功能都包含说明。请检查一下！它提供了堆叠受限玻尔兹曼机 (RBM) 的深度信念网络 (DBN) 的深度学习工具。它包括 Bernoulli-Bernoulli RBM、Gaussian-Bernoulli RBM、用于无监督预训练的对比发散学习、稀疏约束、用于监督训练的反向投影和 dropout 技术。 MNIST 数据集的示例代码包含在 mnist 文件夹中。请参阅 mnist 文件夹中的 readme.txt。 Hinton 等人，通过防止特征检测器的共同适应来改进神经网络，2012 年。 Lee 等人，视觉区域 V2 的稀疏深度信念网络模型，NIPS 2008。 http://read.pudn.com/downloads103/sourcecode/math/421402/drtoolbox/techniques/train_rbm.m__

这是有关深度学习的一个资料基本类型是RBM

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