jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
49 lines
2.9 KiB
Plaintext
49 lines
2.9 KiB
Plaintext
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
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%
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% Permission is granted for anyone to copy, use, modify, or distribute this
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% program and accompanying programs and documents for any purpose, provided
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% this copyright notice is retained and prominently displayed, along with
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% a note saying that the original programs are available from our
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% web page.
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% The programs and documents are distributed without any warranty, express or
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% implied. As the programs were written for research purposes only, they have
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% not been tested to the degree that would be advisable in any important
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% application. All use of these programs is entirely at the user's own risk.
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How to make it work:
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1. Create a separate directory and download all these files into the same directory
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2. Download from http://yann.lecun.com/exdb/mnist the following 4 files:
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* train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz
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* t10k-images-idx3-ubyte.gz t10k-labels-idx1-ubyte.gz
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3. Unzip these 4 files by executing:
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* gunzip train-images-idx3-ubyte.gz
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* gunzip train-labels-idx1-ubyte.gz
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* gunzip t10k-images-idx3-ubyte.gz
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* gunzip t10k-labels-idx1-ubyte.gz
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If unzipping with WinZip, make sure the file names have not been
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changed by Winzip.
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4. Download Conjugate Gradient code minimize.m available at
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http://www.kyb.tuebingen.mpg.de/bs/people/carl/code/minimize/
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5. Download the following 13 files for training an autoencoder and a classification model:
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* mnistdeepauto.m Main file for training deep autoencoder
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* mnistclassify.m Main file for training classification model
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* converter.m Converts raw MNIST digits into matlab format
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* rbm.m Training RBM with binary hidden and visible units
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* rbmhidlinear.m Training RBM with Gaussian hidden and binary visible units
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* backprop.m Backpropagation for fine-tuning an autoencoder
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* backpropclassify.m Backpropagation for classification using "encoder" network
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* CG_MNIST.m Conjugate Gradient optimization for fine-tuning an autoencoder
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* CG_CLASSIFY_INIT.m Conjugate Gradient optimization for classification
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(training top-layer weights while holding low-level weights fixed)
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* CG_CLASSIFY.m Conjugate Gradient optimization for classification (training all weights)
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* makebatches.m Creates minibatches for RBM training
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* mnistdisp.m Displays progress during fine-tuning stage
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* README.txt
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6. For training a deep autoencoder run mnistdeepauto.m in matlab.
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7. For training a classification model run mnistclassify.m in matlab.
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8. Make sure you have enough space to store the entire MNIST dataset on your disk.
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You can also set various parameters in the code, such as maximum number of epochs,
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learning rates, network architecture, etc.
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