Run command on pcDuino Ubuntu Terminal:
$ sudo apt-get install git $ git clone https://github.com/qiuwch/DeepLearning
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:
* train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz
* t10k-images-idx3-ubyte.gz t10k-labels-idx1-ubyte.gz
3. Unzip these 4 files by executing:
* gunzip train-images-idx3-ubyte.gz
* gunzip train-labels-idx1-ubyte.gz
* gunzip t10k-images-idx3-ubyte.gz
* 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 available at
5. Download the following 13 files for training an autoencoder and a classification model:
* mnistdeepauto.m Main file for training deep autoencoder
* mnistclassify.m Main file for training classification model
* converter.m Converts raw MNIST digits into matlab format
* rbm.m Training RBM with binary hidden and visible units
* rbmhidlinear.m Training RBM with Gaussian hidden and binary visible units
* backprop.m Backpropagation for fine-tuning an autoencoder
* backpropclassify.m Backpropagation for classification using “encoder” network
* CG_MNIST.m Conjugate Gradient optimization for fine-tuning an autoencoder
* CG_CLASSIFY_INIT.m Conjugate Gradient optimization for classification
(training top-layer weights while holding low-level weights fixed)
* CG_CLASSIFY.m Conjugate Gradient optimization for classification (training all weights)
* makebatches.m Creates minibatches for RBM training
* mnistdisp.m Displays progress during fine-tuning stage
6. For training a deep autoencoder run mnistdeepauto.m in matlab.
7. For training a classification model run mnistclassify.m in matlab.
8. Make sure you have enough space to store the entire MNIST dataset on your disk.
You can also set various parameters in the code, such as maximum number of epochs,
learning rates, network architecture, etc.
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