% Code provided by YujianLi and Ting Zhang.The Code is based on the autoencoder code. 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.
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:
          * 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 the following 13 files for training a DBN model:
          
* mnistgenerating.m Main file for training a DBN
	  
* converter.m Converts raw MNIST digits into matlab format
          
* rbm.m Training RBM with binary hidden and visible units
          
* top_rbm.m Training ClassRBM* backprop.m Backpropagation for fine-tuning an autoencoder
          
* wake_sleep.m Wake sleep for fine-tuning a DBN  
                            
* mnistdisp.m Displays progress during fine-tuning stage 
          
* README.txt 
   

5. 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.