This is the first of a series of analysis based on Hinton’s program. The subsequent analysis will include 2- Rbm.m and rbmhidlinear.m 3- backprop.m and mnistdisp.m 4- CG_MNIST.m

1. An Analysis of HINTON’s
mnistdeepauto program
1.
By Ali Riza SARAL
Arsaral ((at)) yahoo.com
This is the first of a series of analysis based on Hinton’s
program. The subsequent analysis will include
2- Rbm.m and rbmhidlinear.m
3- backprop.m amd mnistdisp.m
4- CG_MNIST.m

2. Copyright note
• Version 1.000
•
• 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.
• This program pretrains a deep autoencoder for MNIST dataset
• You can set the maximum number of epochs for pretraining each layer
• and you can set the architecture of the multilayer net.

3. Mnistdeepauto input and output
diagram

4. Training data input output

5. Training data input output epoch=1

6. Test data distorted input output

7. Testdata input output epoch=1

8. Mnistdeepauto layers

9. Execution flow

11. CONVERTER.m

12. MNIST data

13. Makebatches.m