This repo is aiming to provide a set of Convolutional Auto-encoder implementations (CAEs) for Deep Learning.
Convolutional Auto-encoder did not draw so much attention since it's proposed. However, I think that this model offered a very nice unsupervised feature learning model from neural network persepective.
Following dependencies are required:
- Python 2.7.8
numpyscipytheano
You can also use Anaconda directly, this python distribution will offer you all dependencies.
##Updates
- ConvNet layer [20141021]
- Original ConvNet Auto-encoder [20141021]
- Tested for AWS GPU instance [20141025]
- Example for classification [20141025]
- Add some support functions for ConvNet Layer [20141027]
##To-do
- Multiple activation function support
- Support functions for ConvNet Layer and ConvNet AE
- Sparse ConvNet Auto-encoder
- Stacked ConvNet Auto-encoder
##Notes
-
All experiment in this repo are conducted on GPU, in order to run it faster, you are suggested to have a GPU on your machine.
-
If you forked this repo, use and modifiy
update.shto avoid updating unnecessary files and data. -
Tested on AWS GPU instance, the performance looks like Tesla K20C.
##Contacts
Hu Yuhuang
Advanced Robotic Lab
Department of Artificial Intelligence
Faculty of Computer Science & IT
University of Malaya
Email: duguyue100@gmail.com