#Deep Learning With Python
This repository is a Python implementation version of UFLDL(Unsupervised Feature Learning and Deep Learning) tutorial exercises, the codes are passed the test and get the same results as exepected.
tutorial homepage: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
IMPORTANT NOTES:
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The datasets used in this repository can be found in UFLDL homepage.
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I implememented softmax with bias, while the tutorial did not. I try softmax with bias and no bias, the result shows softmax with bias can achieved an exepected accuracy of 92.6% on MNIST dataset.
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Scipy 0.17 will lead to python kenerl died when load the *.mat format file. It is a inherent bug in scipy 0.17, so I do not recommend you to use scipy 0.17 to run the codes.
##Prerequisites
- python 2.7
- numpy
- scipy
- or just Anaconda (strongly recommend)
##Exercises' core source code file are listed as follows:
Sparse Autoencoder
- sparseAutoencoder.py
- sparseAutoencoderTest.py
Preprocessing: PCA and Whitening
- whitening.py
Softmax Regression
- softmax.py
Self-Taught Learning and Unsupervised Feature Learning
- self-taughtLearningTest.py
Building Deep Networks for Classification(Stacked Sparse Autoencoder)
- stackedAutoencoder.py
- stackedAutoencoderTest.py
Linear Decoders with Autoencoders
- sparseAutoencoder.py
- SAEWithLinearDecoderTest.py
Working with Large Images(Convolutional Neural Networks)
- cnn.py
- cnnTest.py
If you have any questions, please feel free to contact with me. (guanghuitu@gmail.com or guanghuitu@foxmail.com)
Enjoy it!