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https://github.com/jingxil/Neural-Decision-Forests
An implementation of the Deep Neural Decision Forests in PyTorch
https://github.com/jingxil/Neural-Decision-Forests
pytorch
Last synced: 15 days ago
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An implementation of the Deep Neural Decision Forests in PyTorch
- Host: GitHub
- URL: https://github.com/jingxil/Neural-Decision-Forests
- Owner: jingxil
- License: mit
- Created: 2018-01-29T04:16:59.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-06-28T02:38:02.000Z (over 5 years ago)
- Last Synced: 2024-08-01T16:33:33.082Z (3 months ago)
- Topics: pytorch
- Language: Python
- Size: 16.6 KB
- Stars: 153
- Watchers: 4
- Forks: 50
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Neural-Decision-Forests
An implementation of the Deep Neural Decision Forests(dNDF) in PyTorch.
# Features
- Two stage optimization as in the original paper [Deep Neural Decision Forests](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Kontschieder_Deep_Neural_Decision_ICCV_2015_paper.pdf) (fix the neural network and optimize $\pi$ and then optimize $\Theta$ with the class probability distribution in each leaf node fixed )
- Jointly training $\pi$ and $\Theta$ proposed by chrischoy in his work [Fully Differentiable Deep Neural Decision Forest](https://github.com/chrischoy/fully-differentiable-deep-ndf-tf)
- Shallow Neural Decision Forest (sNDF)
- Deep Neural Decision Forest (dNDF)
# Datasets
MNIST, UCI_Adult, UCI_Letter and UCI_Yeast datasets are available. For datasets other than MNIST, you need to go to corresponding directory and run the `get_data.sh` script.
# Requirements
- Python 3.x
- PyTorch >= 1.0.0
- numpy
- sklearn
# Usage
```
python train.py --ARG=VALUE
```
in the case of training the sNDF on MNIST with alternating optimization, the command is like
```
python train.py -dataset mnist -n_class 10 -gpuid 0 -n_tree 80 -tree_depth 10 -batch_size 1000 -epochs 100
```
# Results
Not spending much time on picking hyperparameters and without bells and whistles, I got the accuracy results(obtained by training $\pi$ and $\Theta$ seperately) as follows:
| Dataset | sNDF | dNDF |
| - | :-: | -: |
| MNIST | 0.9794| 0.9963 |
| UCI_Adult | 0.8558 | NA |
| UCI_Letter | 0.9507 | NA |
| UCI_Yeast | 0.6031 | NA |
By adding the nonlinearity in the routing function, the accuraries can reach 0.6502 and 0.9753 respectively on the UCI_Yeast and UCI_Letter.
# Note
Some people may experience the 'loss is NaN' situation which could be caused by the output probability being zero. Please make sure you have normalized your data and used a large enough tree size and depth. In the case that you want to stick with your tree setting, a workaround could be to clamp the output value.