Description
GPU-Accelerated Deep Learning Library in Python
Hebel is a library for deep learning with neural networks in Python using GPU acceleration with CUDA through PyCUDA. It implements the most important types of neural network models and offers a variety of different activation functions and training methods such as momentum, Nesterov momentum, dropout, and early stopping.
I no longer actively develop Hebel. If you are looking for a deep learning framework in Python, I now recommend Chainer.
hebel alternatives and similar packages
Based on the "Machine Learning" category.
Alternatively, view hebel alternatives based on common mentions on social networks and blogs.
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Scout Monitoring - Free Django app performance insights with Scout Monitoring
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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README
Hebel
GPU-Accelerated Deep Learning Library in Python
Hebel is a library for deep learning with neural networks in Python using GPU acceleration with CUDA through PyCUDA. It implements the most important types of neural network models and offers a variety of different activation functions and training methods such as momentum, Nesterov momentum, dropout, and early stopping.
I no longer actively develop Hebel. If you are looking for a deep learning framework in Python, I now recommend Chainer.
Models
Right now, Hebel implements feed-forward neural networks for classification and regression on one or multiple tasks. Other models such as Autoencoder, Convolutional neural nets, and Restricted Boltzman machines are planned for the future.
Hebel implements dropout as well as L1 and L2 weight decay for regularization.
Optimization
Hebel implements stochastic gradient descent (SGD) with regular and Nesterov momentum.
Compatibility
Currently, Hebel will run on Linux and Windows, and probably Mac OS X (not tested).
Dependencies
- PyCUDA
- numpy
- PyYAML
- skdata (only for MNIST example)
Installation
Hebel is on PyPi, so you can install it with
pip install hebel
Getting started
Study the yaml configuration files in examples/ and run
python train_model.py examples/mnist_neural_net_shallow.yml
The script will create a directory in examples/mnist where the models and logs are saved.
Read the Getting started guide at hebel.readthedocs.org/en/latest/getting_started.html for more information.
Documentation
Contact
Maintained by Hannes Bretschneider ([email protected]). If your are using Hebel, please let me know whether you find it useful and file a Github issue if you find any bugs or have feature requests.
Citing
If you make use of Hebel in your research, please cite it. The BibTeX reference is
@article{Bretschneider:10050,
author = "Hannes Bretschneider",
title = "{Hebel - GPU-Accelerated Deep Learning Library in Python}",
month = "May",
year = "2014",
doi = "10.5281/zenodo.10050",
url = "https://zenodo.org/record/10050",
}
What's with the name?
Hebel is the German word for lever, one of the oldest tools that humans use. As Archimedes said it: "Give me a lever long enough and a fulcrum on which to place it, and I shall move the world."