GPU加速的Python深度学习库：Hebel
Hebel是一个用在Python中的神经网络深度学习库。使用 GPU 加速利用CUDA通过 PyCUD实现。它实现了几类最重要的神经网络模型，提供各种激活函数和训练模型，包括momentum、Nesterov momentum、dropout和early stopping。
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 hebelGetting started
Study the yaml configuration files in examples/ and run
python train_model.py examples/mnist_neural_net_shallow.ymlThe 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.
深度学习是机器学习研究中的一个新的领域，其动机在于建立、模拟人脑进行分析学习的神经网络。