GPU加速的Python深度學習庫：Hebel

Hebel是一個用在Python中的神經網絡深度學習庫。使用 GPU 加速利用CUDA通過 PyCUD實現。它實現了幾類最重要的神經網絡模型，提供各種激活函數和訓練模型，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 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.

深度學習是機器學習研究中的一個新的領域，其動機在于建立、模擬人腦進行分析學習的神經網絡。

項目主頁：http://www.baiduhome.net/lib/view/home/1414997599137