開源深度學習CNN庫DeepCL(C++)：DeepCL

C++實現的卷積神經網絡訓練庫，正在集成、完善Q-learning模塊和Python調用接口。

OpenCL library to train deep convolutional networks

• C++
• OpenCL
• Deep convolutional
• (New!) includes Q-learning module (draft)
• (New!) Python wrappers available (draft too :-) )
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Functionalities:

• convolutional layers
• max-pooling
• normalization layer
• random translations, as in Flexible, High Performance Convolutional Neural Networks for Image Classification
• random patches, as in ImageNet Classification with Deep Convolutional Networks
• multinet, ie Multi-column deep convolutional network, McDnn
• simple command-line network specification, as per notation in Multi-column Deep Neural Networks for Image Classification
• pad-zeros possible for convolutional layer
• various activation functions available:
  • tanh
  • scaled tanh (1.7519 * tanh(2/3x) )
  • linear
  • sigmoid
  • relu
  • softmax
• fully-connected layers
• various loss layers available:
  • square loss
  • cross-entropy
  • multinomial cross-entropy (synonymous with multinomial logistic, etc)
• Q-learning

Example usage:

• intend to target 19 x 19 Go boards, eg something similar to Clark and Storkey or Maddison, Huang, Sutskever and Silver
  • obtained 36.3% test accuracy, on next move prediction task, using 33.6 million training examples from kgsgo v2 dataset
  • commandline used./deepclrun dataset=kgsgoall netdef=32c5{z}-32c5{z}-32c5{z}-32c5{z}-32c5{z}-32c5{z}-500n-361n numepochs=3 learningrate=0.0001
  • 3 epochs, 1.5 days per epoch, on an Amazon GPU instance, comprising half an NVidia GRID K520 GPU (about half as powerful as a GTX780)
• obtained 99.5% test accuracy on MNIST, usingnetdef=rt2-8c5{padzeros}-mp2-16c5{padzeros}-mp3-150n-10n numepochs=20 multinet=6 learningrate=0.002
  • epoch time 99.8 seconds, using an Amazon GPU instance, ie half an NVidia GRID K520 GPU (since we are learning 6 nets in parallel, so 16.6seconds per epoch per net)

項目主頁：http://www.baiduhome.net/lib/view/home/1428632809656