https://github.com/jbahire/cuda-convnet2

This is a fast C++/CUDA implementation of convolutional (or more generally, feed-forward) neural networks.
https://github.com/jbahire/cuda-convnet2

artificial-intelligence convolutional-neural-networks cpp cuda-convnet2 machine-learning-algorithms neural-network

Last synced: 10 months ago
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This is a fast C++/CUDA implementation of convolutional (or more generally, feed-forward) neural networks.

• Host: GitHub
• URL: https://github.com/jbahire/cuda-convnet2
• Owner: JBAhire
• License: mit
• Created: 2018-01-12T17:12:24.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2018-01-12T17:25:31.000Z (over 8 years ago)
• Last Synced: 2025-02-12T12:16:55.381Z (over 1 year ago)
• Topics: artificial-intelligence, convolutional-neural-networks, cpp, cuda-convnet2, machine-learning-algorithms, neural-network
• Homepage: https://medium.com/@jayeshbahire
• Size: 23.4 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# CUDA-CONVNET

## I've released an update to cuda-convnet, called cuda-convnet2. The two main new features are faster training on Kepler-generation GPUs and support for multi-GPU training.

This is a fast C++/CUDA implementation of convolutional (or more generally, feed-forward) neural networks. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the back-propagation algorithm.

Fermi-generation GPU (GTX 4xx, GTX 5xx, or Tesla equivalent) required.

Documentation

    Compiling -- how to check out and compile this code.

    Data -- what kind of data this net can train on.

    LayerParams -- how to specify an architecture for the net.

    NeuronTypes -- types of hidden unit nonlinearities.

    TrainingNet -- how to train the net.

    Options -- the command-line arguments that the net takes.

    ViewingNet -- how to look inside the checkpoints saved by the net.

    CheckingGradients -- how to numerically test the gradients for correctness.

## Fast results

    11% error on CIFAR-10 in 75 minutes, with image translations and horizontal reflections (def, params).

    13% error on CIFAR-10 in 25 minutes, with image translations and horizontal reflections (def, params).

        See Methodology for details of training.

            Filters learned by this net:

               https://github.com/JBAhire/Cuda-ConvNet2/blob/master/80-second-filters2.png

    18% error on CIFAR-10 in 20 minutes, without any image translations/transformations/preprocessing (def, params).

    26% error on CIFAR-10 in 80 seconds, without any image translations/transformations/preprocessing (def, params).