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79 lines
3.2 KiB
Markdown
79 lines
3.2 KiB
Markdown
Grenade
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=======
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```
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First shalt thou take out the Holy Pin, then shalt thou count to three, no more, no less.
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Three shall be the number thou shalt count, and the number of the counting shall be three.
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Four shalt thou not count, neither count thou two, excepting that thou then proceed to three.
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Five is right out.
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```
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💣 Machine learning which might blow up in your face 💣
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Grenade is a dependently typed, practical, and pretty quick neural network library for concise and precise
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specifications of complex networks in Haskell.
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As an example, a network which can achieve less than 1.5% error on mnist can be specified and
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initialised with random weights in under 10 lines of code with
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```haskell
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randomMnistNet :: MonadRandom m => m (Network Identity '[('D2 28 28), ('D3 24 24 10), ('D3 12 12 10), ('D3 12 12 10), ('D3 8 8 16), ('D3 4 4 16), ('D1 256), ('D1 256), ('D1 80), ('D1 80), ('D1 10), ('D1 10)])
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randomMnistNet = do
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a :: Convolution 1 10 5 5 1 1 <- randomConvolution
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let b :: Pooling 2 2 2 2 = Pooling
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c :: Convolution 10 16 5 5 1 1 <- randomConvolution
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let d :: Pooling 2 2 2 2 = Pooling
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e :: FullyConnected 256 80 <- randomFullyConnected
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f :: FullyConnected 80 10 <- randomFullyConnected
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return $ a :~> b :~> Relu :~> c :~> d :~> FlattenLayer :~> Relu :~> e :~> Logit :~> f :~> O Logit
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```
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The network can be thought of as a heterogeneous list of layers, and its type signature includes a type
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level list of the shapes of the data passed between the layers of the network.
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In the above example, the input layer can be seen to be a two dimensional (`D2`) image with 28 by 28 pixels.
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The last item in the list is one dimensional (`D1`) with 10 values, representing the categories of the mnist data.
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Layers in Grenade are represented as Haskell classes, so creating one's own is easy in downstream code. If the shapes
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of a network are not specified correctly and a layer can not sensibly perform the operation between two shapes, then
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it will result in a compile time error.
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Build Instructions
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------------------
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Grenade currently only builds with the [mafia](https://github.com/ambiata/mafia) scipt that is located in the
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repository. You will also need the `lapack` and `blas` libraries and development tools. Once you have all
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that, Grenade can be build using:
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```
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./mafia build
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```
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and the tests run using:
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```
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./mafia test
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```
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Grenade is currently known to build with ghc 7.10 and 8.0.
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Thanks
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------
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Writing a library like this has been on my mind for a while now, but a big shout out must go to Justin Le, whose
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dependently typed fully connected network inspired me to get cracking, gave many ideas for the type level tools I
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needed, and was a great starting point for writing this library.
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Performance
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-----------
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Grenade is backed by hmatrix and blas, and uses a pretty clever convolution trick popularised by Caffe, which
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is surprisingly effective and fast. So for many small scale problems it should be sufficient.
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That said, it's currently stuck on a single core and doesn't hit up the GPU, so there's a fair bit of performance
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sitting there begging.
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Training 15 generations over Kaggle's mnist training data took a few hours.
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Contributing
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------------
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Contributions are welcome.
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