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Update Readme with new network type

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Huw Campbell 2017-02-03 22:47:28 +11:00
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README.md
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@ -16,8 +16,10 @@ for concise and precise specifications of complex networks in Haskell.
As an example, a network which can achieve ~1.5% error on MNIST can be
specified and initialised with random weights in a few lines of code with
```haskell
type MNIST = Network '[ Convolution 1 10 5 5 1 1, Pooling 2 2 2 2, Relu, Convolution 10 16 5 5 1 1, Pooling 2 2 2 2, FlattenLayer, Relu, FullyConnected 256 80, Logit, FullyConnected 80 10, Logit]
'[ '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]
type MNIST
= Network
'[ Convolution 1 10 5 5 1 1, Pooling 2 2 2 2, Relu, Convolution 10 16 5 5 1 1, Pooling 2 2 2 2, FlattenLayer, Relu, FullyConnected 256 80, Logit, FullyConnected 80 10, Logit]
'[ '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]
randomMnist :: MonadRandom m => m MNIST
randomMnist = randomNetwork
@ -31,8 +33,10 @@ If recurrent neural networks are more your style, you can try defining something
["unreasonably effective"](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
with
```haskell
type Shakespeare = RecurrentNetwork '[ R (LSTM 40 80), R (LSTM 80 40), F (FullyConnected 40 40), F Logit]
'[ 'D1 40, 'D1 80, 'D1 40, 'D1 40, 'D1 40 ]
type Shakespeare
= RecurrentNetwork
'[ R (LSTM 40 80), R (LSTM 80 40), F (FullyConnected 40 40), F Logit]
'[ 'D1 40, 'D1 80, 'D1 40, 'D1 40, 'D1 40 ]
```
Design
@ -45,8 +49,13 @@ data that are passed between the layers.
The definition of a network is surprisingly simple:
```haskell
data Network :: [*] -> [Shape] -> * where
O :: (SingI i, SingI o, Layer x i o) => !x -> Network '[x] '[i, o]
(:~>) :: (SingI i, SingI h, Layer x i h) => !x -> !(Network xs (h ': hs)) -> Network (x ': xs) (i ': h ': hs)
NNil :: SingI i
=> Network '[] '[i]
(:~>) :: (SingI i, SingI h, Layer x i h)
=> !x
-> !(Network xs (h ': hs))
-> Network (x ': xs) (i ': h ': hs)
```
The `Layer x i o` constraint ensures that the layer `x` can sensibly perform a