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💣 Machine learning which might blow up in your face 💣
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123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293{-# LANGUAGE CPP #-}{-# LANGUAGE DataKinds #-}{-# LANGUAGE GADTs #-}{-# LANGUAGE TypeOperators #-}{-# LANGUAGE TypeFamilies #-}{-# LANGUAGE MultiParamTypeClasses #-}{-# LANGUAGE FlexibleContexts #-}{-# LANGUAGE RankNTypes #-}{-# LANGUAGE FlexibleInstances #-}{-|Module : Grenade.Core.LayerDescription : Defines the Layer ClassesCopyright : (c) Huw Campbell, 2016-2017License : BSD2Stability : experimental
This module defines what a Layer is in a Grenadeneural network.
There are two classes of interest: `UpdateLayer` and `Layer`.
`UpdateLayer` is required for all types which are used as a layerin a network. Having no shape information, this class is agnosticto the input and output data of the layer.
An instance of `Layer` on the other hand is required for usage ina neural network, but also specifies the shapes of data that thenetwork can transform. Multiple instance of `Layer` are permittedfor a single type, to transform different shapes. The `Reshape` layerfor example can act as a flattening layer, and its inverse, projectinga 1D shape up to 2 or 3 dimensions.
Instances of `Layer` should be as strict as possible, and not emitruntime errors.-}module Grenade.Core.Layer ( Layer (..) , UpdateLayer (..) ) where
import Control.Monad.Random ( MonadRandom )
import Data.List ( foldl' )
#if MIN_VERSION_base(4,9,0)import Data.Kind (Type)#endif
import Grenade.Core.Shapeimport Grenade.Core.LearningParameters
-- | Class for updating a layer. All layers implement this, as it-- describes how to create and update the layer.--class UpdateLayer x where -- | The type for the gradient for this layer. -- Unit if there isn't a gradient to pass back. type Gradient x :: Type
-- | Update a layer with its gradient and learning parameters runUpdate :: LearningParameters -> x -> Gradient x -> x
-- | Create a random layer, many layers will use pure createRandom :: MonadRandom m => m x
-- | Update a layer with many Gradients runUpdates :: LearningParameters -> x -> [Gradient x] -> x runUpdates rate = foldl' (runUpdate rate)
{-# MINIMAL runUpdate, createRandom #-}
-- | Class for a layer. All layers implement this, however, they don't-- need to implement it for all shapes, only ones which are-- appropriate.--class UpdateLayer x => Layer x (i :: Shape) (o :: Shape) where -- | The Wengert tape for this layer. Includes all that is required -- to generate the back propagated gradients efficiently. As a -- default, `S i` is fine. type Tape x i o :: Type
-- | Used in training and scoring. Take the input from the previous -- layer, and give the output from this layer. runForwards :: x -> S i -> (Tape x i o, S o)
-- | Back propagate a step. Takes the current layer, the input that -- the layer gave from the input and the back propagated derivatives -- from the layer above. -- -- Returns the gradient layer and the derivatives to push back -- further. runBackwards :: x -> Tape x i o -> S o -> (Gradient x, S i)