Docs

src/Grenade.hs

@@ -46,7 +46,7 @@ is a simple example which runs a logistic regression.
 > randomMyNet = randomNetwork
 
 The function `randomMyNet` witnesses the `CreatableNetwork`
-constraint of the neural network, that is, it ensures the network
+constraint of the neural network, and in doing so, ensures the network
 can be built, and hence, that the architecture is sound.
 -}
 

src/Grenade/Layers/Elu.hs

@@ -2,6 +2,13 @@
 {-# LANGUAGE TypeOperators         #-}
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE MultiParamTypeClasses #-}
+{-|
+Module      : Grenade.Layers.Logit
+Description : Exponential linear unit layer
+Copyright   : (c) Huw Campbell, 2016-2017
+License     : BSD2
+Stability   : experimental
+-}
 module Grenade.Layers.Elu (
     Elu (..)
   ) where

src/Grenade/Layers/Logit.hs

@@ -3,6 +3,13 @@
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE FlexibleInstances     #-}
 {-# LANGUAGE MultiParamTypeClasses #-}
+{-|
+Module      : Grenade.Layers.Logit
+Description : Sigmoid nonlinear layer
+Copyright   : (c) Huw Campbell, 2016-2017
+License     : BSD2
+Stability   : experimental
+-}
 module Grenade.Layers.Logit (
     Logit (..)
   ) where
@@ -14,6 +21,7 @@ import           Data.Singletons
 import           Grenade.Core
 
 -- | A Logit layer.
+--
 --   A layer which can act between any shape of the same dimension, perfoming an sigmoid function.
 --   This layer should be used as the output layer of a network for logistic regression (classification)
 --   problems.

src/Grenade/Layers/Pad.hs

@@ -5,6 +5,13 @@
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE MultiParamTypeClasses #-}
 {-# LANGUAGE FlexibleContexts      #-}
+{-|
+Module      : Grenade.Core.Pad
+Description : Padding layer for 2D and 3D images
+Copyright   : (c) Huw Campbell, 2016-2017
+License     : BSD2
+Stability   : experimental
+-}
 module Grenade.Layers.Pad (
     Pad (..)
   ) where
@@ -22,6 +29,8 @@ import           Numeric.LinearAlgebra (konst, subMatrix, diagBlock)
 import           Numeric.LinearAlgebra.Static (extract, create)
 
 -- | A padding layer for a neural network.
+--
+--   Pads on the X and Y dimension of an image.
 data Pad  :: Nat
           -> Nat
           -> Nat

src/Grenade/Layers/Pooling.hs

@@ -6,6 +6,13 @@
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE MultiParamTypeClasses #-}
 {-# LANGUAGE FlexibleContexts      #-}
+{-|
+Module      : Grenade.Core.Pooling
+Description : Max Pooling layer for 2D and 3D images
+Copyright   : (c) Huw Campbell, 2016-2017
+License     : BSD2
+Stability   : experimental
+-}
 module Grenade.Layers.Pooling (
     Pooling (..)
   ) where
@@ -22,6 +29,7 @@ import           Grenade.Layers.Internal.Pooling
 import           Numeric.LinearAlgebra.Static as LAS hiding ((|||), build, toRows)
 
 -- | A pooling layer for a neural network.
+--
 --   Does a max pooling, looking over a kernel similarly to the convolution network, but returning
 --   maxarg only. This layer is often used to provide minor amounts of translational invariance.
 --

src/Grenade/Layers/Relu.hs

@@ -2,6 +2,13 @@
 {-# LANGUAGE TypeOperators         #-}
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE MultiParamTypeClasses #-}
+{-|
+Module      : Grenade.Layers.Relu
+Description : Rectifying linear unit layer
+Copyright   : (c) Huw Campbell, 2016-2017
+License     : BSD2
+Stability   : experimental
+-}
 module Grenade.Layers.Relu (
     Relu (..)
   ) where

src/Grenade/Layers/Reshape.hs

@@ -3,6 +3,13 @@
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE MultiParamTypeClasses #-}
 {-# LANGUAGE FlexibleContexts      #-}
+{-|
+Module      : Grenade.Layers.Reshape
+Description : Multipurpose reshaping layer
+Copyright   : (c) Huw Campbell, 2016-2017
+License     : BSD2
+Stability   : experimental
+-}
 module Grenade.Layers.Reshape (
     Reshape (..)
   ) where
@@ -19,9 +26,9 @@ import           Grenade.Core
 
 -- | Reshape Layer
 --
--- Flattens input down to D1 from either 2D or 3D data.
---
--- Casts input D1 up to either 2D or 3D data if the shapes are good.
+-- The Reshape layer can flatten any 2D or 3D image to 1D vector with the
+-- same number of activations, as well as cast up from 1D to a 2D or 3D
+-- shape.
 --
 -- Can also be used to turn a 3D image with only one channel into a 2D image
 -- or vice versa.

src/Grenade/Layers/Softmax.hs

@@ -3,6 +3,13 @@
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE FlexibleInstances     #-}
 {-# LANGUAGE MultiParamTypeClasses #-}
+{-|
+Module      : Grenade.Core.Softmax
+Description : Softmax loss layer
+Copyright   : (c) Huw Campbell, 2016-2017
+License     : BSD2
+Stability   : experimental
+-}
 module Grenade.Layers.Softmax (
     Softmax (..)
   , softmax
@@ -17,8 +24,12 @@ import           Grenade.Core
 import           Numeric.LinearAlgebra.Static as LAS
 
 -- | A Softmax layer
+--
 --   This layer is like a logit layer, but normalises
 --   a set of matricies to be probabilities.
+--
+--   One can use this layer as the last layer in a network
+--   if they need normalised probabilities.
 data Softmax = Softmax
   deriving Show
 

src/Grenade/Layers/Tanh.hs

@@ -3,6 +3,13 @@
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE FlexibleInstances     #-}
 {-# LANGUAGE MultiParamTypeClasses #-}
+{-|
+Module      : Grenade.Layers.Tanh
+Description : Hyperbolic tangent nonlinear layer
+Copyright   : (c) Huw Campbell, 2016-2017
+License     : BSD2
+Stability   : experimental
+-}
 module Grenade.Layers.Tanh (
     Tanh (..)
   ) where

src/Grenade/Layers/Trivial.hs

@@ -3,6 +3,13 @@
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE MultiParamTypeClasses #-}
 {-# LANGUAGE FlexibleInstances     #-}
+{-|
+Module      : Grenade.Core.Trivial
+Description : Trivial layer which perfoms no operations on the data
+Copyright   : (c) Huw Campbell, 2016-2017
+License     : BSD2
+Stability   : experimental
+-}
 module Grenade.Layers.Trivial (
     Trivial (..)
   ) where
@@ -11,7 +18,10 @@ import           Data.Serialize
 
 import           Grenade.Core
 
--- | A trivial layer.
+-- | A Trivial layer.
+--
+--   This can be used to pass an unchanged value up one side of a
+--   graph, for a Residual network for example.
 data Trivial = Trivial
   deriving Show