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💣 Machine learning which might blow up in your face 💣
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src/Grenade.hs
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src/Grenade.hs
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> randomMyNet = randomNetwork
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The function `randomMyNet` witnesses the `CreatableNetwork`
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constraint of the neural network, that is, it ensures the network
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constraint of the neural network, and in doing so, ensures the network
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can be built, and hence, that the architecture is sound.
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-}
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src/Grenade/Layers/Elu.hs
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src/Grenade/Layers/Elu.hs
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{-# LANGUAGE TypeOperators #-}
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{-# LANGUAGE TypeFamilies #-}
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{-# LANGUAGE MultiParamTypeClasses #-}
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{-|
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Module : Grenade.Layers.Logit
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Description : Exponential linear unit layer
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Copyright : (c) Huw Campbell, 2016-2017
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License : BSD2
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Stability : experimental
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-}
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module Grenade.Layers.Elu (
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Elu (..)
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) where
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src/Grenade/Layers/Logit.hs
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src/Grenade/Layers/Logit.hs
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{-# LANGUAGE TypeFamilies #-}
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{-# LANGUAGE FlexibleInstances #-}
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{-# LANGUAGE MultiParamTypeClasses #-}
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{-|
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Module : Grenade.Layers.Logit
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Description : Sigmoid nonlinear layer
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Copyright : (c) Huw Campbell, 2016-2017
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License : BSD2
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Stability : experimental
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-}
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module Grenade.Layers.Logit (
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Logit (..)
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) where
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import Grenade.Core
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-- | A Logit layer.
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--
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-- A layer which can act between any shape of the same dimension, perfoming an sigmoid function.
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-- This layer should be used as the output layer of a network for logistic regression (classification)
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-- problems.
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src/Grenade/Layers/Pad.hs
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src/Grenade/Layers/Pad.hs
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{-# LANGUAGE TypeFamilies #-}
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{-# LANGUAGE MultiParamTypeClasses #-}
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{-# LANGUAGE FlexibleContexts #-}
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{-|
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Module : Grenade.Core.Pad
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Description : Padding layer for 2D and 3D images
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Copyright : (c) Huw Campbell, 2016-2017
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License : BSD2
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Stability : experimental
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-}
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module Grenade.Layers.Pad (
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Pad (..)
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) where
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import Numeric.LinearAlgebra.Static (extract, create)
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-- | A padding layer for a neural network.
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--
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-- Pads on the X and Y dimension of an image.
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data Pad :: Nat
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-> Nat
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-> Nat
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src/Grenade/Layers/Pooling.hs
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src/Grenade/Layers/Pooling.hs
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{-# LANGUAGE TypeFamilies #-}
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{-# LANGUAGE MultiParamTypeClasses #-}
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{-# LANGUAGE FlexibleContexts #-}
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{-|
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Module : Grenade.Core.Pooling
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Description : Max Pooling layer for 2D and 3D images
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Copyright : (c) Huw Campbell, 2016-2017
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License : BSD2
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Stability : experimental
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-}
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module Grenade.Layers.Pooling (
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Pooling (..)
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) where
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import Numeric.LinearAlgebra.Static as LAS hiding ((|||), build, toRows)
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-- | A pooling layer for a neural network.
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--
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-- Does a max pooling, looking over a kernel similarly to the convolution network, but returning
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-- maxarg only. This layer is often used to provide minor amounts of translational invariance.
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--
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src/Grenade/Layers/Relu.hs
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src/Grenade/Layers/Relu.hs
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{-# LANGUAGE TypeOperators #-}
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{-# LANGUAGE TypeFamilies #-}
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{-# LANGUAGE MultiParamTypeClasses #-}
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{-|
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Module : Grenade.Layers.Relu
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Description : Rectifying linear unit layer
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Copyright : (c) Huw Campbell, 2016-2017
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License : BSD2
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Stability : experimental
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-}
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module Grenade.Layers.Relu (
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Relu (..)
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) where
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src/Grenade/Layers/Reshape.hs
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src/Grenade/Layers/Reshape.hs
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{-# LANGUAGE TypeFamilies #-}
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{-# LANGUAGE MultiParamTypeClasses #-}
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{-# LANGUAGE FlexibleContexts #-}
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{-|
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Module : Grenade.Layers.Reshape
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Description : Multipurpose reshaping layer
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Copyright : (c) Huw Campbell, 2016-2017
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License : BSD2
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Stability : experimental
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-}
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module Grenade.Layers.Reshape (
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Reshape (..)
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) where
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-- | Reshape Layer
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--
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-- Flattens input down to D1 from either 2D or 3D data.
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--
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-- Casts input D1 up to either 2D or 3D data if the shapes are good.
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-- The Reshape layer can flatten any 2D or 3D image to 1D vector with the
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-- same number of activations, as well as cast up from 1D to a 2D or 3D
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-- shape.
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--
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-- Can also be used to turn a 3D image with only one channel into a 2D image
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-- or vice versa.
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src/Grenade/Layers/Softmax.hs
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src/Grenade/Layers/Softmax.hs
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{-# LANGUAGE TypeFamilies #-}
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{-# LANGUAGE FlexibleInstances #-}
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{-# LANGUAGE MultiParamTypeClasses #-}
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{-|
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Module : Grenade.Core.Softmax
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Description : Softmax loss layer
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Copyright : (c) Huw Campbell, 2016-2017
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License : BSD2
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Stability : experimental
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-}
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module Grenade.Layers.Softmax (
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Softmax (..)
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, softmax
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import Numeric.LinearAlgebra.Static as LAS
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-- | A Softmax layer
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--
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-- This layer is like a logit layer, but normalises
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-- a set of matricies to be probabilities.
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--
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-- One can use this layer as the last layer in a network
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-- if they need normalised probabilities.
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data Softmax = Softmax
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deriving Show
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src/Grenade/Layers/Tanh.hs
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src/Grenade/Layers/Tanh.hs
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{-# LANGUAGE TypeFamilies #-}
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{-# LANGUAGE FlexibleInstances #-}
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{-# LANGUAGE MultiParamTypeClasses #-}
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{-|
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Module : Grenade.Layers.Tanh
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Description : Hyperbolic tangent nonlinear layer
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Copyright : (c) Huw Campbell, 2016-2017
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License : BSD2
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Stability : experimental
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-}
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module Grenade.Layers.Tanh (
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Tanh (..)
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) where
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src/Grenade/Layers/Trivial.hs
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src/Grenade/Layers/Trivial.hs
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{-# LANGUAGE TypeFamilies #-}
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{-# LANGUAGE MultiParamTypeClasses #-}
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{-# LANGUAGE FlexibleInstances #-}
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{-|
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Module : Grenade.Core.Trivial
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Description : Trivial layer which perfoms no operations on the data
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Copyright : (c) Huw Campbell, 2016-2017
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License : BSD2
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Stability : experimental
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-}
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module Grenade.Layers.Trivial (
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Trivial (..)
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) where
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import Grenade.Core
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-- | A trivial layer.
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-- | A Trivial layer.
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--
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-- This can be used to pass an unchanged value up one side of a
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-- graph, for a Residual network for example.
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data Trivial = Trivial
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deriving Show
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