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💣 Machine learning which might blow up in your face 💣
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1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768{-# LANGUAGE DataKinds #-}{-# LANGUAGE GADTs #-}{-# LANGUAGE TypeOperators #-}{-# LANGUAGE TypeFamilies #-}{-# LANGUAGE MultiParamTypeClasses #-}{-# LANGUAGE FlexibleContexts #-}{-# LANGUAGE RankNTypes #-}{-# LANGUAGE FlexibleInstances #-}{-# LANGUAGE ScopedTypeVariables #-}{-|Module : Grenade.Core.NetworkDescription : Inception style parallel convolutional network composition.Copyright : (c) Huw Campbell, 2016-2017License : BSD2Stability : experimental
Export an Inception style type, which can be used to build upcomplex multiconvolution size networks.-}module Grenade.Layers.Inception ( Inception , InceptionMini , Resnet ) where
import GHC.TypeLits
import Grenade.Coreimport Grenade.Layers.Convolutionimport Grenade.Layers.Padimport Grenade.Layers.Concatimport Grenade.Layers.Mergeimport Grenade.Layers.Trivial
-- | Type of an inception layer.---- It looks like a bit of a handful, but is actually pretty easy to use.---- The first three type parameters are the size of the (3D) data the-- inception layer will take. It will emit 3D data with the number of-- channels being the sum of @chx@, @chy@, @chz@, which are the number-- of convolution filters in the 3x3, 5x5, and 7x7 convolutions Layers-- respectively.---- The network get padded effectively before each convolution filters-- such that the output dimension is the same x and y as the input.type Inception rows cols channels chx chy chz = Network '[ Concat ('D3 rows cols (chx + chy)) (InceptionMini rows cols channels chx chy) ('D3 rows cols chz) (Inception7x7 rows cols channels chz) ] '[ 'D3 rows cols channels, 'D3 rows cols (chx + chy + chz) ]
type InceptionMini rows cols channels chx chy = Network '[ Concat ('D3 rows cols chx) (Inception3x3 rows cols channels chx) ('D3 rows cols chy) (Inception5x5 rows cols channels chy) ] '[ 'D3 rows cols channels, 'D3 rows cols (chx + chy) ]
type Inception3x3 rows cols channels chx = Network '[ Pad 1 1 1 1, Convolution channels chx 3 3 1 1 ] '[ 'D3 rows cols channels, 'D3 (rows + 2) (cols + 2) channels, 'D3 rows cols chx ]
type Inception5x5 rows cols channels chx = Network '[ Pad 2 2 2 2, Convolution channels chx 5 5 1 1 ] '[ 'D3 rows cols channels, 'D3 (rows + 4) (cols + 4) channels, 'D3 rows cols chx ]
type Inception7x7 rows cols channels chx = Network '[ Pad 3 3 3 3, Convolution channels chx 7 7 1 1 ] '[ 'D3 rows cols channels, 'D3 (rows + 6) (cols + 6) channels, 'D3 rows cols chx ]
type Resnet branch = Merge Trivial branch