857 lines
34 KiB
Julia
857 lines
34 KiB
Julia
module learn
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export learn!, compute_paramsChange!
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using Statistics, Random, LinearAlgebra, JSON3, Flux, CUDA, Dates
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using GeneralUtils
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using ..type, ..snnUtil
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#------------------------------------------------------------------------------------------------100
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function compute_paramsChange!(kfn::kfn_1, modelError::CuArray, outputError::CuArray, label)
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lifComputeParamsChange!(kfn.timeStep,
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kfn.lif_phi,
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kfn.lif_epsilonRec,
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kfn.lif_eta,
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kfn.lif_eRec,
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kfn.lif_wRec,
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kfn.lif_exInType,
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kfn.lif_wRecChange,
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kfn.on_wOut,
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kfn.lif_firingCounter,
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kfn.lif_firingTargetFrequency,
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kfn.lif_arrayProjection4d,
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kfn.lif_error,
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modelError,
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outputError,
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kfn.inputSize,
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kfn.bk,
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label,
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)
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alifComputeParamsChange!(kfn.timeStep,
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kfn.alif_phi,
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kfn.alif_epsilonRec,
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kfn.alif_eta,
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kfn.alif_eRec,
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kfn.alif_wRec,
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kfn.alif_exInType,
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kfn.alif_wRecChange,
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kfn.on_wOut,
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kfn.alif_firingCounter,
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kfn.alif_firingTargetFrequency,
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kfn.alif_arrayProjection4d,
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kfn.alif_error,
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modelError,
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outputError,
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kfn.inputSize,
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kfn.bk,
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label,
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kfn.alif_epsilonRecA,
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kfn.alif_beta,
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)
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onComputeParamsChange!(kfn.on_phi,
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kfn.on_epsilonRec,
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kfn.on_eta,
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kfn.on_eRec,
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kfn.on_wOutChange,
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kfn.on_arrayProjection4d,
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kfn.on_error,
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outputError,
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)
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# error("DEBUG -> kfn compute_paramsChange! $(Dates.now())")
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end
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function lifComputeParamsChange!( timeStep::CuArray,
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phi::CuArray,
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epsilonRec::CuArray,
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eta::CuArray,
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eRec::CuArray,
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wRec::CuArray,
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exInType::CuArray,
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wRecChange::CuArray,
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wOut::CuArray,
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firingCounter::CuArray,
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firingTargetFrequency::CuArray,
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arrayProjection4d::CuArray,
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nError::CuArray,
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modelError::CuArray,
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outputError::CuArray,
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inputSize::CuArray,
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bk::CuArray,
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label,
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)
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eRec .= phi .* epsilonRec
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# 2D wRec matrix contain input, lif, alif neurons. I need only lif neurons
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startIndex = prod(inputSize) +1
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stopIndex = startIndex + size(wRec, 3) -1
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startCol = CartesianIndices(wRec)[startIndex][2]
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stopCol = CartesianIndices(wRec)[stopIndex][2]
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# some RSNN neuron that has direct connection to output neuron need to get Bjk
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# from output neuron that represent correct answer, the rest of RSNN get random Bjk
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onW = @view(wOut[:, startCol:stopCol, sum(label), 1])
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_bk = @view(bk[:, startCol:stopCol, 1])
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mask = iszero.(onW)
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bk_ = mask .* _bk
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bkComposed = onW .+ bk_
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nError = bkComposed .* modelError
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nError = reshape(nError, (1,1,:,1))
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# _,_,i3,_ = size(wOut)
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# for i in 1:i3
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# # nError a.k.a. learning signal use dopamine concept,
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# # this neuron receive summed error signal (modelError)
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# onW = @view(wOut[:, startCol:stopCol, i, 1])
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# _bk = @view(bk[:, startCol:stopCol, i, 1])
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# mask = (iszero).(onW)
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# bk_ = mask .* _bk
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# bkComposed = onW .+ bk_
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# nError = bkComposed .* modelError
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# nError = reshape(nError, (1,1,:,1))
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# # compute wRecChange of all neurons wrt to iᵗʰ output neuron
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# wRecChange .+= (eta .* nError .* eRec)
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# end
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# compute wRecChange of all neurons wrt to iᵗʰ output neuron
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wRecChange .+= (eta .* nError .* eRec)
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# frequency regulator
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wRecChange .+= 0.001 .* ((firingTargetFrequency - (firingCounter./timeStep)) ./ timeStep) .*
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eta .* eRec
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# if sum(timeStep) == 785
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# epsilonRec_cpu = epsilonRec |> cpu
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# println("modelError $modelError $(size(modelError))", modelError)
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# println("")
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# println("wOutSum $(size(wOutSum))")
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# wchange = (eta .* nError .* eRec) |> cpu
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# println("wchange 5 1 ", wchange[:,:,5,1])
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# println("")
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# println("epsilonRec 5 1 ", epsilonRec_cpu[:,:,5,1])
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# println("")
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# error("DEBUG lifComputeParamsChange!")
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# end
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# error("DEBUG lifComputeParamsChange!")
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# reset epsilonRec
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epsilonRec .= 0
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end
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function alifComputeParamsChange!( timeStep::CuArray,
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phi::CuArray,
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epsilonRec::CuArray,
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eta::CuArray,
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eRec::CuArray,
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wRec::CuArray,
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exInType::CuArray,
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wRecChange::CuArray,
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wOut::CuArray,
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firingCounter::CuArray,
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firingTargetFrequency::CuArray,
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arrayProjection4d::CuArray,
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nError::CuArray,
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modelError::CuArray,
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outputError::CuArray,
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inputSize::CuArray,
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bk::CuArray,
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label,
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epsilonRecA::CuArray,
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beta::CuArray,
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)
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eRec .= phi .* (epsilonRec .- (beta .* epsilonRecA)) # use eq. 25
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# 2D wRec matrix contain input, lif, alif neurons. I need only lif neurons
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startIndex = prod(inputSize) +1
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stopIndex = startIndex + size(wRec, 3) -1
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startCol = CartesianIndices(wRec)[startIndex][2]
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stopCol = CartesianIndices(wRec)[stopIndex][2]
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# some RSNN neuron that has direct connection to output neuron need to get Bjk
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# from output neuron that represent correct answer, the rest of RSNN get random Bjk
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onW = @view(wOut[:, startCol:stopCol, sum(label), 1])
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_bk = @view(bk[:, startCol:stopCol, 1])
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mask = iszero.(onW)
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bk_ = mask .* _bk
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bkComposed = onW .+ bk_
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nError = bkComposed .* modelError
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nError = reshape(nError, (1,1,:,1))
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wRecChange .+= (eta .* nError .* eRec)
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# frequency regulator
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wRecChange .+= 0.001 .* ((firingTargetFrequency - (firingCounter./timeStep)) ./ timeStep) .*
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eta .* eRec
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# reset epsilonRec
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epsilonRec .= 0
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epsilonRecA .= 0
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# error("DEBUG -> alifComputeParamsChange! $(Dates.now())")
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end
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function onComputeParamsChange!(phi::CuArray,
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epsilonRec::CuArray,
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eta::CuArray,
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eRec::CuArray,
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wOutChange::CuArray,
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arrayProjection4d::CuArray,
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nError::CuArray,
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outputError::CuArray # outputError is output neuron's error
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)
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eRec .= phi .* epsilonRec
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nError .= reshape(outputError, (1, 1, :, size(outputError, 2))) .* arrayProjection4d
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wOutChange .+= (-eta .* nError .* eRec)
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# reset epsilonRec
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epsilonRec .= 0
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# error("DEBUG -> onComputeParamsChange! $(Dates.now())")
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end
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function lifComputeParamsChange!( phi::AbstractArray,
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epsilonRec::AbstractArray,
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eta::AbstractArray,
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wRec::AbstractArray,
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wRecChange::AbstractArray,
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wOut::AbstractArray,
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modelError::AbstractArray)
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d1, d2, d3, d4 = size(epsilonRec)
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# Bₖⱼ in paper, sum() to get each neuron's total wOut weight
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wOutSum = reshape(sum(wOut, dims=3), (d1, :, d4))
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for j in 1:d4, i in 1:d3 # compute along neurons axis of every batch
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# how much error of this neuron 1-spike causing each output neuron's error
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view(wRecChange, :, :, i, j) .+= (-1 * view(eta, :, :, i, j)[1]) .*
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# eRec
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(
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(view(phi, :, :, i, j)[1] .* view(epsilonRec, :, :, i, j)) .*
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# nError a.k.a. learning signal
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(
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view(modelError, :, j)[1] * # dopamine concept, this neuron receive summed error signal
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# RSNN neuron's total wOut weight (neuron synaptic subscription .* wOutSum)
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view(wOutSum, :, :, j)[i]
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)
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)
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end
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end
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function alifComputeParamsChange!( phi::AbstractArray,
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epsilonRec::AbstractArray,
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epsilonRecA::AbstractArray,
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eta::AbstractArray,
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wRec::AbstractArray,
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wRecChange::AbstractArray,
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beta::AbstractArray,
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wOut::AbstractArray,
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modelError::AbstractArray)
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d1, d2, d3, d4 = size(epsilonRec)
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# Bₖⱼ in paper, sum() to get each neuron's total wOut weight
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wOutSum = reshape(sum(wOut, dims=3), (d1, :, d4))
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for j in 1:d4, i in 1:d3 # compute along neurons axis of every batch
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# how much error of this neuron 1-spike causing each output neuron's error
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view(wRecChange, :, :, i, j) .+= (-1 * view(eta, :, :, i, j)[1]) .*
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# eRec
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(
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# eRec_v
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(view(phi, :, :, i, j)[1] .* view(epsilonRec, :, :, i, j)) .+
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# eRec_a
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((view(phi, :, :, i, j)[1] * view(beta, :, :, i, j)[1]) .*
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view(epsilonRecA, :, :, i, j))
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) .*
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# nError a.k.a. learning signal
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(
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view(modelError, :, j)[1] *
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# RSNN neuron's total wOut weight (neuron synaptic subscription .* wOutSum)
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view(wOutSum, :, :, j)[i]
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# sum(GeneralUtils.isNotEqual.(view(wRec, :, :, i, j), 0) .*
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# view(wOutSum, :, :, j))
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)
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end
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end
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function onComputeParamsChange!(phi::AbstractArray,
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epsilonRec::AbstractArray,
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eta::AbstractArray,
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wOutChange::AbstractArray,
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outputError::AbstractArray)
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d1, d2, d3, d4 = size(epsilonRec)
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for j in 1:d4, i in 1:d3 # compute along neurons axis of every batch
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# how much error of this neuron 1-spike causing each output neuron's error
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view(wOutChange, :, :, i, j) .+= (-1 * view(eta, :, :, i, j)[1]) .*
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# eRec
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(
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(view(phi, :, :, i, j)[1] .* view(epsilonRec, :, :, i, j)) .*
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# nError a.k.a. learning signal, output neuron receives error of its own answer - correct answer.
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view(outputError, :, j)[i]
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)
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end
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end
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function learn!(kfn::kfn_1, progress, device=cpu)
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# lif learn
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kfn.lif_wRec, kfn.lif_neuronInactivityCounter, kfn.lif_synapseReconnectDelay =
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lifLearn(kfn.lif_wRec,
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kfn.lif_wRecChange,
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kfn.lif_exInType,
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kfn.lif_arrayProjection4d,
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kfn.lif_neuronInactivityCounter,
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kfn.lif_synapseReconnectDelay,
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kfn.lif_synapseConnectionNumber,
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kfn.lif_synapticActivityCounter,
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kfn.lif_eta,
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kfn.lif_vt,
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kfn.zitCumulative,
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progress,
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device)
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# alif learn
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kfn.alif_wRec, kfn.alif_neuronInactivityCounter, kfn.alif_synapseReconnectDelay =
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alifLearn(kfn.alif_wRec,
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kfn.alif_wRecChange,
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kfn.alif_exInType,
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kfn.alif_arrayProjection4d,
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kfn.alif_neuronInactivityCounter,
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kfn.alif_synapseReconnectDelay,
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kfn.alif_synapseConnectionNumber,
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kfn.alif_synapticActivityCounter,
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kfn.alif_eta,
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kfn.alif_vt,
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kfn.zitCumulative,
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progress,
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device)
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# on learn
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onLearn!(kfn.on_wOut,
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kfn.on_wOutChange,
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kfn.on_arrayProjection4d)
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# wrap up learning session
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if kfn.learningStage == [3]
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kfn.learningStage = [0]
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end
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# error("DEBUG -> kfn learn! $(Dates.now())")
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end
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# function lifLearn(wRec,
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# exInType,
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# wRecChange,
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# arrayProjection4d,
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# neuronInactivityCounter,
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# synapseReconnectDelay,
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# synapseConnectionNumber,
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# synapticWChangeCounter, #TODO
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# eta,
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# zitCumulative,
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# device)
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# # merge learning weight with average learning weight of all batch
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# wch = sum(wRecChange, dims=4) ./ (size(wRec, 4)) .* arrayProjection4d
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# wRec .= (exInType .* wRec) .+ wch
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# arrayProjection4d_cpu = arrayProjection4d |> cpu
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# wRec_cpu = wRec |> cpu
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# wRec_cpu = wRec_cpu[:,:,:,1] # since every batch has the same neuron wRec, (row, col, n)
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# eta_cpu = eta |> cpu
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# eta_cpu = eta_cpu[:,:,:,1]
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# neuronInactivityCounter_cpu = neuronInactivityCounter |> cpu
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# neuronInactivityCounter_cpu = neuronInactivityCounter_cpu[:,:,:,1] # (row, col, n)
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# synapseReconnectDelay_cpu = synapseReconnectDelay |> cpu
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# synapseReconnectDelay_cpu = synapseReconnectDelay_cpu[:,:,:,1]
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# zitCumulative_cpu = zitCumulative |> cpu
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# zitCumulative_cpu = zitCumulative_cpu[:,:,1] # (row, col)
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# # -W if less than 10% of repeat avg, +W otherwise
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# _, _, i3 = size(wRec_cpu)
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# for i in 1:i3
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# x = 0.1 * (sum(synapseReconnectDelay[:,:,i]) / length(synapseReconnectDelay[:,:,i]))
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# mask = GeneralUtils.replaceLessThan.(wRec_cpu[:,:,i], x, -1, 1)
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# wRec_cpu[:,:,i] .+= mask .* eta_cpu[:,:,i] .* wRec_cpu[:,:,i]
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# end
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# # weak / negative synaptic connection will get randomed in neuroplasticity()
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# wRec_cpu = GeneralUtils.replaceBetween.(wRec_cpu, 0.0, 0.01, -1.0) # mark with -1.0
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# # neuroplasticity, work on CPU side
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# wRec_cpu = neuroplasticity(synapseConnectionNumber,
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# zitCumulative_cpu,
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# wRec_cpu,
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# neuronInactivityCounter_cpu,
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# synapseReconnectDelay_cpu)
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# wRec_cpu = wRec_cpu .* arrayProjection4d_cpu
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# wRec = wRec_cpu |> device
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# neuronInactivityCounter_cpu = neuronInactivityCounter_cpu .* arrayProjection4d_cpu
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# neuronInactivityCounter = neuronInactivityCounter_cpu |> device
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# synapseReconnectDelay_cpu = synapseReconnectDelay_cpu .* arrayProjection4d_cpu
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# synapseReconnectDelay = synapseReconnectDelay_cpu |> device
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# return wRec, neuronInactivityCounter, synapseReconnectDelay
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# end
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function lifLearn(wRec,
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wRecChange,
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exInType,
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arrayProjection4d,
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neuronInactivityCounter,
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synapseReconnectDelay,
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synapseConnectionNumber,
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synapticActivityCounter,
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eta,
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vt,
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zitCumulative,
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progress,
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device)
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# transfer data to cpu
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arrayProjection4d_cpu = arrayProjection4d |> cpu
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wRec_cpu = wRec |> cpu
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wRecChange_cpu = wRecChange |> cpu
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wRecChange_cpu = wRecChange_cpu[:,:,:,1]
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eta_cpu = eta |> cpu
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eta_cpu = eta_cpu[:,:,:,1]
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exInType_cpu = exInType |> cpu
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exInType_cpu = exInType_cpu[:,:,:,1]
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neuronInactivityCounter_cpu = neuronInactivityCounter |> cpu
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neuronInactivityCounter_cpu = neuronInactivityCounter_cpu[:,:,:,1] # (row, col, n)
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synapseReconnectDelay_cpu = synapseReconnectDelay |> cpu
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synapseReconnectDelay_cpu = synapseReconnectDelay_cpu[:,:,:,1]
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synapticActivityCounter_cpu = synapticActivityCounter |> cpu
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synapticActivityCounter_cpu = synapticActivityCounter_cpu[:,:,:,1]
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zitCumulative_cpu = zitCumulative |> cpu
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zitCumulative_cpu = zitCumulative_cpu[:,:,1]
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# neuroplasticity, work on CPU side
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wRec_cpu, neuronInactivityCounter_cpu, synapseReconnectDelay_cpu =
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neuroplasticity(synapseConnectionNumber,
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zitCumulative_cpu,
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wRec_cpu,
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exInType_cpu,
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wRecChange_cpu,
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vt,
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eta,
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neuronInactivityCounter_cpu,
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synapseReconnectDelay_cpu,
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synapticActivityCounter_cpu,
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progress,)
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# # merge learning weight with average learning weight of all batch
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# wch = sum(wRecChange, dims=4) ./ (size(wRec, 4)) .* arrayProjection4d
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# wRec .= (exInType .* wRec) .+ wch
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# # (row, col)
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# # -W if less than 10% of repeat avg, +W otherwise
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# _, _, i3 = size(wRec_cpu)
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# for i in 1:i3
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# x = 0.1 * (sum(synapseReconnectDelay[:,:,i]) / length(synapseReconnectDelay[:,:,i]))
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# mask = GeneralUtils.replaceLessThan.(wRec_cpu[:,:,i], x, -1, 1)
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# wRec_cpu[:,:,i] .+= mask .* eta_cpu[:,:,i] .* wRec_cpu[:,:,i]
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# end
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# # weak / negative synaptic connection will get randomed in neuroplasticity()
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# wRec_cpu = GeneralUtils.replaceBetween.(wRec_cpu, 0.0, 0.01, -1.0) # mark with -1.0
|
|
|
|
# neuroplasticity, work on CPU side
|
|
# wRec_cpu = neuroplasticity(synapseConnectionNumber,
|
|
# zitCumulative_cpu,
|
|
# wRec_cpu,
|
|
# wRecChange_cpu,
|
|
# vt,
|
|
# neuronInactivityCounter_cpu,
|
|
# synapseReconnectDelay_cpu)
|
|
|
|
# transfer data backto gpu
|
|
wRec_cpu = wRec_cpu .* arrayProjection4d_cpu
|
|
wRec = wRec_cpu |> device
|
|
neuronInactivityCounter_cpu = neuronInactivityCounter_cpu .* arrayProjection4d_cpu
|
|
neuronInactivityCounter = neuronInactivityCounter_cpu |> device
|
|
synapseReconnectDelay_cpu = synapseReconnectDelay_cpu .* arrayProjection4d_cpu
|
|
synapseReconnectDelay = synapseReconnectDelay_cpu |> device
|
|
|
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return wRec, neuronInactivityCounter, synapseReconnectDelay
|
|
end
|
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|
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function alifLearn(wRec,
|
|
wRecChange,
|
|
exInType,
|
|
arrayProjection4d,
|
|
neuronInactivityCounter,
|
|
synapseReconnectDelay,
|
|
synapseConnectionNumber,
|
|
synapticActivityCounter,
|
|
eta,
|
|
vt,
|
|
zitCumulative,
|
|
progress,
|
|
device)
|
|
|
|
# merge learning weight with average learning weight of all batch
|
|
wch = sum(wRecChange, dims=4) ./ (size(wRec, 4)) .* arrayProjection4d
|
|
wRec .= (exInType .* wRec) .+ wch
|
|
|
|
arrayProjection4d_cpu = arrayProjection4d |> cpu
|
|
wRec_cpu = wRec |> cpu
|
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wRec_cpu = wRec_cpu[:,:,:,1] # since every batch has the same neuron wRec, (row, col, n)
|
|
eta_cpu = eta |> cpu
|
|
eta_cpu = eta_cpu[:,:,:,1]
|
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neuronInactivityCounter_cpu = neuronInactivityCounter |> cpu
|
|
neuronInactivityCounter_cpu = neuronInactivityCounter_cpu[:,:,:,1] # (row, col, n)
|
|
synapseReconnectDelay_cpu = synapseReconnectDelay |> cpu
|
|
synapseReconnectDelay_cpu = synapseReconnectDelay_cpu[:,:,:,1]
|
|
zitCumulative_cpu = zitCumulative |> cpu
|
|
zitCumulative_cpu = zitCumulative_cpu[:,:,1] # (row, col)
|
|
|
|
# -W if less than 10% of repeat avg, +W otherwise
|
|
_, _, i3 = size(wRec_cpu)
|
|
for i in 1:i3
|
|
x = 0.1 * (sum(synapseReconnectDelay[:,:,i]) / length(synapseReconnectDelay[:,:,i]))
|
|
mask = GeneralUtils.replaceLessThan.(wRec_cpu[:,:,i], x, -1, 1)
|
|
wRec_cpu[:,:,i] .+= mask .* eta_cpu[:,:,i] .* wRec_cpu[:,:,i]
|
|
end
|
|
|
|
# weak / negative synaptic connection will get randomed in neuroplasticity()
|
|
wRec_cpu = GeneralUtils.replaceBetween.(wRec_cpu, 0.0, 0.01, -1.0) # mark with -1.0
|
|
|
|
# neuroplasticity, work on CPU side
|
|
wRec_cpu = neuroplasticity(synapseConnectionNumber,
|
|
zitCumulative_cpu,
|
|
wRec_cpu,
|
|
neuronInactivityCounter_cpu,
|
|
synapseReconnectDelay_cpu)
|
|
|
|
wRec_cpu = wRec_cpu .* arrayProjection4d_cpu
|
|
wRec = wRec_cpu |> device
|
|
|
|
neuronInactivityCounter_cpu = neuronInactivityCounter_cpu .* arrayProjection4d_cpu
|
|
neuronInactivityCounter = neuronInactivityCounter_cpu |> device
|
|
|
|
synapseReconnectDelay_cpu = synapseReconnectDelay_cpu .* arrayProjection4d_cpu
|
|
synapseReconnectDelay = synapseReconnectDelay_cpu |> device
|
|
|
|
# error("DEBUG -> alifLearn! $(Dates.now())")
|
|
return wRec, neuronInactivityCounter, synapseReconnectDelay
|
|
end
|
|
|
|
function onLearn!(wOut,
|
|
wOutChange,
|
|
arrayProjection4d)
|
|
# merge learning weight with average learning weight
|
|
wOut .+= (sum(wOutChange, dims=4) ./ (size(wOut, 4))) .* arrayProjection4d
|
|
|
|
# adaptive wOut to help convergence using c_decay
|
|
wOut .-= 0.001 .* wOut
|
|
end
|
|
|
|
alltrue(args...) = false ∈ [args...] ? false : true
|
|
isbetween(x, lowerlimit, upperlimit) = lowerlimit < x < upperlimit ? true : false
|
|
|
|
#WORKING 1) implement 90% +w, 10% -w 2) rewrite this function
|
|
function neuroplasticity(synapseConnectionNumber,
|
|
zitCumulative, # (row, col)
|
|
wRec, # (row, col, n)
|
|
exInType,
|
|
wRecChange,
|
|
vt,
|
|
eta,
|
|
neuronInactivityCounter,
|
|
synapseReconnectDelay,
|
|
synapticActivityCounter,
|
|
progress,) # (row, col, n)
|
|
i1,i2,i3 = size(wRec)
|
|
println("eta $(size(eta))")
|
|
println("wRec $(size(wRec))")
|
|
error("DEBUG -> neuroplasticity $(Dates.now())")
|
|
|
|
if progress == 2 # no need to learn
|
|
# skip neuroplasticity
|
|
#TODO I may need to do something with neuronInactivityCounter and other variables
|
|
wRecChange .= 0
|
|
elseif progress == 1 # progress increase
|
|
# ready to reconnect synapse must not have wRecChange
|
|
mask = (!isequal).(wRec, 0)
|
|
wRecChange .*= mask
|
|
|
|
# merge learning weight with average learning weight of all batch
|
|
wRec .= abs.((exInType .* wRec) .+ wRecChange) # abs because wRec doesn't carry sign
|
|
|
|
# seperate active synapse out of inactive in this signal
|
|
mask_inactiveSynapse = isequal.(synapticActivityCounter, 0)
|
|
mask_activeSynapse = (!isequal).(synapticActivityCounter, 0)
|
|
|
|
# adjust weight based on vt progress and repeatition (90% +w, 10% -w) depend on epsilonRec
|
|
avgActivity = sum(synapticActivityCounter) / length(synapticActivityCounter)
|
|
lowerlimit = 0.1 * avgActivity
|
|
|
|
# +w, synapse with more than 10% of avg activity get increase weight by eta
|
|
mask_more = (!isless).(synapticActivityCounter, lowerlimit)
|
|
mask_2 = alltrue.(mask_activeSynapse, mask_more)
|
|
mask_2 .*= 1 .+ eta # minor activity synapse weight will be reduced by eta
|
|
wRec .*= mask_2
|
|
|
|
# -w, synapse with less than 10% of avg activity get reduced weight by eta
|
|
mask_less = isless.(synapticActivityCounter, lowerlimit) # 1st criteria
|
|
|
|
mask_3 = alltrue.(mask_activeSynapse, mask_less)
|
|
mask_3 .*= 1 .- eta # minor activity synapse weight will be reduced by eta
|
|
wRec .*= mask_3
|
|
|
|
# -w all non-fire connection except mature connection
|
|
mask_notmature = isless.(wRec, 0.1) # 2nd criteria, not mature synapse has weight < 0.1
|
|
mask_1 = alltrue.(mask_inactiveSynapse, mask_notmature)
|
|
mask_1 .*= 1 .- eta
|
|
wRec .*= mask_1
|
|
|
|
#WORKING prune weak connection
|
|
# mark weak / negative synaptic connection so they will get randomed in neuroplasticity()
|
|
mask_weak = isbetween.(wRec, 0.0, 0.01)
|
|
mask_notweak = (!isbetween).(wRec, 0.0, 0.01)
|
|
wRec .*= mask_notweak # all marked weak synapse weight need to be 0.0
|
|
r = rand((1:1000), size(wRec)) # synapse random wait time to reconnect
|
|
r .*= mask_weak
|
|
synapticActivityCounter .*= mask_notweak # all marked weak synapse is set 0
|
|
synapticActivityCounter .+= r # set pruned synapse to random wait time
|
|
|
|
#TODO rewire synapse connection
|
|
|
|
|
|
elseif progress == 0 # no progress, no weight update, only rewire
|
|
|
|
# -w all non-fire connection except mature connection
|
|
|
|
# prune weak connection
|
|
|
|
# rewire synapse connection
|
|
elseif progress == -1 # setback
|
|
# adjust weight based on vt progress and repeatition (90% +w, 10% -w) depend on epsilonRec
|
|
|
|
# -w all non-fire connection except mature connection
|
|
|
|
# prune weak connection
|
|
|
|
# rewire synapse connection
|
|
else
|
|
error("undefined condition line $(@__LINE__)")
|
|
end
|
|
|
|
# error("DEBUG -> neuroplasticity $(Dates.now())")
|
|
|
|
# merge learning weight with average learning weight of all batch
|
|
wRec .= abs.((exInType .* wRec) .+ wRecChange) # abs because wRec doesn't carry sign
|
|
|
|
|
|
# adjust weight based on vt progress and repeatition (90% +w, 10% -w) depend on epsilonRec
|
|
|
|
|
|
|
|
# -w all non-fire connection except mature connection
|
|
|
|
|
|
|
|
# prune weak connection
|
|
|
|
|
|
|
|
|
|
# rewire synapse connection
|
|
|
|
|
|
|
|
# for each neuron, find total number of synaptic conn that should draw
|
|
# new connection to firing and non-firing neurons pool
|
|
subToFireNeuron_toBe = Int(floor(0.7 * synapseConnectionNumber))
|
|
|
|
# for each neuron, count how many synapse already subscribed to firing-neurons
|
|
zw = zitCumulative .* wRec
|
|
subToFireNeuron_current = sum(GeneralUtils.isBetween.(zw, 0.0, 100.0), dims=(1,2)) # (1, 1, n)
|
|
zitMask = (!iszero).(zitCumulative) # zitMask of firing neurons = 1, non-firing = 0
|
|
projection = ones(i1,i2,i3)
|
|
zitMask = zitMask .* projection # (row, col, n)
|
|
totalNewConn = sum(isequal.(wRec, -1.0), dims=(1,2)) # count new conn mark (-1.0), (1, 1, n)
|
|
println("neuroplasticity, from $(synapseConnectionNumber*size(totalNewConn, 3)) conn, $(sum(totalNewConn)) are replaced")
|
|
|
|
# clear -1.0 marker
|
|
GeneralUtils.replaceElements!(wRec, -1.0, synapseReconnectDelay, -0.99)
|
|
GeneralUtils.replaceElements!(wRec, -1.0, 0.0) # -1.0 marker is no longer required
|
|
|
|
for i in 1:i3
|
|
if neuronInactivityCounter[1:1:i][1] < -10000 # neuron die i.e. reset all weight
|
|
println("neuron die")
|
|
neuronInactivityCounter[:,:,i] .= 0 # reset
|
|
w = random_wRec(i1,i2,1,synapseConnectionNumber)
|
|
wRec[:,:,i] .= w
|
|
|
|
a = similar(w) .= -0.99 # synapseConnectionNumber of this neuron
|
|
mask = (!iszero).(w)
|
|
GeneralUtils.replaceElements!(mask, 1, a, 0)
|
|
synapseReconnectDelay[:,:,i] = a
|
|
else
|
|
remaining = 0
|
|
if subToFireNeuron_current[1,1,i] < subToFireNeuron_toBe
|
|
toAddConn = subToFireNeuron_toBe - subToFireNeuron_current[1,1,i]
|
|
totalNewConn[1,1,i] = totalNewConn[1,1,i] - toAddConn
|
|
# add new conn to firing neurons pool
|
|
remaining = addNewSynapticConn!(zitMask[:,:,i], 1,
|
|
@view(wRec[:,:,i]),
|
|
@view(synapseReconnectDelay[:,:,i]),
|
|
toAddConn)
|
|
totalNewConn[1,1,i] += remaining
|
|
end
|
|
|
|
# add new conn to non-firing neurons pool
|
|
remaining = addNewSynapticConn!(zitMask[:,:,i], 0,
|
|
@view(wRec[:,:,i]),
|
|
@view(synapseReconnectDelay[:,:,i]),
|
|
totalNewConn[1,1,i])
|
|
if remaining > 0 # final get-all round if somehow non-firing pool has not enough slot
|
|
remaining = addNewSynapticConn!(zitMask[:,:,i], 1,
|
|
@view(wRec[:,:,i]),
|
|
@view(synapseReconnectDelay[:,:,i]),
|
|
remaining)
|
|
end
|
|
end
|
|
end
|
|
|
|
# error("DEBUG -> neuroplasticity $(Dates.now())")
|
|
return wRec
|
|
end
|
|
|
|
# function neuroplasticity(synapseConnectionNumber,
|
|
# zitCumulative, # (row, col)
|
|
# wRec, # (row, col, n)
|
|
# neuronInactivityCounter,
|
|
# synapseReconnectDelay) # (row, col, n)
|
|
|
|
# i1,i2,i3 = size(wRec)
|
|
|
|
# # for each neuron, find total number of synaptic conn that should draw
|
|
# # new connection to firing and non-firing neurons pool
|
|
# subToFireNeuron_toBe = Int(floor(0.7 * synapseConnectionNumber))
|
|
|
|
# # for each neuron, count how many synap already subscribed to firing-neurons
|
|
# zw = zitCumulative .* wRec
|
|
# subToFireNeuron_current = sum(GeneralUtils.isBetween.(zw, 0.0, 100.0), dims=(1,2)) # (1, 1, n)
|
|
# zitMask = (!iszero).(zitCumulative) # zitMask of firing neurons = 1, non-firing = 0
|
|
# projection = ones(i1,i2,i3)
|
|
# zitMask = zitMask .* projection # (row, col, n)
|
|
# totalNewConn = sum(isequal.(wRec, -1.0), dims=(1,2)) # count new conn mark (-1.0), (1, 1, n)
|
|
# println("neuroplasticity, from $(synapseConnectionNumber*size(totalNewConn, 3)) conn, $(sum(totalNewConn)) are replaced")
|
|
|
|
# # clear -1.0 marker
|
|
# GeneralUtils.replaceElements!(wRec, -1.0, synapseReconnectDelay, -0.99)
|
|
# GeneralUtils.replaceElements!(wRec, -1.0, 0.0) # -1.0 marker is no longer required
|
|
|
|
# for i in 1:i3
|
|
# if neuronInactivityCounter[1:1:i][1] < -10000 # neuron die i.e. reset all weight
|
|
# println("neuron die")
|
|
# neuronInactivityCounter[:,:,i] .= 0 # reset
|
|
# w = random_wRec(i1,i2,1,synapseConnectionNumber)
|
|
# wRec[:,:,i] .= w
|
|
|
|
# a = similar(w) .= -0.99 # synapseConnectionNumber of this neuron
|
|
# mask = (!iszero).(w)
|
|
# GeneralUtils.replaceElements!(mask, 1, a, 0)
|
|
# synapseReconnectDelay[:,:,i] = a
|
|
# else
|
|
# remaining = 0
|
|
# if subToFireNeuron_current[1,1,i] < subToFireNeuron_toBe
|
|
# toAddConn = subToFireNeuron_toBe - subToFireNeuron_current[1,1,i]
|
|
# totalNewConn[1,1,i] = totalNewConn[1,1,i] - toAddConn
|
|
# # add new conn to firing neurons pool
|
|
# remaining = addNewSynapticConn!(zitMask[:,:,i], 1,
|
|
# @view(wRec[:,:,i]),
|
|
# @view(synapseReconnectDelay[:,:,i]),
|
|
# toAddConn)
|
|
# totalNewConn[1,1,i] += remaining
|
|
# end
|
|
|
|
# # add new conn to non-firing neurons pool
|
|
# remaining = addNewSynapticConn!(zitMask[:,:,i], 0,
|
|
# @view(wRec[:,:,i]),
|
|
# @view(synapseReconnectDelay[:,:,i]),
|
|
# totalNewConn[1,1,i])
|
|
# if remaining > 0 # final get-all round if somehow non-firing pool has not enough slot
|
|
# remaining = addNewSynapticConn!(zitMask[:,:,i], 1,
|
|
# @view(wRec[:,:,i]),
|
|
# @view(synapseReconnectDelay[:,:,i]),
|
|
# remaining)
|
|
# end
|
|
# end
|
|
# end
|
|
|
|
# # error("DEBUG -> neuroplasticity $(Dates.now())")
|
|
# return wRec
|
|
# end
|
|
|
|
# learningLiquidity(x) = -0.0001x + 1 # -10000 to +10000; f(x) = -5e-05x+0.5
|
|
|
|
function learningLiquidity(x)
|
|
if x > 10000
|
|
y = 0.0
|
|
elseif x < -10000
|
|
y = 1.0
|
|
else
|
|
y = -5e-05x+0.5 # range -10000 to +10000
|
|
end
|
|
return y
|
|
end
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
end # module |