beta
This commit is contained in:
@@ -27,8 +27,8 @@ using .interface
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""" version 0.0.9
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Todo:
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[0*] change madel error calculation in user script, (progress based)
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[1] +W 90% of most active conn
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[DONE] change madel error calculation in user script, (progress based)
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[1*] +W 90% of most active conn
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[2] -W 10% of less active conn
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[3] synapse reconnect delay counter
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[-] add temporal summation in addition to already used spatial summation.
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138
src/learn.jl
138
src/learn.jl
@@ -8,8 +8,8 @@ using ..type, ..snnUtil
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#------------------------------------------------------------------------------------------------100
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function compute_paramsChange!(kfn::kfn_1, modelError, outputError)
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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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@@ -24,29 +24,35 @@ function compute_paramsChange!(kfn::kfn_1, modelError, outputError)
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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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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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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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@@ -58,7 +64,7 @@ function compute_paramsChange!(kfn::kfn_1, modelError, 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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#WORKING implement 90% +w, 10% -w
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function lifComputeParamsChange!( timeStep::CuArray,
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phi::CuArray,
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epsilonRec::CuArray,
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@@ -73,45 +79,59 @@ function lifComputeParamsChange!( timeStep::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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# Bₖⱼ in paper, sum() to get each neuron's total wOut weight,
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# use absolute because only magnitude is needed
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wOutSum_all = reshape( abs.(sum(wOut, dims=3)), (1,1,:, size(wOut, 4)) ) # (1,1,allNeuron,batch)
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# get only each lif neuron's wOut, leaving out other neuron's wOut
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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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wOutSum = @view(wOutSum_all[1,1, startIndex:stopIndex, :])
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wOutSum = reshape(wOutSum, (1, 1, size(wOutSum, 1), size(wOutSum, 2))) # (1,1,n,batch)
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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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nError .= (modelError .* wOutSum) .* arrayProjection4d
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eRec .= phi .* epsilonRec
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wRecChange .+= (-eta .* nError .* eRec)
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startCol = CartesianIndices(wRec)[startIndex][2]
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stopCol = CartesianIndices(wRec)[stopIndex][2]
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_bk = @view(bk[:, startCol:stopCol, 1])
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nError = _bk .* 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)
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# println("modelError $modelError $(size(modelError))", modelError)
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# println("")
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# wchange = (-eta .* nError .* eRec) |> cpu
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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("wchange 5 2 ", wchange[:,:,5,2])
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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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# println("epsilonRec 5 2 ", epsilonRec_cpu[:,:,5,2])
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# println("")
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# error("DEBUG lifComputeParamsChange!")
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# end
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# reset epsilonRec
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epsilonRec .= 0
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end
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@@ -130,24 +150,28 @@ function alifComputeParamsChange!( timeStep::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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beta::CuArray,
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)
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# Bₖⱼ in paper, sum() to get each neuron's total wOut weight,
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# use absolute because only magnitude is needed
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wOutSum_all = reshape( abs.(sum(wOut, dims=3)), (1,1,:, size(wOut, 4)) ) # (1,1,allNeuron,batch)
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# get only each lif neuron's wOut, leaving out other neuron's wOut
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wOutSum = @view(wOutSum_all[1,1, end-size(wRec, 3)+1:end, :])
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wOutSum = reshape(wOutSum, (1, 1, size(wOutSum, 1), size(wOutSum, 2))) # (1,1,n,batch)
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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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nError .= (modelError .* wOutSum) .* arrayProjection4d
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eRec .= phi .* (epsilonRec .- (beta .* epsilonRecA)) # use eq. 25
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wRecChange .+= (-eta .* nError .* eRec)
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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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_bk = @view(bk[:, startCol:stopCol, 1])
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nError = _bk .* 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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@@ -395,8 +419,8 @@ function lifLearn(wRec,
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zitCumulative_cpu = zitCumulative_cpu[:,:,1]
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#TODO neuroplasticity, work on CPU side
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wRec_cpu, neuronInactivityCounter_cpu, synapseReconnectDelayCounter_cpu,
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= neuroplasticity(synapseConnectionNumber,
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wRec_cpu, neuronInactivityCounter_cpu, synapseReconnectDelayCounter_cpu =
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neuroplasticity(synapseConnectionNumber,
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zitCumulative_cpu,
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wRec_cpu,
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wRecChange_cpu,
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@@ -28,6 +28,7 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
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exInType::Union{AbstractArray, Nothing} = nothing
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modelError::Union{AbstractArray, Nothing} = nothing # store RSNN error
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outputError::Union{AbstractArray, Nothing} = nothing # store output neurons error
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bk::Union{AbstractArray, Nothing} = nothing # Bⱼₖ
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# ---------------------------------------------------------------------------- #
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# LIF Neurons #
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@@ -190,6 +191,7 @@ function kfn_1(params::Dict; device=cpu)
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kfn.zit = zeros(row, col, batch) |> device
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kfn.zitCumulative = (similar(kfn.zit) .= 0)
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kfn.modelError = zeros(1) |> device
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kfn.bk = rand(size(kfn.zit)...) |> device
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# ---------------------------------------------------------------------------- #
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# LIF config #
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@@ -222,7 +224,7 @@ function kfn_1(params::Dict; device=cpu)
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kfn.lif_eta = (similar(kfn.lif_wRec) .= 0.01)
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kfn.lif_gammaPd = (similar(kfn.lif_wRec) .= 0.3)
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kfn.lif_wRecChange = (similar(kfn.lif_wRec) .= 0)
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kfn.lif_error = (similar(kfn.lif_wRec) .= 0)
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kfn.lif_error = (similar(kfn.lif_wRec) .= 0)
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kfn.lif_firingCounter = (similar(kfn.lif_wRec) .= 0)
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kfn.lif_firingTargetFrequency = (similar(kfn.lif_wRec) .= 0.1)
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@@ -280,7 +282,7 @@ function kfn_1(params::Dict; device=cpu)
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kfn.alif_eta = (similar(kfn.alif_wRec) .= 0.01)
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kfn.alif_gammaPd = (similar(kfn.alif_wRec) .= 0.3)
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kfn.alif_wRecChange = (similar(kfn.alif_wRec) .= 0)
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kfn.alif_error = (similar(kfn.alif_wRec) .= 0)
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kfn.alif_error = (similar(kfn.alif_wRec) .= 0)
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kfn.alif_firingCounter = (similar(kfn.alif_wRec) .= 0)
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kfn.alif_firingTargetFrequency = (similar(kfn.alif_wRec) .= 0.1)
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