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lif forward

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This commit is contained in:
ton
2023-07-23 11:03:08 +07:00
parent 6b71450055
commit b7c87bd0fa
3 changed files with 114 additions and 84 deletions
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141
src/forward.jl
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@@ -17,91 +17,114 @@ function (kfn::kfn_1)(input::AbstractArray)
end end
println(">>> input ", size(input)) println(">>> input ", size(input))
println(">>> zit ", size(kfn.zit))
# pass input_data into input neuron. # println(">>> lif_zit ", size(kfn.lif_zit))
GeneralUtils.cartesianAssign!(kfn.z_i_t, input) # println(">>> lif_recSignal ", size(kfn.lif_recSignal))
kfn.lif_z_i_t = GeneralUtils.matMul_3Dto4D_batchwise(kfn.z_i_t, println(">>> lif_wRec ", size(kfn.lif_wRec))
ones(size(kfn.z_i_t)[1], size(kfn.z_i_t)[2], size(kfn.lif_w)[3], size(kfn.z_i_t)[3])) println(">>> lif_refractoryCounter ", size(kfn.lif_refractoryCounter))
println(">>> z_i_t ", size(kfn.z_i_t))
println(">>> lif_z_i_t ", size(kfn.lif_z_i_t))
println(">>> lif_recSignal ", size(kfn.lif_recSignal))
println(">>> lif_w ", size(kfn.lif_w))
println(">>> lif_refractoryActive ", size(kfn.lif_refractoryCounter))
println(">>> lif_alpha ", size(kfn.lif_alpha)) println(">>> lif_alpha ", size(kfn.lif_alpha))
println(">>> lif_vt0 ", size(kfn.lif_vt0)) println(">>> lif_vt0 ", size(kfn.lif_vt0))
println(">>> lif_vt0 sum ", sum(kfn.lif_vt0)) println(">>> lif_vt0 sum ", sum(kfn.lif_vt0))
# check active/inactive neurons # pass input_data into input neuron.
refractoryStatus!(kfn.lif_refractoryCounter, kfn.lif_refractoryActive, kfn.lif_refractoryInactive) s1, s2, s3 = size(input)
refractoryStatus!(kfn.alif_refractoryCounter, kfn.alif_refractoryActive, kfn.alif_refractoryInactive) GeneralUtils.cartesianAssign!(kfn.zit, reshape(input, (s1, s2, 1, s3)))
#WORKING LIF forward active neurons #WORKING LIF forward active neurons
# a = kfn.lif_refractoryActive .* kfn.lif_w lifForward( kfn.zit,
# lifForward.(kfn.lif_refractoryCounter, kfn.z_i_t0, kfn.z_i_t1, kfn.lif_zit,
kfn.lif_wRec,
kfn.lif_vt0,
kfn.lif_vt1,
kfn.lif_vth,
kfn.lif_vRest,
kfn.lif_zt1,
kfn.lif_alpha,
kfn.lif_phi,
kfn.lif_epsilonRec,
kfn.lif_refractoryCounter,
kfn.lif_refractoryDuration,)
error("debug end kfn forward")
# kfn.lif_zit = GeneralUtils.matMul_3Dto4D_batchwise(kfn.zit,
# ones(size(kfn.zit)[1], size(kfn.zit)[2], size(kfn.lif_wRec)[3], size(kfn.zit)[3]))
# check active/inactive neurons
# refractoryStatus!(kfn.lif_refractoryCounter, kfn.lif_refractoryActive, kfn.lif_refractoryInactive)
# refractoryStatus!(kfn.alif_refractoryCounter, kfn.alif_refractoryActive, kfn.alif_refractoryInactive)
# a = kfn.lif_refractoryActive .* kfn.lif_wRec
# lifForward.(kfn.lif_refractoryCounter, kfn.zit0, kfn.zit1,
# kfn.lif_vt0, kfn.lif_vt1, kfn.lif_alpha, kfn.lif_recSignal) # kfn.lif_vt0, kfn.lif_vt1, kfn.lif_alpha, kfn.lif_recSignal)
# kfn.lif_recSignal .= GeneralUtils.sumAlongDim3( # kfn.lif_recSignal .= GeneralUtils.sumAlongDim3(
# GeneralUtils.matMul_3Dto4D_batchwise(kfn.z_i_t1, kfn.lif_refractoryActive .* kfn.lif_w)) # GeneralUtils.matMul_3Dto4D_batchwise(kfn.zit1, kfn.lif_refractoryActive .* kfn.lif_wRec))
# kfn.lif_vt1 = (kfn.lif_alpha .* kfn.lif_vt0) .+ kfn.lif_recSignal # kfn.lif_vt1 = (kfn.lif_alpha .* kfn.lif_vt0) .+ kfn.lif_recSignal
# GeneralUtils.batchMatEleMul(kfn.z_i_t1, kfn.alif_w, resultStorage=kfn.alif_recSignal) # GeneralUtils.batchMatEleMul(kfn.zit1, kfn.alif_wRec, resultStorage=kfn.alif_recSignal)
error("debug end kfn forward")
end end
function lifForward(lif_refractoryCounter, z_i_t0, z_i_t1, lif_w, lif_vt0, lif_vt1, lif_alpha, function lifForward(zit,
lif_recSignal) lif_zit,
error("debug end LIF forward") lif_wRec,
lif_vt0,
lif_vt1,
lif_vth,
# if n.refractoryCounter != 0 lif_vRest,
# n.refractoryCounter -= 1 lif_zt1,
lif_alpha,
lif_phi,
lif_epsilonRec,
lif_refractoryCounter,
lif_refractoryDuration,)
_, _, d3, d4 = size(lif_wRec)
lif_zit .= zit .* ones(size(lif_wRec)...) # project zit into lif_zit
# # neuron is in refractory state, skip all calculation
# n.z_t1 = false # used by timestep_forward() in kfn. Set to zero because neuron spike
# # last only 1 timestep follow by a period of refractory.
# n.recSignal = n.recSignal * 0.0
# # decay of v_t1 for j in 1:d4, i in 1:d3 # compute along neurons axis of every batch
# n.v_t1 = n.alpha * n.v_t if view(lif_refractoryCounter, :, :, i, j)[1] > 0 # refractory period is active
view(lif_refractoryCounter, :, :, i, j)[1] -= 1
view(lif_zt1, :, :, i, j)[1] = 0
view(lif_vt1, :, :, i, j)[1] = view(lif_alpha, :, :, i, j)[1] * view(lif_vt0, :, :, i, j)[1]
view(lif_phi, :, :, i, j)[1] = 0.0
view(lif_epsilonRec, :, :, i, j) .= view(lif_alpha, :, :, i, j)[1] .*
view(lif_epsilonRec, :, :, i, j)
else # refractory period is inactive
view(lif_vt1, :, :, i, j)[1] =
(view(lif_alpha, :, :, i, j)[1] * view(lif_vt0,:, :, i, j)[1]) +
sum(view(lif_zit, :, :, i, j) .* view(lif_wRec, :, :, i, j))
if view(lif_vt1, :, :, i, j)[1] > view(lif_vth, :, :, i, j)[1]
view(lif_zt1, :, :, i, j)[1] = 1
view(lif_refractoryCounter, :, :, i, j)[1] = view(lif_refractoryDuration, :, :, i, j)[1]
view(lif_firingCounter, :, :, i, j)[1] += 1
view(lif_vt1, :, :, i, j)[1] = view(lif_vRest, :, :, i, j)[1]
else
view(lif_zt1, :, :, i, j)[1] = 0
end
end
end
# n.phi = 0.0 error("debug end -> LIF forward")
# n.decayedEpsilonRec = n.alpha * n.epsilonRec
# n.epsilonRec = n.decayedEpsilonRec
# else
# n.recSignal = sum(n.wRec .* n.z_i_t) # signal from other neuron that this neuron subscribed
# # computeAlpha!(n)
# n.alpha_v_t = n.alpha * n.v_t
# n.v_t1 = n.alpha_v_t + n.recSignal
# # n.v_t1 = no_negative!(n.v_t1)
# if n.v_t1 > n.v_th
# n.z_t1 = true
# n.refractoryCounter = n.refractoryDuration
# n.firingCounter += 1
# n.v_t1 = n.vRest
# else
# n.z_t1 = false
# end
# # there is a difference from alif formula
# n.phi = (n.gammaPd / n.v_th) * max(0, 1 - (n.v_t1 - n.v_th) / n.v_th)
# n.decayedEpsilonRec = n.alpha * n.epsilonRec
# n.epsilonRec = n.decayedEpsilonRec + n.z_i_t
# end
end end

1
src/snnUtil.jl
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@@ -72,7 +72,6 @@ end
end # module end # module

56
src/type.jl
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@@ -21,40 +21,44 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
timeStep::AbstractArray = [0] timeStep::AbstractArray = [0]
learningStage::AbstractArray = [0] # 0 inference, 1 start, 2 during, 3 end learning learningStage::AbstractArray = [0] # 0 inference, 1 start, 2 during, 3 end learning
z_i_t::Union{AbstractArray, Nothing} = nothing # 3D activation matrix zit::Union{AbstractArray, Nothing} = nothing # 3D activation matrix
# ---------------------------------------------------------------------------- # # ---------------------------------------------------------------------------- #
# LIF # # LIF #
# ---------------------------------------------------------------------------- # # ---------------------------------------------------------------------------- #
# a projection of kfn.z_i_t into lif dimension for broadcasting later) # a projection of kfn.zit into lif dimension for broadcasting later)
lif_z_i_t::Union{AbstractArray, Nothing} = nothing lif_zit::Union{AbstractArray, Nothing} = nothing
lif_w::Union{AbstractArray, Nothing} = nothing lif_wRec::Union{AbstractArray, Nothing} = nothing
lif_recSignal::Union{AbstractArray, Nothing} = nothing # lif_recSignal::Union{AbstractArray, Nothing} = nothing
lif_vt0::Union{AbstractArray, Nothing} = nothing lif_vt0::Union{AbstractArray, Nothing} = nothing
lif_vt1::Union{AbstractArray, Nothing} = nothing lif_vt1::Union{AbstractArray, Nothing} = nothing
lif_vth::Union{AbstractArray, Nothing} = nothing lif_vth::Union{AbstractArray, Nothing} = nothing
lif_vRest::Union{AbstractArray, Nothing} = nothing
lif_zt0::Union{AbstractArray, Nothing} = nothing lif_zt0::Union{AbstractArray, Nothing} = nothing
lif_zt1::Union{AbstractArray, Nothing} = nothing lif_zt1::Union{AbstractArray, Nothing} = nothing
lif_refractoryCounter::Union{AbstractArray, Nothing} = nothing lif_refractoryCounter::Union{AbstractArray, Nothing} = nothing
lif_refractoryActive::Union{AbstractArray, Nothing} = nothing lif_refractoryDuration::Union{AbstractArray, Nothing} = nothing
lif_refractoryInactive::Union{AbstractArray, Nothing} = nothing # lif_refractoryActive::Union{AbstractArray, Nothing} = nothing
# lif_refractoryInactive::Union{AbstractArray, Nothing} = nothing
lif_alpha::Union{AbstractArray, Nothing} = nothing lif_alpha::Union{AbstractArray, Nothing} = nothing
lif_delta::AbstractFloat = 1.0 lif_delta::AbstractFloat = 1.0
lif_tau_m::AbstractFloat = 20.0 lif_tau_m::AbstractFloat = 20.0
lif_phi::Union{AbstractArray, Nothing} = nothing
lif_epsilonRec::Union{AbstractArray, Nothing} = nothing
lif_firingCounter::Union{AbstractArray, Nothing} = nothing
# ---------------------------------------------------------------------------- # # ---------------------------------------------------------------------------- #
# ALIF # # ALIF #
# ---------------------------------------------------------------------------- # # ---------------------------------------------------------------------------- #
alif_w::Union{AbstractArray, Nothing} = nothing alif_wRec::Union{AbstractArray, Nothing} = nothing
alif_recSignal::Union{AbstractArray, Nothing} = nothing alif_recSignal::Union{AbstractArray, Nothing} = nothing
alif_zt0::Union{AbstractArray, Nothing} = nothing alif_zt0::Union{AbstractArray, Nothing} = nothing
alif_zt1::Union{AbstractArray, Nothing} = nothing alif_zt1::Union{AbstractArray, Nothing} = nothing
alif_refractoryCounter::Union{AbstractArray, Nothing} = nothing alif_refractoryCounter::Union{AbstractArray, Nothing} = nothing
alif_refractoryActive::Union{AbstractArray, Nothing} = nothing alif_refractoryActive::Union{AbstractArray, Nothing} = nothing
alif_refractoryInactive::Union{AbstractArray, Nothing} = nothing alif_refractoryInactive::Union{AbstractArray, Nothing} = nothing
end end
# outer constructor # outer constructor
@@ -70,23 +74,27 @@ function kfn_1(params::Dict)
col += kfn.params[:computeNeuron][:alif][:numbers][2] col += kfn.params[:computeNeuron][:alif][:numbers][2]
# activation matrix # activation matrix
kfn.z_i_t = zeros(row, col, batch) kfn.zit = zeros(row, col, 1, batch)
# -------------------------------- LIF config -------------------------------- # # -------------------------------- LIF config -------------------------------- #
# In 3D LIF matrix, z-axis represent each neuron while each 2D slice represent that neuron's # In 3D LIF matrix, z-axis represent each neuron while each 2D slice represent that neuron's
# synaptic subscription to other neurons (via activation matrix) # synaptic subscription to other neurons (via activation matrix)
z = kfn.params[:computeNeuron][:lif][:numbers][1] * kfn.params[:computeNeuron][:lif][:numbers][2] z = kfn.params[:computeNeuron][:lif][:numbers][1] * kfn.params[:computeNeuron][:lif][:numbers][2]
kfn.lif_zit = zeros(row, col, z, batch)
kfn.lif_recSignal = zeros(1, 1, z, batch) # kfn.lif_recSignal = zeros(1, 1, z, batch)
kfn.lif_vt0 = zeros(1, 1, z, batch) kfn.lif_vt0 = zeros(1, 1, z, batch)
kfn.lif_vt1 = zeros(1, 1, z, batch) kfn.lif_vt1 = zeros(1, 1, z, batch)
kfn.lif_vth = ones(1, 1, z, batch) kfn.lif_vth = ones(1, 1, z, batch)
kfn.lif_vRest = zeros(1, 1, z, batch)
kfn.lif_zt0 = zeros(1, 1, z, batch) kfn.lif_zt0 = zeros(1, 1, z, batch)
kfn.lif_zt1 = zeros(1, 1, z, batch) kfn.lif_zt1 = zeros(1, 1, z, batch)
kfn.lif_refractoryCounter = zeros(1, 1, z, batch) kfn.lif_refractoryCounter = zeros(1, 1, z, batch)
kfn.lif_refractoryActive = zeros(1, 1, z, batch) kfn.lif_refractoryDuration = ones(1, 1, z, batch) .* 3
kfn.lif_refractoryInactive = zeros(1, 1, z, batch) # kfn.lif_refractoryActive = zeros(1, 1, z, batch)
# kfn.lif_refractoryInactive = zeros(1, 1, z, batch)
kfn.lif_alpha = ones(1, 1, z, batch) .* (exp(-kfn.lif_delta / kfn.lif_tau_m)) kfn.lif_alpha = ones(1, 1, z, batch) .* (exp(-kfn.lif_delta / kfn.lif_tau_m))
kfn.lif_phi = zeros(1, 1, z, batch)
kfn.lif_epsilonRec = zeros(row, col, z, batch)
# subscription # subscription
w = zeros(row, col, z) w = zeros(row, col, z)
@@ -98,14 +106,13 @@ function kfn_1(params::Dict)
slice[i] = randn()/10 # assign weight to synaptic connection slice[i] = randn()/10 # assign weight to synaptic connection
end end
end end
#WORKING project 3D w into 4D kfn.lif_w # project 3D w into 4D kfn.lif_wRec
kfn.lif_w = reshape(w, (row, col, z, 1)) .* ones(row, col, z, batch) kfn.lif_wRec = reshape(w, (row, col, z, 1)) .* ones(row, col, z, batch)
println(">>> lif_w ", size(kfn.lif_w))
error("end WORKING")
# ALIF kfn.lif_firingCounter = zeros(1, 1, z, batch)
# -------------------------------- ALIF config ------------------------------- #
z = kfn.params[:computeNeuron][:alif][:numbers][1] * kfn.params[:computeNeuron][:alif][:numbers][2] z = kfn.params[:computeNeuron][:alif][:numbers][1] * kfn.params[:computeNeuron][:alif][:numbers][2]
kfn.alif_w = zeros(row, col, z) # matrix z-axis represent each neurons
kfn.alif_recSignal = zeros(1, 1, z, batch) kfn.alif_recSignal = zeros(1, 1, z, batch)
kfn.alif_zt0 = zeros(1, 1, z, batch) kfn.alif_zt0 = zeros(1, 1, z, batch)
kfn.alif_zt1 = zeros(1, 1, z, batch) kfn.alif_zt1 = zeros(1, 1, z, batch)
@@ -114,16 +121,17 @@ function kfn_1(params::Dict)
kfn.alif_refractoryInactive = zeros(1, 1, z, batch) kfn.alif_refractoryInactive = zeros(1, 1, z, batch)
# subscription # subscription
row, col, _ = size(kfn.alif_w) # row*col is synaptic subscribe weight for each neuron in z-axis w = zeros(row, col, z)
synapticConnectionPercent = kfn.params[:computeNeuron][:alif][:params][:synapticConnectionPercent] synapticConnectionPercent = kfn.params[:computeNeuron][:alif][:params][:synapticConnectionPercent]
synapticConnection = Int(floor(row*col * synapticConnectionPercent/100)) synapticConnection = Int(floor(row*col * synapticConnectionPercent/100))
for slice in eachslice(kfn.alif_w, dims=3) for slice in eachslice(w, dims=3)
pool = shuffle!([1:row*col...])[1:synapticConnection] pool = shuffle!([1:row*col...])[1:synapticConnection]
for i in pool for i in pool
slice[i] = randn()/10 slice[i] = randn()/10
end end
end end
# project 3D w into 4D kfn.lif_wRec
kfn.alif_wRec = reshape(w, (row, col, z, 1)) .* ones(row, col, z, batch)