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ton
2023-08-26 07:11:27 +07:00
parent c74eea9cdf
commit 9c988583aa
4 changed files with 199 additions and 75 deletions
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8
src/forward.jl
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@@ -18,7 +18,7 @@ function (kfn::kfn_1)(input::AbstractArray)
# what to do at the start of learning round
if view(kfn.learningStage, 1)[1] == 1
# reset learning params
kfn.zit_cumulative .= 0
kfn.zitCumulative .= 0
kfn.lif_vt .= 0
kfn.lif_wRecChange .= 0
@@ -118,7 +118,7 @@ function (kfn::kfn_1)(input::AbstractArray)
reshape(kfn.lif_zt, (size(input, 1), :, 1, size(input, 3))),
reshape(kfn.alif_zt, (size(input, 1), :, 1, size(input, 3))), dims=2)
kfn.zit .= reshape(_zit, (size(input, 1), :, size(input, 3)))
kfn.zit_cumulative .+= kfn.zit
kfn.zitCumulative .+= kfn.zit
# project 3D kfn zit into 4D on zit
i1, i2, i3, i4 = size(kfn.on_zit)
@@ -291,7 +291,7 @@ function lifForward( zit,
# count synaptic inactivity
if !iszero(wRec[i1,i2,i3,i4]) # check if this is wRec subscription
if !iszero(zit[i1,i2,i3,i4]) # synapse is active, reset counter
synapticInactivityCounter[i1,i2,i3,i4] = 10000
synapticInactivityCounter[i1,i2,i3,i4] += 1
else # synapse is inactive, counting
synapticInactivityCounter[i1,i2,i3,i4] -= 1
end
@@ -478,7 +478,7 @@ function alifForward( zit,
# count synaptic inactivity
if !iszero(wRec[i1,i2,i3,i4]) # check if this is wRec subscription
if !iszero(zit[i1,i2,i3,i4]) # synapse is active, reset counter
synapticInactivityCounter[i1,i2,i3,i4] = 10000
synapticInactivityCounter[i1,i2,i3,i4] += 1
else # synapse is inactive, counting
synapticInactivityCounter[i1,i2,i3,i4] -= 1
end

115
src/learn.jl
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@@ -270,8 +270,9 @@ function learn!(kfn::kfn_1, device=cpu)
kfn.lif_wRecChange,
kfn.lif_arrayProjection4d,
kfn.lif_neuronInactivityCounter,
kfn.lif_synapticInactivityCounter,
kfn.lif_synapticConnectionNumber,
kfn.zit_cumulative,
kfn.zitCumulative,
device)
# alif learn
@@ -279,8 +280,9 @@ function learn!(kfn::kfn_1, device=cpu)
kfn.alif_wRecChange,
kfn.alif_arrayProjection4d,
kfn.alif_neuronInactivityCounter,
kfn.lif_synapticInactivityCounter,
kfn.alif_synapticConnectionNumber,
kfn.zit_cumulative,
kfn.zitCumulative,
device)
# on learn
@@ -298,31 +300,38 @@ end
function lifLearn!(wRec,
wRecChange,
arrayProjection4d,
inactivityCounter,
neuronInactivityCounter,
synapticInactivityCounter,
synapticConnectionNumber,
zit_cumulative,
zitCumulative,
device)
# merge learning weight with average learning weight of all batch
wRec .+= (sum(wRecChange, dims=4) ./ (size(wRec, 4))) .* arrayProjection4d
wRec_cpu = wRec |> cpu
wRec_cpu = wRec_cpu[:,:,:,1] # since every batch has the same neuron wRec, (row, col, n)
inactivityCounter_cpu = inactivityCounter |> cpu
inactivityCounter_cpu = inactivityCounter_cpu[:,:,:,1] # (row, col, n)
zit_cumulative_cpu = zit_cumulative |> cpu
zit_cumulative_cpu = zit_cumulative_cpu[:,:,1] # (row, col)
neuronInactivityCounter_cpu = neuronInactivityCounter |> cpu
neuronInactivityCounter_cpu = neuronInactivityCounter_cpu[:,:,:,1] # (row, col, n)
synapticInactivityCounter_cpu = synapticInactivityCounter |> cpu
synapticInactivityCounter_cpu = synapticInactivityCounter_cpu[:,:,:,1]
zitCumulative_cpu = zitCumulative |> cpu
zitCumulative_cpu = zitCumulative_cpu[:,:,1] # (row, col)
# weak / negative synaptic connection will get randomed in neuroplasticity()
wRec_cpu = GeneralUtils.replaceBetween.(wRec_cpu, 0.0, 0.1, -1.0) # mark with -1.0
# synaptic connection that has no inactivity will get randomed in neuroplasticity()
GeneralUtils.replace_elements!(inactivityCounter_cpu, 0.0, wRec_cpu, -1.0)
# reset lif_inactivity elements to 10000
GeneralUtils.replace_elements!(inactivityCounter_cpu, 0.0, -9.0) # -9.0 is base value
GeneralUtils.replace_elements!(neuronInactivityCounter_cpu, 0.0, wRec_cpu, -1.0)
# reset lif_inactivity elements to -9
GeneralUtils.replace_elements!(neuronInactivityCounter_cpu, 0.0, -9.0) # -9.0 is base value
#WORKING neuroplasticity
wRec_cpu = neuroplasticity(synapticConnectionNumber, zit_cumulative_cpu, wRec_cpu,
inactivityCounter_cpu)
wRec_cpu = neuroplasticity(synapticConnectionNumber,
zitCumulative_cpu,
wRec_cpu,
neuronInactivityCounter_cpu,
synapticInactivityCounter_cpu)
error("DEBUG -> lifLearn! $(Dates.now())")
# #TODO send to device with correct dimension
# wRec = wRec |> device
@@ -333,9 +342,10 @@ end
function alifLearn!(wRec,
wRecChange,
arrayProjection4d,
inactivityCounter,
neuronInactivityCounter,
synapticInactivityCounter,
synapticConnectionNumber,
zit_cumulative,
zitCumulative,
device)
# merge learning weight with average learning weight
wRec .+= (sum(wRecChange, dims=4) ./ (size(wRec, 4))) .* arrayProjection4d
@@ -365,9 +375,10 @@ function onLearn!(wOut,
end
function neuroplasticity(synapticConnectionNumber,
zit_cumulative, # (row, col)
zitCumulative, # (row, col)
wRec, # (row, col, n)
inactivityCounter_cpu) # (row, col, n)
neuronInactivityCounter, #WORKING neuron die i.e. reset all weight
synapticInactivityCounter) # (row, col, n)
i1,i2,i3 = size(wRec)
@@ -376,42 +387,52 @@ function neuroplasticity(synapticConnectionNumber,
subToFireNeuron_toBe = Int(floor(0.7 * synapticConnectionNumber))
subToNonFiringNeuron_toBe = synapticConnectionNumber - subToFireNeuron_toBe
#WORKING for each neuron, count how many synap already subscribed to firing-neurons
subToFireNeuron_current = sum((!iszero).(zit_cumulative .* wRec), dims=(1,2)) # (1, 1, n)
subToNonFiringNeuron_current = synapticConnectionNumber .- subToFireNeuron_current # (1, 1, n)
mask = (!iszero).(zit_cumulative) # mask of firing neurons = 1, non-firing = 0
# 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)
mask = mask .* projection # (row, col, n)
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("mask ", size(mask))
println("wRec ", size(wRec))
println("inactivityCounter_cpu ", size(inactivityCounter_cpu))
println("totalNeurons ", totalNewConn, size(totalNewConn))
error("DEBUG -> neuroplasticity $(Dates.now())")
#WORKING clear -1.0 marker
GeneralUtils.replace_elements!(wRec, -1.0, synapticInactivityCounter, -9.0)
GeneralUtils.replace_elements!(wRec, -1.0, 0.0) # -1.0 marker is no longer required
println("/////////")
println("wRec 1 ", wRec[:,:,1])
println("synapticInactivityCounter 1 ", synapticInactivityCounter[:,:,1])
for i in 1:i3
# add new conn to firing neurons pool
remaining = GeneralUtils.replace_elements(mask[:,:,i],
1,
wRecmask[:,:,i],
inactivityCounter_cpumask[:,:,i],
totalNewConn[:,:,i])
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(synapticInactivityCounter[:,:,i]),
toAddConn)
totalNewConn[1,1,i] += remaining
end
#TODO add new conn to non-firing neurons pool
# add new conn to non-firing neurons pool
remaining = addNewSynapticConn!(zitMask[:,:,i], 0,
@view(wRec[:,:,i]),
@view(synapticInactivityCounter[:,:,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(synapticInactivityCounter[:,:,i]),
remaining)
end
end
println("==========")
println("wRec 2 ", wRec[:,:,1])
println("synapticInactivityCounter 2 ", synapticInactivityCounter[:,:,1])
newFiringConn = subToFireNeuron_toBe - subToFireNeuron_current
newFiringConn = newFiringConn > 0 ? newFiringConn : 0
newNonFiringConn = subToNonFiringNeuron_toBe - subToNonFiringNeuron_current
# error("DEBUG -> neuroplasticity $(Dates.now())")
return wRec
end

136
src/snnUtil.jl
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@@ -1,8 +1,8 @@
module snnUtil
export refractoryStatus!
export refractoryStatus!, addNewSynapticConn!
# using
using Random
#------------------------------------------------------------------------------------------------100
@@ -21,28 +21,132 @@ function refractoryStatus!(refractoryCounter, refractoryActive, refractoryInacti
end
end
function frobenius_distance(A, B)
# Check if the matrices have the same size
if size(A) != size(B)
error("The matrices must have the same size")
# function frobenius_distance(A, B)
# # Check if the matrices have the same size
# if size(A) != size(B)
# error("The matrices must have the same size")
# end
# # Initialize the distance to zero
# distance = 0.0
# # Loop over the elements of the matrices and add the squared differences
# for i in 1:size(A, 1)
# for j in 1:size(A, 2)
# distance += (A[i, j] - B[i, j])^2
# end
# end
# # Return the square root of the distance
# return sqrt(distance)
# end
function addNewSynapticConn!(mask::AbstractArray{<:Any}, x::Number, wRec::AbstractArray{<:Any},
counter::AbstractArray{<:Any}, n=0;
rng::AbstractRNG=MersenneTwister(1234))
# println("mask ", mask, size(mask))
# println("")
# println("x ", x, size(x))
# println("")
# println("wRec ", wRec, size(wRec))
# println("")
# println("counter ", counter, size(counter))
# println("")
# println("n ", n, size(n))
# println("")
total_x_tobeReplced = sum(isequal.(mask, x))
remaining = 0
if n == 0 || n > total_x_tobeReplced
remaining = n - total_x_tobeReplced
n = total_x_tobeReplced
end
# Initialize the distance to zero
distance = 0.0
# Loop over the elements of the matrices and add the squared differences
for i in 1:size(A, 1)
for j in 1:size(A, 2)
distance += (A[i, j] - B[i, j])^2
# check if mask and wRec have the same size
if size(mask) != size(wRec)
error("mask and wRec must have the same size")
end
# get the indices of elements in mask that equal x
indices = findall(x -> x == x, mask)
alreadySub = findall(x -> x != 0, wRec) # get already subscribe
setdiff!(indices, alreadySub) # remove already sub conn from pool
# shuffle the indices using the rng function
shuffle!(rng, indices)
# select the first n indices
selected = indices[1:n]
# replace the elements in wRec at the selected positions with a
for i in selected
wRec[i] = 0.1 #rand(0.1:0.01:0.3)
if counter !== nothing
counter[i] = 0 # reset
end
end
# Return the square root of the distance
return sqrt(distance)
# println("==================")
# println("mask ", mask, size(mask))
# println("")
# println("x ", x, size(x))
# println("")
# println("wRec ", wRec, size(wRec))
# println("")
# println("counter ", counter, size(counter))
# println("")
# println("n ", n, size(n))
# println("")
# error("DEBUG addNewSynapticConn!")
return remaining
end
# function addNewSynapticConn!(mask::AbstractArray{<:Any}, x::Number, A::AbstractArray{<:Any},
# A2::AbstractArray{<:Any}, n=0;
# rng::AbstractRNG=MersenneTwister(1234))
# # println("mask ", mask, size(mask))
# # println("")
# # println("x ", x, size(x))
# # println("")
# # println("A ", A, size(A))
# # println("")
# # println("A2 ", A2, size(A2))
# # println("")
# # println("n ", n, size(n))
# # println("")
# total_x_tobeReplced = sum(isequal.(mask, x))
# remaining = 0
# if n == 0 || n > total_x_tobeReplced
# remaining = n - total_x_tobeReplced
# n = total_x_tobeReplced
# end
# # check if mask and A have the same size
# if size(mask) != size(A)
# error("mask and A must have the same size")
# end
# # get the indices of elements in mask that equal x
# indices = findall(x -> x == x, mask)
# # shuffle the indices using the rng function
# shuffle!(rng, indices)
# # select the first n indices
# selected = indices[1:n]
# # replace the elements in A at the selected positions with a
# for i in selected
# A[i] = rand(0.1:0.01:0.3)
# if A2 !== nothing
# A2[i] = 10000
# end
# end
# # println("==================")
# # println("mask ", mask, size(mask))
# # println("")
# # println("x ", x, size(x))
# # println("")
# # println("A ", A, size(A))
# # println("")
# # println("A2 ", A2, size(A2))
# # println("")
# # println("n ", n, size(n))
# # println("")
# # error("DEBUG addNewSynapticConn!")
# return remaining
# end

15
src/type.jl
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@@ -23,7 +23,7 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
learningStage::Union{AbstractArray, Nothing} = nothing # 0 inference, 1 start, 2 during, 3 end learning
inputSize::Union{AbstractArray, Nothing} = nothing
zit::Union{AbstractArray, Nothing} = nothing # 3D activation matrix
zit_cumulative::Union{AbstractArray, Nothing} = nothing
zitCumulative::Union{AbstractArray, Nothing} = nothing
exInType::Union{AbstractArray, Nothing} = nothing
modelError::Union{AbstractArray, Nothing} = nothing # store RSNN error
outputError::Union{AbstractArray, Nothing} = nothing # store output neurons error
@@ -185,7 +185,7 @@ function kfn_1(params::Dict; device=cpu)
# activation matrix
kfn.zit = zeros(row, col, batch) |> device
kfn.zit_cumulative = (similar(kfn.zit) .= 0)
kfn.zitCumulative = (similar(kfn.zit) .= 0)
kfn.modelError = zeros(1) |> device
# ---------------------------------------------------------------------------- #
@@ -237,7 +237,7 @@ function kfn_1(params::Dict; device=cpu)
kfn.lif_neuronInactivityCounter = (similar(kfn.lif_wRec) .= 10000)
kfn.lif_synapticInactivityCounter = Array(similar(kfn.lif_wRec) .= -9) # -9 for non-sub conn
mask = Array((!iszero).(kfn.lif_wRec))
GeneralUtils.replace_elements!(mask, 1, kfn.lif_synapticInactivityCounter, 10000)
GeneralUtils.replace_elements!(mask, 1, kfn.lif_synapticInactivityCounter, 0) # initial value subscribed conn
kfn.lif_synapticInactivityCounter = kfn.lif_synapticInactivityCounter |> device
kfn.lif_arrayProjection4d = (similar(kfn.lif_wRec) .= 1)
@@ -296,7 +296,7 @@ function kfn_1(params::Dict; device=cpu)
kfn.alif_neuronInactivityCounter = (similar(kfn.alif_wRec) .= 10000)
kfn.alif_synapticInactivityCounter = Array(similar(kfn.alif_wRec) .= -9) # -9 for non-sub conn
mask = Array((!iszero).(kfn.alif_wRec))
GeneralUtils.replace_elements!(mask, 1, kfn.alif_synapticInactivityCounter, 10000)
GeneralUtils.replace_elements!(mask, 1, kfn.alif_synapticInactivityCounter, 0) # initial value subscribed conn
kfn.alif_synapticInactivityCounter = kfn.alif_synapticInactivityCounter |> device
kfn.alif_arrayProjection4d = (similar(kfn.alif_wRec) .= 1)
@@ -333,7 +333,6 @@ function kfn_1(params::Dict; device=cpu)
synapticConnection = Int(floor(subable * synapticConnectionPercent/100))
for slice in eachslice(w, dims=3) # each slice is a neuron
startInd = row*col - subable + 1 # e.g. 100(row*col) - 50(subable) = 50 -> startInd = 51
# pool must contain only lif, alif neurons
pool = shuffle!([startInd:row*col...])[1:synapticConnection]
for i in pool
@@ -342,9 +341,9 @@ function kfn_1(params::Dict; device=cpu)
end
end
# # 10% of neuron connection should be enough to start to make neuron fires
# should_be_avg_weight = 1 / (0.2 * n)
# w = w .* (should_be_avg_weight / maximum(w)) # adjust overall weight
# 10% of neuron connection should be enough to start to make neuron fires
should_be_avg_weight = 1 / (0.1 * n)
w = w .* (should_be_avg_weight / maximum(w)) # adjust overall weight
# project 3D w into 4D kfn.lif_wOut (row, col, n, batch)
kfn.on_wOut = reshape(w, (row, col, n, 1)) .* ones(row, col, n, batch) |> device