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experimenting compute neuron association

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tonaerospace
2023-05-25 21:23:43 +07:00
parent d12ba02efd
commit 3556167591
5 changed files with 60 additions and 37 deletions
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1
src/Ironpen.jl
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@@ -34,6 +34,7 @@ using .interface
"""
Todo:
[*1] add maximum weight cap of each connection
[2] implement connection strength based on right or wrong answer
[4] implement dormant connection
[3] Δweight * connection strength

20
src/forward.jl
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@@ -14,11 +14,11 @@ function (m::model)(input_data::AbstractVector)
m.timeStep += 1
# process all corresponding KFN
raw_model_respond, outputNeuron_v_t1 = m.knowledgeFn[:I](m, input_data)
# raw_model_respond, outputNeuron_v_t1, firedNeurons_t1 = m.knowledgeFn[:I](m, input_data)
# the 2nd return (KFN error) should not be used as model error but I use it because there is
# only one KFN in a model right now
return raw_model_respond::Array{Bool}, outputNeuron_v_t1::Array{Float64}
return m.knowledgeFn[:I](m, input_data)
end
#------------------------------------------------------------------------------------------------100
@@ -96,7 +96,8 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
out = [n.z_t1 for n in kfn.outputNeuronsArray]
outputNeuron_v_t1 = [n.v_t1 for n in kfn.outputNeuronsArray]
return out::Array{Bool}, outputNeuron_v_t1::Array{Float64}
return out::Array{Bool}, outputNeuron_v_t1::Array{Float64}, sum(kfn.firedNeurons_t1),
kfn.exSignalSum, kfn.inSignalsum
end
#------------------------------------------------------------------------------------------------100
@@ -220,7 +221,18 @@ function (n::linearNeuron)(kfn::T) where T<:knowledgeFn
n.v_t1 = n.alpha * n.v_t
n.vError = n.v_t1 # store voltage that will be used to calculate error later
else
n.recSignal = sum(n.wRec .* n.z_i_t) # signal from other neuron that this neuron subscribed
recSignal = n.wRec .* n.z_i_t
if n.id == 1 #FIXME debugging output neuron dead
for i in recSignal
if i > 0
kfn.exSignalSum += i
elseif i < 0
kfn.inSignalsum += i
else
end
end
end
n.recSignal = sum(recSignal) # signal from other neuron that this neuron subscribed
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)

52
src/learn.jl
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@@ -23,41 +23,41 @@ end
""" knowledgeFn learn()
"""
function learn!(kfn::kfn_1, correctAnswer::BitVector)
# compute kfn error for each neuron
outs = [n.z_t1 for n in kfn.outputNeuronsArray]
for (i, out) in enumerate(outs)
if out != correctAnswer[i] # need to adjust weight
kfnError = ( (kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError) *
100 / kfn.outputNeuronsArray[i].v_th )
Threads.@threads for n in kfn.neuronsArray
# for n in kfn.neuronsArray
learn!(n, kfnError)
end
learn!(kfn.outputNeuronsArray[i], kfnError)
end
end
# #TESTING compute kfn error for each neuron
# outs = [n.z_t1 for n in kfn.outputNeuronsArray]
# for (i, out) in enumerate(outs)
# if out != correctAnswer[i] # need to adjust weight
# kfnError = ( (kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError) *
# 100 / kfn.outputNeuronsArray[i].v_th )
# if correctAnswer[i] == 1 # output neuron that associated with correctAnswer
# Threads.@threads for n in kfn.neuronsArray
# # for n in kfn.neuronsArray
# learn!(n, kfnError)
# end
# learn!(kfn.outputNeuronsArray[i], kfnError)
# else # output neuron that is NOT associated with correctAnswer
# learn!(kfn.outputNeuronsArray[i], kfnError)
# Threads.@threads for n in kfn.neuronsArray
# # for n in kfn.neuronsArray
# learn!(n, kfnError)
# end
# learn!(kfn.outputNeuronsArray[i], kfnError)
# end
# end
#TESTING compute kfn error for each neuron
outs = [n.z_t1 for n in kfn.outputNeuronsArray]
for (i, out) in enumerate(outs)
if out != correctAnswer[i] # need to adjust weight
kfnError = ( (kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError) /
kfn.outputNeuronsArray[i].v_th )
if correctAnswer[i] == 1 # output neuron that associated with correctAnswer
Threads.@threads for n in kfn.neuronsArray
# for n in kfn.neuronsArray
learn!(n, kfnError)
end
learn!(kfn.outputNeuronsArray[i], kfnError)
else # output neuron that is NOT associated with correctAnswer
learn!(kfn.outputNeuronsArray[i], kfnError)
end
end
end
# wrap up learning session
if kfn.learningStage == "end_learning"
Threads.@threads for n in kfn.neuronsArray
@@ -69,8 +69,9 @@ function learn!(kfn::kfn_1, correctAnswer::BitVector)
nonFlipedSign = isequal.(wSign_0, wSign_1) # 1 not fliped, 0 fliped
# normalize wRec peak to prevent input signal overwhelming neuron
normalizePeak!(n.wRec, n.wRecChange, 2)
# set weight that fliped sign to 0 for random new connection
# set weight that fliped sign to 0 for random new connection
n.wRec .*= nonFlipedSign
capMaxWeight!(n.wRec) # cap maximum weight
synapticConnStrength!(n)
neuroplasticity!(n, kfn.firedNeurons, kfn.nExInType)
@@ -84,6 +85,7 @@ function learn!(kfn::kfn_1, correctAnswer::BitVector)
nonFlipedSign = isequal.(wSign_0, wSign_1) # 1 not fliped, 0 fliped
normalizePeak!(n.wRec, n.wRecChange, 2)
n.wRec .*= nonFlipedSign # set weight that fliped sign to 0 for random new connection
capMaxWeight!(n.wRec) # cap maximum weight
synapticConnStrength!(n)
neuroplasticity!(n, kfn.firedNeurons, kfn.nExInType, kfn.kfnParams[:totalInputPort])

15
src/snn_utils.jl
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@@ -7,7 +7,7 @@ export calculate_α, calculate_ρ, calculate_k, timestep_forward!, init_neuron,
reset_epsilonRecA!, synapticConnStrength!, normalizePeak!,
firing_rate_error!, firing_rate_regulator!, update_Bn!, cal_firing_reg!,
neuroplasticity!, shakeup!, reset_learning_no_wchange!, adjust_internal_learning_rate!,
gradient_withloss
gradient_withloss, capMaxWeight!
using Statistics, Random, LinearAlgebra, Distributions, Zygote, Flux
using GeneralUtils
@@ -283,12 +283,12 @@ end
function synapticConnStrength!(n::Union{computeNeuron, outputNeuron})
for (i, connStrength) in enumerate(n.synapticStrength)
# check whether connStrength increase or decrease based on usage from n.epsilonRec
""" use n.wRecChange instead of the best choise, epsilonRec, here because ΔwRecChange
""" use n.z_i_t_commulative instead of the best choice, epsilonRec, here because ΔwRecChange
calculation in learn!() will reset epsilonRec to zeroes vector in case where
output neuron fires and trigger learn!() just before this synapticConnStrength
calculation.
Since n.wRecChange indicates whether a synaptic connection were used or not, it is
ok to use. n.wRecChange also span across a training sample without resetting.
Since n.z_i_t_commulative indicates whether a synaptic connection were used or not, it is
ok to use. n.z_i_t_commulative also span across a training sample without resetting.
"""
updown = n.z_i_t_commulative[i] == 0 ? "down" : "up" #
updatedConnStrength = synapticConnStrength(connStrength, updown)
@@ -441,7 +441,12 @@ function neuroplasticity!(n::outputNeuron, firedNeurons::Vector,
end
end
""" Cap maximum weight of each neuron connection
"""
function capMaxWeight!(v::Vector{Float64}, max=1.0)
originalSign = sign.(v)
v = originalSign .* GeneralUtils.replaceMoreThan.(abs.(v), max)
end

9
src/types.jl
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@@ -115,6 +115,9 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
nInhabitory::Array{Int64} = Int64[] # list of inhabitory neuron id
nExInType::Array{Int64} = Int64[] # list all neuron EX or IN
excitatoryPercent::Int64 = 60 # percentage of excitatory neuron, inhabitory percent will be 100-ExcitatoryPercent
exSignalSum = 0
inSignalsum = 0
end
#------------------------------------------------------------------------------------------------100
@@ -347,7 +350,7 @@ Base.@kwdef mutable struct lifNeuron <: computeNeuron
refractoryDuration::Int64 = 3 # neuron's refratory period in millisecond
refractoryCounter::Int64 = 0
tau_m::Float64 = 0.0 # τ_m, membrane time constant in millisecond
eta::Float64 = 0.01 # η, learning rate
eta::Float64 = 0.0001 # η, learning rate
wRecChange::Array{Float64} = Float64[] # Δw_rec, cumulated wRec change
recSignal::Float64 = 0.0 # incoming recurrent signal
alpha_v_t::Float64 = 0.0 # alpha * v_t
@@ -435,7 +438,7 @@ Base.@kwdef mutable struct alifNeuron <: computeNeuron
eRec_v::Array{Float64} = Float64[] # a component of neuron's eligibility trace resulted from v_t
eRec_a::Array{Float64} = Float64[] # a component of neuron's eligibility trace resulted from av_th
eRec::Array{Float64} = Float64[] # neuron's eligibility trace
eta::Float64 = 0.01 # eta, learning rate
eta::Float64 = 0.0001 # eta, learning rate
gammaPd::Float64 = 0.3 # γ_pd, discount factor, value from paper
phi::Float64 = 0.0 # ϕ, psuedo derivative
refractoryDuration::Int64 = 3 # neuron's refractory period in millisecond
@@ -545,7 +548,7 @@ Base.@kwdef mutable struct linearNeuron <: outputNeuron
refractoryDuration::Int64 = 3 # neuron's refratory period in millisecond
refractoryCounter::Int64 = 0
tau_out::Float64 = 0.0 # τ_out, membrane time constant in millisecond
eta::Float64 = 0.01 # η, learning rate
eta::Float64 = 0.0001 # η, learning rate
wRecChange::Array{Float64} = Float64[] # Δw_rec, cumulated wRec change
recSignal::Float64 = 0.0 # incoming recurrent signal
alpha_v_t::Float64 = 0.0 # alpha * v_t