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bug fix

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This commit is contained in:
tonaerospace
2023-05-17 22:41:09 +07:00
parent e47ab2a855
commit 9539b1b39b
4 changed files with 27 additions and 26 deletions
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8
src/forward.jl
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@@ -129,7 +129,6 @@ function (n::lifNeuron)(kfn::knowledgeFn)
n.v_t1 = n.alpha * n.v_t n.v_t1 = n.alpha * n.v_t
else else
n.recSignal = sum(n.wRec .* n.z_i_t) # signal from other neuron that this neuron subscribed n.recSignal = sum(n.wRec .* n.z_i_t) # signal from other neuron that this neuron subscribed
n.alpha_v_t = n.alpha * n.v_t n.alpha_v_t = n.alpha * n.v_t
n.v_t1 = n.alpha_v_t + n.recSignal n.v_t1 = n.alpha_v_t + n.recSignal
n.v_t1 = no_negative!(n.v_t1) n.v_t1 = no_negative!(n.v_t1)
@@ -171,7 +170,6 @@ function (n::alifNeuron)(kfn::knowledgeFn)
n.v_t1 = n.alpha * n.v_t n.v_t1 = n.alpha * n.v_t
n.phi = 0 n.phi = 0
else else
n.z_t = isnothing(n.z_t) ? false : n.z_t
n.a = (n.rho * n.a) + ((1 - n.rho) * n.z_t) n.a = (n.rho * n.a) + ((1 - n.rho) * n.z_t)
n.av_th = n.v_th + (n.beta * n.a) n.av_th = n.v_th + (n.beta * n.a)
n.recSignal = sum(n.wRec .* n.z_i_t) # signal from other neuron that this neuron subscribed n.recSignal = sum(n.wRec .* n.z_i_t) # signal from other neuron that this neuron subscribed
@@ -205,7 +203,7 @@ function (n::linearNeuron)(kfn::T) where T<:knowledgeFn
n.timeStep = kfn.timeStep n.timeStep = kfn.timeStep
# pulling other neuron's firing status at time t # pulling other neuron's firing status at time t
n.z_i_t = getindex(kfn.firedNeurons_t0, n.subscriptionList) n.z_i_t = getindex(kfn.firedNeurons_t1, n.subscriptionList)
if n.refractoryCounter != 0 if n.refractoryCounter != 0
n.refractoryCounter -= 1 n.refractoryCounter -= 1
@@ -217,13 +215,13 @@ function (n::linearNeuron)(kfn::T) where T<:knowledgeFn
# decay of v_t1 # decay of v_t1
n.v_t1 = n.alpha * n.v_t n.v_t1 = n.alpha * n.v_t
n.vError = n.v_t1
else else
n.recSignal = sum(n.wRec .* n.z_i_t) # signal from other neuron that this neuron subscribed n.recSignal = sum(n.wRec .* n.z_i_t) # signal from other neuron that this neuron subscribed
n.alpha_v_t = n.alpha * n.v_t n.alpha_v_t = n.alpha * n.v_t
n.v_t1 = n.alpha_v_t + n.recSignal n.v_t1 = n.alpha_v_t + n.recSignal
n.v_t1 = no_negative!(n.v_t1) n.v_t1 = no_negative!(n.v_t1)
n.vError = n.v_t1
if n.v_t1 > n.v_th if n.v_t1 > n.v_th
n.z_t1 = true n.z_t1 = true
n.refractoryCounter = n.refractoryDuration n.refractoryCounter = n.refractoryDuration

26
src/learn.jl
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@@ -27,7 +27,7 @@ function learn!(kfn::kfn_1, correctAnswer::AbstractVector)
outs = [n.z_t1 for n in kfn.outputNeuronsArray] outs = [n.z_t1 for n in kfn.outputNeuronsArray]
for (i, out) in enumerate(outs) for (i, out) in enumerate(outs)
if out != correctAnswer[i] # need to adjust weight if out != correctAnswer[i] # need to adjust weight
kfnError = (kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].v_t1) * kfnError = (kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError) *
100 / kfn.outputNeuronsArray[i].v_th 100 / kfn.outputNeuronsArray[i].v_th
# Threads.@threads for n in kfn.neuronsArray # Threads.@threads for n in kfn.neuronsArray
@@ -35,7 +35,7 @@ function learn!(kfn::kfn_1, correctAnswer::AbstractVector)
learn!(n, kfnError) learn!(n, kfnError)
end end
learn!(kfn.outputNeuronsArray[i], kfn) learn!(kfn.outputNeuronsArray[i], kfnError)
end end
end end
@@ -43,18 +43,20 @@ function learn!(kfn::kfn_1, correctAnswer::AbstractVector)
if kfn.learningStage == "end_learning" if kfn.learningStage == "end_learning"
# Threads.@threads for n in kfn.neuronsArray # Threads.@threads for n in kfn.neuronsArray
for n in kfn.neuronsArray for n in kfn.neuronsArray
wSign_0 = sign.(n.wRec) # original sign if typeof(n) <: computeNeuron
n.wRec += n.wRecChange # merge wRecChange into wRec wSign_0 = sign.(n.wRec) # original sign
wSign_1 = sign.(n.wRec) # check for fliped sign, 1 indicates non-fliped sign n.wRec += n.wRecChange # merge wRecChange into wRec
nonFlipedSign = isequal.(wSign_0, wSign_1) # 1 not fliped, 0 fliped wSign_1 = sign.(n.wRec) # check for fliped sign, 1 indicates non-fliped sign
# normalize wRec peak to prevent input signal overwhelming neuron nonFlipedSign = isequal.(wSign_0, wSign_1) # 1 not fliped, 0 fliped
normalizePeak!(n.wRec, 2) # normalize wRec peak to prevent input signal overwhelming neuron
n.wRec .*= nonFlipedSign # set weight that fliped sign to 0 for random new connection normalizePeak!(n.wRec, 2)
n.wRec .*= nonFlipedSign # set weight that fliped sign to 0 for random new connection
synapticConnStrength!(n) synapticConnStrength!(n)
#TODO neuroplasticity #TODO neuroplasticity
println("") println("")
end
end end
for n in kfn.outputNeuronsArray # merge wRecChange into wRec for n in kfn.outputNeuronsArray # merge wRecChange into wRec

9
src/snn_utils.jl
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@@ -123,7 +123,7 @@ function resetLearningParams!(n::alifNeuron)
# reset refractory state at the start/end of episode. Otherwise once neuron goes into # reset refractory state at the start/end of episode. Otherwise once neuron goes into
# refractory state, it will stay in refractory state forever # refractory state, it will stay in refractory state forever
reset_refractoryCounter!(n) # reset_refractoryCounter!(n)
end end
# function reset_learning_no_wchange!(n::passthroughNeuron) # function reset_learning_no_wchange!(n::passthroughNeuron)
@@ -136,12 +136,12 @@ end
function resetLearningParams!(n::linearNeuron) function resetLearningParams!(n::linearNeuron)
reset_epsilonRec!(n) reset_epsilonRec!(n)
reset_wRecChange!(n) reset_wRecChange!(n)
reset_v_t!(n) # reset_v_t!(n)
reset_firing_counter!(n) reset_firing_counter!(n)
# reset refractory state at the start/end of episode. Otherwise once neuron goes into # reset refractory state at the start/end of episode. Otherwise once neuron goes into
# refractory state, it will stay in refractory state forever # refractory state, it will stay in refractory state forever
reset_refractoryCounter!(n) # reset_refractoryCounter!(n)
end end
#------------------------------------------------------------------------------------------------100 #------------------------------------------------------------------------------------------------100
@@ -279,6 +279,7 @@ end
function synapticConnStrength!(n::Union{computeNeuron, outputNeuron}) function synapticConnStrength!(n::Union{computeNeuron, outputNeuron})
for (i, connStrength) in enumerate(n.synapticStrength) for (i, connStrength) in enumerate(n.synapticStrength)
# check whether connStrength increase or decrease based on usage from n.epsilonRec # check whether connStrength increase or decrease based on usage from n.epsilonRec
#WORKING n.epsilonRec is all 0.0 why? may b it was reset?
updown = n.epsilonRec[i] == 0.0 ? "down" : "up" updown = n.epsilonRec[i] == 0.0 ? "down" : "up"
updatedConnStrength = synapticConnStrength(connStrength, updown) updatedConnStrength = synapticConnStrength(connStrength, updown)
updatedConnStrength = GeneralUtils.limitvalue(updatedConnStrength, updatedConnStrength = GeneralUtils.limitvalue(updatedConnStrength,
@@ -321,7 +322,7 @@ end
within its radius. radius must be odd number within its radius. radius must be odd number
""" """
function normalizePeak!(v::Vector, radius::Integer=2) function normalizePeak!(v::Vector, radius::Integer=2)
peak = findall(isequal.(v, maximum(abs.(v))))[1] peak = findall(isequal.(abs.(v), maximum(abs.(v))))[1]
upindex = peak - radius upindex = peak - radius
upindex = upindex < 1 ? 1 : upindex upindex = upindex < 1 ? 1 : upindex
downindex = peak + radius downindex = peak + radius

8
src/types.jl
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@@ -512,6 +512,7 @@ Base.@kwdef mutable struct linearNeuron <: outputNeuron
v_t1::Float64 = rand() # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep v_t1::Float64 = rand() # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep
v_th::Float64 = 1.0 # vᵗʰ, neuron firing threshold v_th::Float64 = 1.0 # vᵗʰ, neuron firing threshold
vRest::Float64 = 0.0 # resting potential after neuron fired vRest::Float64 = 0.0 # resting potential after neuron fired
vError::Union{Float64,Nothing} = nothing # used to compute model error
z_t::Bool = false # zᵗ, neuron postsynaptic firing of previous timestep z_t::Bool = false # zᵗ, neuron postsynaptic firing of previous timestep
# zᵗ⁺¹, neuron firing status at time = t+1. I need this because the way I calculate all # zᵗ⁺¹, neuron firing status at time = t+1. I need this because the way I calculate all
# neurons forward function at each timestep-by-timestep is to do every neuron # neurons forward function at each timestep-by-timestep is to do every neuron
@@ -539,7 +540,6 @@ Base.@kwdef mutable struct linearNeuron <: outputNeuron
wRecChange::Union{Array{Float64},Nothing} = nothing # Δw_rec, cumulated wRec change wRecChange::Union{Array{Float64},Nothing} = nothing # Δw_rec, cumulated wRec change
recSignal::Union{Float64,Nothing} = nothing # incoming recurrent signal recSignal::Union{Float64,Nothing} = nothing # incoming recurrent signal
alpha_v_t::Union{Float64,Nothing} = nothing # alpha * v_t alpha_v_t::Union{Float64,Nothing} = nothing # alpha * v_t
error::Union{Float64,Nothing} = nothing # local neuron error
firingCounter::Integer = 0 # store how many times neuron fires firingCounter::Integer = 0 # store how many times neuron fires
end end
@@ -627,7 +627,7 @@ function init_neuron!(id::Int64, n::lifNeuron, n_params::Dict, kfnParams::Dict)
n.synapticStrength = rand(-5:0.1:-3, length(n.subscriptionList)) n.synapticStrength = rand(-5:0.1:-3, length(n.subscriptionList))
n.epsilonRec = zeros(length(n.subscriptionList)) n.epsilonRec = zeros(length(n.subscriptionList))
n.wRec = rand(length(n.subscriptionList)) n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
n.wRecChange = zeros(length(n.subscriptionList)) n.wRecChange = zeros(length(n.subscriptionList))
n.alpha = calculate_α(n) n.alpha = calculate_α(n)
end end
@@ -646,7 +646,7 @@ function init_neuron!(id::Int64, n::alifNeuron, n_params::Dict,
n.synapticStrength = rand(-5:0.1:-3, length(n.subscriptionList)) n.synapticStrength = rand(-5:0.1:-3, length(n.subscriptionList))
n.epsilonRec = zeros(length(n.subscriptionList)) n.epsilonRec = zeros(length(n.subscriptionList))
n.wRec = rand(length(n.subscriptionList)) n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
n.wRecChange = zeros(length(n.subscriptionList)) n.wRecChange = zeros(length(n.subscriptionList))
# the more time has passed from the last time neuron was activated, the more # the more time has passed from the last time neuron was activated, the more
@@ -668,7 +668,7 @@ function init_neuron!(id::Int64, n::linearNeuron, n_params::Dict, kfnParams::Dic
n.synapticStrength = rand(-5:0.1:-3, length(n.subscriptionList)) n.synapticStrength = rand(-5:0.1:-3, length(n.subscriptionList))
n.epsilonRec = zeros(length(n.subscriptionList)) n.epsilonRec = zeros(length(n.subscriptionList))
n.wRec = rand(length(n.subscriptionList)) n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
n.wRecChange = zeros(length(n.subscriptionList)) n.wRecChange = zeros(length(n.subscriptionList))
n.alpha = calculate_k(n) n.alpha = calculate_k(n)
end end