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refractoring

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This commit is contained in:
tonaerospace
2023-05-17 14:28:43 +07:00
parent df26a01929
commit 214466d9e9
5 changed files with 36 additions and 53 deletions
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2
src/Ironpen.jl
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@@ -35,7 +35,7 @@ using .interface
"""
Todo:
[3] verify that model can complete learning cycle with no error
[2] neuroplasticity() i.e. change connection
[*2] neuroplasticity() i.e. change connection
[] using RL to control learning signal
[] consider using Dates.now() instead of timestamp because time_stamp may overflow
[] training should include adjusting α, neuron membrane potential decay factor

35
src/forward.jl
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@@ -31,6 +31,29 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
kfn.learningStage = m.learningStage
if kfn.learningStage == "start_learning"
# reset params here instead of at the end_learning so that neuron's parameter data
# don't gets wiped and can be logged for visualization later
for n in kfn.neuronsArray
# epsilonRec need to be reset because it counting how many each synaptic fires and
# use this info to calculate how much synaptic weight should be adjust
resetLearningParams!(n)
end
for n in kfn.output_neuron
# epsilonRec need to be reset because it counting how many each synaptic fires and
# use this info to calculate how much synaptic weight should be adjust
resetLearningParams!(n)
end
# clear variables
kfn.firedNeurons = Vector{Int64}()
kfn.firedNeurons_t0 = Vector{Bool}()
kfn.firedNeurons_t1 = Vector{Bool}()
kfn.learningStage = "learning"
end
# generate noise
noise = [GeneralUtils.randomChoiceWithProb([true, false],[0.5,0.5])
for i in 1:length(input_data)]
@@ -41,6 +64,9 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
for n in kfn.neuronsArray
timestep_forward!(n)
end
for n in kfn.outputNeuronsArray
timestep_forward!(n)
end
# pass input_data into input neuron.
# number of data point equals to number of input neuron starting from id 1
@@ -89,7 +115,6 @@ function (n::lif_neuron)(kfn::knowledgeFn)
# pulling other neuron's firing status at time t
n.z_i_t = getindex(kfn.firedNeurons_t0, n.subscriptionList)
n.z_i_t .*= n.subExInType
if n.refractoryCounter != 0
n.refractoryCounter -= 1
@@ -102,7 +127,7 @@ function (n::lif_neuron)(kfn::knowledgeFn)
# decay of v_t1
n.v_t1 = n.alpha * n.v_t
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 .* n.subExInType) # 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
@@ -132,7 +157,6 @@ function (n::alif_neuron)(kfn::knowledgeFn)
n.timeStep = kfn.timeStep
n.z_i_t = getindex(kfn.firedNeurons_t0, n.subscriptionList)
n.z_i_t .*= n.subExInType
if n.refractoryCounter != 0
n.refractoryCounter -= 1
@@ -149,7 +173,7 @@ function (n::alif_neuron)(kfn::knowledgeFn)
n.z_t = isnothing(n.z_t) ? false : 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.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 .* n.subExInType) # 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)
@@ -181,7 +205,6 @@ function (n::linear_neuron)(kfn::T) where T<:knowledgeFn
# pulling other neuron's firing status at time t
n.z_i_t = getindex(kfn.firedNeurons_t0, n.subscriptionList)
n.z_i_t .*= n.subExInType
if n.refractoryCounter != 0
n.refractoryCounter -= 1
@@ -194,7 +217,7 @@ function (n::linear_neuron)(kfn::T) where T<:knowledgeFn
# decay of v_t1
n.v_t1 = n.alpha * n.v_t
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 .* n.subExInType) # 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

37
src/learn.jl
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@@ -12,10 +12,6 @@ export learn!
function learn!(m::model, modelRespond, correctAnswer=nothing)
m.knowledgeFn[:I].learningStage = m.learningStage
# # how many matched respond and correct answer
# matched = sum(isequal(modelRespond, correctAnswer))
if correctAnswer === nothing
correctAnswer_I = zeros(length(modelRespond))
else
@@ -28,39 +24,6 @@ end
""" knowledgeFn learn()
"""
function learn!(kfn::kfn_1, correctAnswer::AbstractVector)
# ΔWeight Conn. Strength
# case 1 no no during input signal, no correct answer available, no answer
# case 2 no - during input signal, no correct answer available, wrong answer
# case 3 + - during input signal, correct answer available, no answer
# case 4 no - during input signal, correct answer available, wrong answer
# case 5 no ++ during input signal, correct answer
# case 6 no ++ after input signal, at correct timing, correct answer
# case 6 + - after input signal, at correct timing, no answer
# case 9 no -- after input signal, at correct timing, wrong answer
# case 7 adjust + after input signal, after correct timing (late), correct answer
# case 8 after input signal, after correct timing (late), no answer
# case 8 no - after input signal, after correct timing (late), wrong answer
# success
if kfn.learningStage == "start_learning"
# reset params here instead of at the end_learning so that neuron's parameter data
# don't gets wiped and can be logged for visualization later
for n in kfn.neuronsArray
# epsilonRec need to be reset because it counting how many each synaptic fires and
# use this info to calculate how much synaptic weight should be adjust
resetLearningParams!(n)
end
# clear variables
kfn.firedNeurons = Vector{Int64}()
kfn.firedNeurons_t0 = Vector{Bool}()
kfn.firedNeurons_t1 = Vector{Bool}()
kfn.learningStage = "learning"
end
# compute kfn error
outs = [n.z_t1 for n in kfn.outputNeuronsArray]
for (i, out) in enumerate(outs)

2
src/snn_utils.jl
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@@ -19,7 +19,7 @@ function timestep_forward!(x::passthrough_neuron)
x.z_t = x.z_t1
end
function timestep_forward!(x::compute_neuron)
function timestep_forward!(x::Union{compute_neuron, output_neuron})
x.z_t = x.z_t1
x.v_t = x.v_t1
end

13
src/types.jl
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@@ -310,9 +310,8 @@ Base.@kwdef mutable struct lif_neuron <: compute_neuron
subExInType::Array{Int64} = Vector{Int64}() # store ExIn type of subscribed neurons
timeStep::Number = 0.0 # current time
wRec::Union{Array{Float64},Nothing} = nothing # synaptic weight (for receiving signal from other neuron)
v_t::Float64 = rand() # vᵗ, postsynaptic neuron membrane potential of previous timestep
v_t::Float64 = 0.0 # vᵗ, postsynaptic neuron membrane potential of previous timestep
v_t1::Float64 = rand() # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep
v_t_default::Union{Float64,Nothing} = 0.0 # default membrane potential voltage
v_th::Float64 = 1.0 # vᵗʰ, neuron firing threshold
vRest::Float64 = 0.0 # resting potential after neuron fired
z_t::Bool = false # zᵗ, neuron postsynaptic firing of previous timestep
@@ -403,9 +402,8 @@ Base.@kwdef mutable struct alif_neuron <: compute_neuron
subExInType::Array{Int64} = Vector{Int64}() # store ExIn type of subscribed neurons
timeStep::Union{Number,Nothing} = nothing # current time
wRec::Union{Array{Float64},Nothing} = nothing # synaptic weight (for receiving signal from other neuron)
v_t::Float64 = rand() # vᵗ, postsynaptic neuron membrane potential of previous timestep
v_t::Float64 = 0.0 # vᵗ, postsynaptic neuron membrane potential of previous timestep
v_t1::Float64 = rand() # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep
v_t_default::Union{Float64,Nothing} = 0.0
v_th::Float64 = 1.0 # vᵗʰ, neuron firing threshold
vRest::Float64 = 0.0 # resting potential after neuron fired
z_t::Bool = false # zᵗ, neuron postsynaptic firing of previous timestep
@@ -508,14 +506,13 @@ Base.@kwdef mutable struct linear_neuron <: output_neuron
knowledgeFnName::Union{String,Nothing} = nothing # knowledgeFn that this neuron belongs to
subscriptionList::Union{Array{Int64},Nothing} = nothing # list of other neuron that this neuron synapse subscribed to
timeStep::Union{Number,Nothing} = nothing # current time
subExInType::Array{Int64} = Vector{Int64}() # store ExIn type of subscribed neurons
subExInType::Array{Int64} = Vector{Int64}() # store ExIn type of subscribed neurons
wRec::Union{Array{Float64},Nothing} = nothing # synaptic weight (for receiving signal from other neuron)
v_t::Float64 = 0.0 # vᵗ, postsynaptic neuron membrane potential of previous timestep
v_t1::Float64 = rand() # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep
v_t_default::Union{Float64,Nothing} = 0.0 # default membrane potential voltage
v_t1::Float64 = rand() # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep
v_th::Float64 = 1.0 # vᵗʰ, neuron firing threshold
vRest::Float64 = 0.0 # resting potential after neuron fired
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
# neurons forward function at each timestep-by-timestep is to do every neuron
# forward calculation. Each neuron requires access to other neuron's firing status