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implement start learning

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tonaerospace
2023-05-15 08:33:48 +07:00
parent 89371736e4
commit 68c8a3597d
4 changed files with 74 additions and 68 deletions
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42
src/types.jl
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@@ -106,7 +106,6 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
learningStage::String = "inference"
error::Union{Float64,Nothing} = nothing
outputError::Union{Array,Nothing} = Vector{AbstractFloat}()
softreset::Bool = false
firedNeurons::Array{Int64} = Vector{Int64}() # store unique id of firing neurons to be used when random neuron connection
@@ -331,7 +330,7 @@ Base.@kwdef mutable struct lif_neuron <: compute_neuron
decayedEpsilonRec::Union{Array{Float64},Nothing} = nothing # α * epsilonRec
eRec::Union{Array{Float64},Nothing} = nothing # eligibility trace for neuron spike
delta::Union{Float64,Nothing} = 1.0 # δ, discreate timestep size in millisecond
lastFiringTime::Union{Float64,Nothing} = 0.0 # the last time neuron fires
lastFiringTime::Union{Float64,Nothing} = 0.0 # the last time neuron fires, use to calculate exponantial decay of v_t1
refractoryDuration::Union{Float64,Nothing} = 3 # neuron's refratory period in millisecond
# refractory_state_active::Union{Bool,Nothing} = false # if true, neuron is in refractory state and cannot process new information
refractoryCounter::Integer = 0
@@ -340,7 +339,6 @@ Base.@kwdef mutable struct lif_neuron <: compute_neuron
wRecChange::Union{Array{Float64},Nothing} = nothing # Δw_rec, cumulated w_rec change
recSignal::Union{Float64,Nothing} = nothing # incoming recurrent signal
alpha_v_t::Union{Float64,Nothing} = nothing # alpha * v_t
voltageDropPercentage::Union{Float64,Nothing} = 1.0 # voltage drop as a percentage of v_th
error::Union{Float64,Nothing} = nothing # local neuron error
optimiser::Union{Any,Nothing} = load_optimiser("AdaBelief") # Flux optimizer
@@ -428,7 +426,7 @@ Base.@kwdef mutable struct alif_neuron <: compute_neuron
eRec::Union{Array{Float64},Nothing} = nothing # neuron's eligibility trace
eta::Union{Float64,Nothing} = 0.01 # eta, learning rate
gammaPd::Union{Float64,Nothing} = 0.3 # γ_pd, discount factor, value from paper
lastFiringTime::Union{Float64,Nothing} = 0.0 # the last time neuron fires
lastFiringTime::Union{Float64,Nothing} = 0.0 # the last time neuron fires, use to calculate exponantial decay of v_t1
phi::Union{Float64,Nothing} = nothing # ϕ, psuedo derivative
refractoryDuration::Union{Float64,Nothing} = 3 # neuron's refractory period in millisecond
# refractory_state_active::Union{Bool,Nothing} = false # if true, neuron is in refractory state and cannot process new information
@@ -437,7 +435,6 @@ Base.@kwdef mutable struct alif_neuron <: compute_neuron
wRecChange::Union{Array{Float64},Nothing} = nothing # Δw_rec, cumulated w_rec change
recSignal::Union{Float64,Nothing} = nothing # incoming recurrent signal
alpha_v_t::Union{Float64,Nothing} = nothing # alpha * v_t
voltageDropPercentage::Union{Float64,Nothing} = 1.0 # voltage drop as a percentage of v_th
error::Union{Float64,Nothing} = nothing # local neuron error
optimiser::Union{Any,Nothing} = load_optimiser("AdaBelief") # Flux optimizer
@@ -510,9 +507,42 @@ 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
delta::Union{Float64,Nothing} = 1.0 # δ, discreate timestep size in millisecond
out_t::Bool = false # output of linear neuron BEFORE forward()
out_t1::Bool = false # output of linear neuron AFTER forward()
#WORKING
subExInType::Array{Int64} = Vector{Int64}() # store ExIn type of subscribed neurons
w_rec::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 = 0.0 # 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
# 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
# during v_t1 calculation hence I need a variable to hold z_t1 so that I'm not replacing z_t
z_t1::Bool = false # neuron postsynaptic firing at current timestep (after neuron's calculation)
# neuron presynaptic firing at current timestep (which is other neuron postsynaptic firing of
# previous timestep)
z_i_t::Union{Array{Bool},Nothing} = nothing
gammaPd::Union{Float64,Nothing} = 0.3 # γ_pd, discount factor, value from paper
alpha::Union{Float64,Nothing} = nothing # α, neuron membrane potential decay factor
phi::Union{Float64,Nothing} = nothing # ϕ, psuedo derivative
epsilonRec::Union{Array{Float64},Nothing} = nothing # ϵ_rec, eligibility vector for neuron spike
decayedEpsilonRec::Union{Array{Float64},Nothing} = nothing # α * epsilonRec
eRec::Union{Array{Float64},Nothing} = nothing # eligibility trace for neuron spike
delta::Union{Float64,Nothing} = 1.0 # δ, discreate timestep size in millisecond
lastFiringTime::Union{Float64,Nothing} = 0.0 # the last time neuron fires, use to calculate exponantial decay of v_t1
refractoryDuration::Union{Float64,Nothing} = 3 # neuron's refratory period in millisecond
refractoryCounter::Integer = 0
tau_m::Union{Float64,Nothing} = nothing # τ_m, membrane time constant in millisecond
eta::Union{Float64,Nothing} = 0.01 # η, learning rate
wRecChange::Union{Array{Float64},Nothing} = nothing # Δw_rec, cumulated w_rec change
recSignal::Union{Float64,Nothing} = nothing # incoming recurrent signal
alpha_v_t::Union{Float64,Nothing} = nothing # alpha * v_t
error::Union{Float64,Nothing} = nothing # local neuron error
end
""" linear neuron outer constructor