ton/Ironpen
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

implement start learning

Browse Source
This commit is contained in:
tonaerospace
2023-05-15 08:33:48 +07:00
parent 89371736e4
commit 68c8a3597d
4 changed files with 74 additions and 68 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
src/.vscode/settings.json vendored Normal file
View File

@@ -0,0 +1 @@
{}

16
src/Ironpen.jl
View File

@@ -34,13 +34,15 @@ using .interface
"""
Todo:
[*3] implement "start learning", reset learning and "during_learning", "end_learning and
"inference"
[4] output neuron connect to random multiple compute neurons
[7] add time-based learning method.
[] implement "thinking period"
[7] time-based learning method based on new error formula
if output neuron not activate when it should, use output neuron's
(vth - vt)*100/vth as error
if output neuron activates when it should NOT, use output neuron's
(vt*100)/vth as error
[*4] output neuron connect to random multiple compute neurons and have the same structure
as lif
[8] verify that model can complete learning cycle with no error
[5] synaptic connection strength concept
[5] synaptic connection strength concept. use sigmoid
[6] neuroplasticity() i.e. change connection
[] using RL to control learning signal
[] consider using Dates.now() instead of timestamp because time_stamp may overflow
@@ -50,6 +52,8 @@ using .interface
[DONE] each knowledgeFn should have its own noise generater
[DONE] where to put pseudo derivative (n.phi)
[DONE] add excitatory, inhabitory to neuron
[DONE] implement "start learning", reset learning and "learning", "end_learning and
"inference"
Change from version: v06_36a
-

81
src/learn.jl
View File

@@ -10,70 +10,37 @@ export learn!
#------------------------------------------------------------------------------------------------100
function learn!(m::model, modelRespond, correctAnswer=nothing, correctTiming=nothing)
function learn!(m::model, modelRespond, correctAnswer=nothing)
m.knowledgeFn[:I].learningStage = m.learningStage
# 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
# set all KFN
if m.learningStage == "start_learning"
m.knowledgeFn[:I].learningStage = "start_learning"
elseif m.learningStage == "end_learning"
m.knowledgeFn[:I].learningStage = "end_learning"
else
end
# success
#WORKING compute error
# timingError =
# how many matched respond and correct answer
matched = sum(isequal(modelRespond, correctAnswer))
too_early = m.modelParams[:perfect_timing] - m.timeStep
model_error = (model_respond .- correct_answer) * too_early
model_error = Flux.logitcrossentropy(model_respond, correct_answer)
output_elements_error = model_respond - correct_answer
learn!(m.knowledgeFn[:I], model_error, output_elements_error)
return model_error
end
# function learn!(m::model, raw_model_respond, correct_answer=nothing)
# if m.learningStage != "doing_inference"
# model_error = Flux.logitcrossentropy(raw_model_respond, correct_answer)
# output_elements_error = raw_model_respond - correct_answer
# learn!(m.knowledgeFn[:I], model_error, output_elements_error)
# else
# model_error = nothing
# end
correctAnswer_I = correctAnswer # correct answer for kfn I
learn!(m.knowledgeFn[:I], correctAnswer_I)
# return model_error
# end
end
""" knowledgeFn learn()
"""
function learn!(kfn::knowledgeFn, error::Union{Float64,Nothing}=nothing,
outputError::Union{Vector,Nothing}=nothing)
kfn.error = error
kfn.outputError = outputError
kfn.learningStage = m.learningStage
if m.learningStage == "start_learning"
function learn!(kfn::kfn_1, correctAnswer=nothing)
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
@@ -85,6 +52,10 @@ function learn!(kfn::knowledgeFn, error::Union{Float64,Nothing}=nothing,
# clear variables
kfn.firedNeurons = Vector{Int64}()
kfn.outputs = nothing
kfn.learningStage = "learning"
elseif kfn.learningStage = "end_learning"
kfn.learningStage = "inference"
end
# Threads.@threads for n in kfn.neuronsArray

42
src/types.jl
View File

@@ -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