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reset_epsilonRec after ΔwRecChange is calculated

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This commit is contained in:
tonaerospace
2023-05-16 22:04:47 +07:00
parent 0ac5a703ea
commit 7d943bce6a
5 changed files with 75 additions and 120 deletions
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3
src/Ironpen.jl
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@@ -34,8 +34,6 @@ using .interface
"""
Todo:
[9] verify that model can complete learning cycle with no error
[*5] synaptic connection strength concept. use sigmoid, turn connection offline
[8] neuroplasticity() i.e. change connection
@@ -58,6 +56,7 @@ using .interface
if output neuron activates when it should NOT, use output neuron's
(vt*100)/vth as error
[DONE] use LinearAlgebra.normalize!(vector, 1) to adjust weight after weight merge
[DONE] reset_epsilonRec after ΔwRecChange is calculated
Change from version: v06_36a
-

12
src/forward.jl
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@@ -102,7 +102,7 @@ function (n::lif_neuron)(kfn::knowledgeFn)
# decay of v_t1
n.v_t1 = n.alpha * n.v_t
else
n.recSignal = sum(n.w_rec .* 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.v_t1 = n.alpha_v_t + n.recSignal
@@ -119,6 +119,8 @@ function (n::lif_neuron)(kfn::knowledgeFn)
# there is a difference from alif formula
n.phi = (n.gammaPd / n.v_th) * max(0, 1 - (n.v_t1 - n.v_th) / n.v_th)
n.decayedEpsilonRec = n.alpha * n.epsilonRec
n.epsilonRec = n.decayedEpsilonRec + n.z_i_t
end
end
@@ -147,7 +149,7 @@ 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.w_rec .* 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.v_t1 = n.alpha_v_t + n.recSignal
n.v_t1 = no_negative!.(n.v_t1)
@@ -162,6 +164,8 @@ function (n::alif_neuron)(kfn::knowledgeFn)
# there is a difference from lif formula
n.phi = (n.gammaPd / n.v_th) * max(0, 1 - (n.v_t1 - n.av_th) / n.v_th)
n.decayedEpsilonRec = n.alpha * n.epsilonRec
n.epsilonRec = n.decayedEpsilonRec + n.z_i_t
end
end
@@ -188,7 +192,7 @@ 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.w_rec .* 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.v_t1 = n.alpha_v_t + n.recSignal
@@ -205,6 +209,8 @@ function (n::linear_neuron)(kfn::T) where T<:knowledgeFn
# there is a difference from alif formula
n.phi = (n.gammaPd / n.v_th) * max(0, 1 - (n.v_t1 - n.v_th) / n.v_th)
n.decayedEpsilonRec = n.alpha * n.epsilonRec
n.epsilonRec = n.decayedEpsilonRec + n.z_i_t
end
end

115
src/learn.jl
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@@ -59,23 +59,12 @@ function learn!(kfn::kfn_1, correctAnswer::AbstractVector)
kfn.firedNeurons_t1 = Vector{Bool}()
kfn.learningStage = "learning"
#TODO prepare for end learning
elseif kfn.learningStage == "end_learning"
resetLearningParams!(n)
# clear variables
kfn.firedNeurons = Vector{Int64}()
kfn.firedNeurons_t0 = Vector{Bool}()
kfn.firedNeurons_t1 = Vector{Bool}()
kfn.learningStage = "inference"
end
# compute kfn error
out = [n.z_t1 for n in kfn.outputNeuronsArray]
for (i, v) in enumerate(out)
if v != correctAnswer[i] # need to adjust weight
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].v_t) *
100 / kfn.outputNeuronsArray[i].v_th
@@ -87,55 +76,36 @@ function learn!(kfn::kfn_1, correctAnswer::AbstractVector)
learn!(kfn.outputNeuronsArray[i], kfn)
end
end
#WORKING
# Threads.@threads for n in kfn.neuronsArray
for n in kfn.neuronsArray
learn!(n, kfn) # Neurons are always learning, besides error from model output
end
if kfn.outputError !== nothing
# Threads.@threads for n in kfn.outputNeuronsArray
for n in kfn.outputNeuronsArray # not use multithreading because 1st output neuron
# will set learning rate that will be used by
# other output neurons
learn!(n, kfn)
end
# for main loop user's display and training's exit condition
avgNeuronsFiringRate = 0.0
for n in kfn.neuronsArray
if typeof(n) <: compute_neuron
avgNeuronsFiringRate += n.firingRate
end
end
kfn.avgNeuronsFiringRate = avgNeuronsFiringRate /
kfn.kfnParams[:compute_neuron_number]
avgNeurons_v_t1 = 0.0
for n in kfn.neuronsArray
if typeof(n) <: compute_neuron
avgNeurons_v_t1 += n.v_t1
end
end
kfn.avgNeurons_v_t1 = avgNeurons_v_t1 / kfn.kfnParams[:compute_neuron_number]
end
# wrap up learning session
if kfn.learningStage == "end_learning"
# Threads.@threads for n in kfn.neuronsArray
for n in kfn.neuronsArray
n.wRec += n.wRecChange # merge wRecChange into wRec
wSign = sign.(n.wRec) # check for fliped sign, 1 indicates non-fliped sign
nonFlipedSign = isequal.(n.subExInType, wSign) # 1 not fliped, 0 fliped
LinearAlgebra.normalize!(n.wRec, 1)
n.wRec .*= nonFlipedSign # set weight that fliped sign to 0 for random new connection
#WORKING synapticConnStrength
#TODO neuroplasticity
end
for n in kfn.outputNeuronsArray # merge wRecChange into wRec
n.wRec += n.wRecChange
wSign = sign.(n.wRec) # check for fliped sign, 1 indicates non-fliped sign
nonFlipedSign = isequal.(n.subExInType, wSign) # 1 not fliped, 0 fliped
LinearAlgebra.normalize!(n.wRec, 1)
n.wRec .*= nonFlipedSign # set weight that fliped sign to 0 for random new connection
#TODO synapticConnStrength
#TODO neuroplasticity
end
resetLearningParams!(n)
# clear variables
@@ -156,25 +126,16 @@ end
""" lif learn()
"""
function learn!(n::lif_neuron, error::Number)
n.decayedEpsilonRec = n.alpha * n.epsilonRec
n.epsilonRec = n.decayedEpsilonRec + n.z_i_t
n.eRec = n.phi * n.epsilonRec
ΔwRecChange = n.eta * error
n.wRecChange = (n.subExInType * n.wRecChange) + ΔwRecChange
LinearAlgebra.normalize!(n.wRecChange, 1)
# check for fliped sign, 1 indicates non-fliped sign
wSign = sign.(n.wRecChange)
nonFlipedSign = isequal.(n.subExInType, wSign) # 1 not fliped, 0 fliped
n.wRecChange .*= nonFlipedSign # set weight that fliped sign to 0 for random new connection
reset_epsilonRec!(n)
end
""" alif_neuron learn()
"""
function learn!(n::alif_neuron, error::Number)
n.decayedEpsilonRec = n.alpha * n.epsilonRec
n.epsilonRec = n.decayedEpsilonRec + n.z_i_t
n.epsilonRecA = (n.phi * n.epsilonRec) +
((n.rho - (n.phi * n.beta)) * n.epsilonRecA)
n.eRec_v = n.phi * n.epsilonRec
@@ -183,29 +144,17 @@ function learn!(n::alif_neuron, error::Number)
ΔwRecChange = n.eta * error
n.wRecChange = (n.subExInType * n.wRecChange) + ΔwRecChange
LinearAlgebra.normalize!(n.wRecChange, 1)
# check for fliped sign, 1 indicates non-fliped sign
wSign = sign.(n.wRecChange)
nonFlipedSign = isequal.(n.subExInType, wSign) # 1 not fliped, 0 fliped
n.wRecChange .*= nonFlipedSign # set weight that fliped sign to 0 for random new connection
reset_epsilonRec!(n)
end
""" linear_neuron learn()
"""
function learn!(n::linear_neuron, error::Number)
n.decayedEpsilonRec = n.alpha * n.epsilonRec
n.epsilonRec = n.decayedEpsilonRec + n.z_i_t
n.eRec = n.phi * n.epsilonRec
ΔwRecChange = n.eta * error
n.wRecChange = (n.subExInType * n.wRecChange) + ΔwRecChange
LinearAlgebra.normalize!(n.wRecChange, 1)
# check for fliped sign, 1 indicates non-fliped sign
wSign = sign.(n.wRecChange)
nonFlipedSign = isequal.(n.subExInType, wSign) # 1 not fliped, 0 fliped
n.wRecChange .*= nonFlipedSign # set weight that fliped sign to 0 for random new connection
reset_epsilonRec!(n)
end

27
src/snn_utils.jl
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@@ -3,7 +3,7 @@ module snn_utils
using Flux.Optimise: apply!
export calculate_α, calculate_ρ, calculate_k, timestep_forward!, init_neuron, no_negative!,
precision, calculate_w_change!, store_knowledgefn_error!, interneurons_adjustment!,
reset_z_t!, resetLearningParams!, reset_learning_history_params!,
reset_z_t!, resetLearningParams!, reset_learning_history_params!, reset_epsilonRec!,
cal_v_reg!, calculate_w_change_end!,
firing_rate_error!, firing_rate_regulator!, update_Bn!, cal_firing_reg!,
neuroplasticity!, shakeup!, reset_learning_no_wchange!, adjust_internal_learning_rate!,
@@ -32,12 +32,13 @@ reset_last_firing_time!(n::compute_neuron) = n.lastFiringTime = 0.0
reset_refractory_state_active!(n::compute_neuron) = n.refractory_state_active = false
reset_v_t!(n::neuron) = n.v_t = n.vRest
reset_z_t!(n::compute_neuron) = n.z_t = false
reset_epsilon_rec!(n::compute_neuron) = n.epsilonRec = n.epsilonRec * 0.0
reset_epsilonRec!(n::compute_neuron) = n.epsilonRec = n.epsilonRec * 0.0
reset_epsilonRec!(n::output_neuron) = n.epsilonRec = n.epsilonRec * 0.0
reset_epsilon_rec_a!(n::alif_neuron) = n.epsilonRecA = n.epsilonRecA * 0.0
reset_epsilon_in!(n::compute_neuron) = n.epsilon_in = isnothing(n.epsilon_in) ? nothing : n.epsilon_in * 0.0
reset_error!(n::Union{compute_neuron, linear_neuron}) = n.error = nothing
reset_w_in_change!(n::compute_neuron) = n.w_in_change = isnothing(n.w_in_change) ? nothing : n.w_in_change * 0.0
reset_w_rec_change!(n::compute_neuron) = n.wRecChange = n.wRecChange * 0.0
reset_wRecChange!(n::compute_neuron) = n.wRecChange = n.wRecChange * 0.0
reset_a!(n::alif_neuron) = n.a = n.a * 0.0
reset_reg_voltage_a!(n::compute_neuron) = n.reg_voltage_a = n.reg_voltage_a * 0.0
reset_reg_voltage_b!(n::compute_neuron) = n.reg_voltage_b = n.reg_voltage_b * 0.0
@@ -57,7 +58,7 @@ reset_b_change!(n::linear_neuron) = n.b_change = n.b_change * 0.0
session
"""
# function reset_learning_no_wchange!(n::lif_neuron)
# reset_epsilon_rec!(n)
# reset_epsilonRec!(n)
# # reset_v_t!(n)
# # reset_z_t!(n)
# # reset_reg_voltage_a!(n)
@@ -73,7 +74,7 @@ reset_b_change!(n::linear_neuron) = n.b_change = n.b_change * 0.0
# # reset_refractory_state_active!(n)
# end
# function reset_learning_no_wchange!(n::Union{alif_neuron, elif_neuron})
# reset_epsilon_rec!(n)
# reset_epsilonRec!(n)
# reset_epsilon_rec_a!(n)
# reset_v_t!(n)
# reset_z_t!(n)
@@ -99,8 +100,8 @@ reset_b_change!(n::linear_neuron) = n.b_change = n.b_change * 0.0
""" Reset all learning-related params at the END of learning session
"""
function resetLearningParams!(n::lif_neuron)
reset_epsilon_rec!(n)
reset_w_rec_change!(n)
reset_epsilonRec!(n)
reset_wRecChange!(n)
# reset_v_t!(n)
# reset_z_t!(n)
reset_firing_counter!(n)
@@ -111,9 +112,9 @@ function resetLearningParams!(n::lif_neuron)
reset_refractoryCounter!(n)
end
function resetLearningParams!(n::alif_neuron)
reset_epsilon_rec!(n)
reset_epsilonRec!(n)
reset_epsilon_rec_a!(n)
reset_w_rec_change!(n)
reset_wRecChange!(n)
# reset_v_t!(n)
# reset_z_t!(n)
# reset_a!(n)
@@ -133,8 +134,8 @@ function resetLearningParams!(n::passthrough_neuron)
end
function resetLearningParams!(n::linear_neuron)
reset_epsilon_rec!(n)
reset_w_rec_change!(n)
reset_epsilonRec!(n)
reset_wRecChange!(n)
reset_v_t!(n)
reset_firing_counter!(n)
@@ -245,7 +246,7 @@ firing_diff!(n::compute_neuron) = n.firingDiff = n.firingRate - n.firingRateTarg
function neuroplasticity!(n::compute_neuron, firedNeurons::Vector)
# if there is 0-weight then replace it with new connection
zero_weight_index = findall(iszero.(n.w_rec))
zero_weight_index = findall(iszero.(n.wRec))
if length(zero_weight_index) != 0
""" sampling new connection from list of neurons that fires instead of ramdom choose from
all compute neuron because there is no point to connect to neuron that not fires i.e.
@@ -262,7 +263,7 @@ function neuroplasticity!(n::compute_neuron, firedNeurons::Vector)
for i in zero_weight_index
if Utils.random_choices([true, false], percentage)
n.subscriptionList[i] = pop!(subscribe_options)
n.w_rec[i] = 0.01 # new connection should not send large signal otherwise it would throw
n.wRec[i] = 0.01 # new connection should not send large signal otherwise it would throw
# RSNN off path. Let weight grow by an optimiser
end
end

28
src/types.jl
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@@ -34,7 +34,7 @@ Base.@kwdef mutable struct model <: Ironpen
"learning" = neuron will accumulate epsilon_j, compute Δw_rec_change each time
correct answer is available then merge Δw_rec_change into wRecChange then
reset epsilon_j.
"reflect" = neuron will merge wRecChange into w_rec then reset wRecChange. """
"reflect" = neuron will merge wRecChange into wRec then reset wRecChange. """
learningStage::String = "inference"
softreset::Bool = false
@@ -102,7 +102,7 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
"learning" = neuron will accumulate epsilon_j, compute Δw_rec_change each time
correct answer is available then merge Δw_rec_change into wRecChange then
reset epsilon_j.
"reflect" = neuron will merge wRecChange into w_rec then reset wRecChange. """
"reflect" = neuron will merge wRecChange into wRec then reset wRecChange. """
learningStage::String = "inference"
error::Union{Float64,Nothing} = nothing
@@ -312,7 +312,7 @@ Base.@kwdef mutable struct lif_neuron <: compute_neuron
subscriptionList::Union{Array{Int64},Nothing} = nothing # list of other neuron that this neuron synapse subscribed to
subExInType::Array{Int64} = Vector{Int64}() # store ExIn type of subscribed neurons
timeStep::Number = 0.0 # current time
w_rec::Union{Array{Float64},Nothing} = nothing # synaptic weight (for receiving signal from other neuron)
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_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
@@ -340,7 +340,7 @@ Base.@kwdef mutable struct lif_neuron <: compute_neuron
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
wRecChange::Union{Array{Float64},Nothing} = nothing # Δw_rec, cumulated wRec 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
@@ -356,7 +356,7 @@ Base.@kwdef mutable struct lif_neuron <: compute_neuron
"learning" = neuron will accumulate epsilon_j, compute Δw_rec_change each time
correct answer is available then merge Δw_rec_change into wRecChange then
reset epsilon_j.
"reflect" = neuron will merge wRecChange into w_rec then reset wRecChange. """
"reflect" = neuron will merge wRecChange into wRec then reset wRecChange. """
learningStage::String = "inference"
end
@@ -405,7 +405,7 @@ Base.@kwdef mutable struct alif_neuron <: compute_neuron
subscriptionList::Union{Array{Int64},Nothing} = nothing # list of other neuron that this neuron synapse subscribed to
subExInType::Array{Int64} = Vector{Int64}() # store ExIn type of subscribed neurons
timeStep::Union{Number,Nothing} = nothing # current time
w_rec::Union{Array{Float64},Nothing} = nothing # synaptic weight (for receiving signal from other neuron)
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_t1::Float64 = 0.0 # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep
v_t_default::Union{Float64,Nothing} = 0.0
@@ -436,7 +436,7 @@ Base.@kwdef mutable struct alif_neuron <: compute_neuron
# refractory_state_active::Union{Bool,Nothing} = false # if true, neuron is in refractory state and cannot process new information
refractoryCounter::Integer = 0
tau_m::Union{Float64,Nothing} = nothing # τ_m, membrane time constant in millisecond
wRecChange::Union{Array{Float64},Nothing} = nothing # Δw_rec, cumulated w_rec change
wRecChange::Union{Array{Float64},Nothing} = nothing # Δw_rec, cumulated wRec 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
@@ -458,7 +458,7 @@ Base.@kwdef mutable struct alif_neuron <: compute_neuron
"learning" = neuron will accumulate epsilon_j, compute Δw_rec_change each time
correct answer is available then merge Δw_rec_change into wRecChange then
reset epsilon_j.
"reflect" = neuron will merge wRecChange into w_rec then reset wRecChange. """
"reflect" = neuron will merge wRecChange into wRec then reset wRecChange. """
learningStage::String = "inference"
end
@@ -513,7 +513,7 @@ Base.@kwdef mutable struct linear_neuron <: output_neuron
timeStep::Union{Number,Nothing} = nothing # current time
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)
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 = 0.0 # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep
v_t_default::Union{Float64,Nothing} = 0.0 # default membrane potential voltage
@@ -542,7 +542,7 @@ Base.@kwdef mutable struct linear_neuron <: output_neuron
refractoryCounter::Integer = 0
tau_out::Union{Float64,Nothing} = nothing # τ_out, 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
wRecChange::Union{Array{Float64},Nothing} = nothing # Δw_rec, cumulated wRec 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
@@ -614,7 +614,7 @@ end
# end
# filter!(x -> x != n.id, n.subscriptionList)
# n.epsilonRec = zeros(length(n.subscriptionList))
# n.w_rec = Random.rand(length(n.subscriptionList))
# n.wRec = Random.rand(length(n.subscriptionList))
# n.wRecChange = zeros(length(n.subscriptionList))
# n.reg_voltage_b = zeros(length(n.subscriptionList))
# n.alpha = calculate_α(n)
@@ -633,7 +633,7 @@ function init_neuron!(id::Int64, n::lif_neuron, n_params::Dict, kfnParams::Dict)
n.synapticStrength = normalize!(rand(length(n.subscriptionList)), 1)
n.epsilonRec = zeros(length(n.subscriptionList))
n.w_rec = Random.rand(length(n.subscriptionList))
n.wRec = Random.rand(length(n.subscriptionList))
n.wRecChange = zeros(length(n.subscriptionList))
n.alpha = calculate_α(n)
end
@@ -652,7 +652,7 @@ function init_neuron!(id::Int64, n::alif_neuron, n_params::Dict,
n.synapticStrength = normalize!(rand(length(n.subscriptionList)), 1)
n.epsilonRec = zeros(length(n.subscriptionList))
n.w_rec = Random.rand(length(n.subscriptionList))
n.wRec = Random.rand(length(n.subscriptionList))
n.wRecChange = zeros(length(n.subscriptionList))
# the more time has passed from the last time neuron was activated, the more
@@ -673,7 +673,7 @@ function init_neuron!(id::Int64, n::linear_neuron, n_params::Dict, kfnParams::Di
n.subscriptionList = [pop!(subscription_options) for i = 1:subscription_numbers]
n.synapticStrength = normalize!(rand(length(n.subscriptionList)), 1)
n.epsilonRec = zeros(length(n.subscriptionList))
n.w_rec = Random.rand(length(n.subscriptionList))
n.wRec = Random.rand(length(n.subscriptionList))
n.wRecChange = zeros(length(n.subscriptionList))
n.alpha = calculate_k(n)
end