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refractoring

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This commit is contained in:
tonaerospace
2023-05-17 14:54:39 +07:00
parent 214466d9e9
commit ab3caad413
3 changed files with 39 additions and 39 deletions
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2
src/forward.jl
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@@ -40,7 +40,7 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
resetLearningParams!(n) resetLearningParams!(n)
end end
for n in kfn.output_neuron for n in kfn.outputNeuronsArray
# epsilonRec need to be reset because it counting how many each synaptic fires and # 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 # use this info to calculate how much synaptic weight should be adjust
resetLearningParams!(n) resetLearningParams!(n)

50
src/snn_utils.jl
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@@ -19,7 +19,7 @@ function timestep_forward!(x::passthrough_neuron)
x.z_t = x.z_t1 x.z_t = x.z_t1
end end
function timestep_forward!(x::Union{compute_neuron, output_neuron}) function timestep_forward!(x::Union{computeNeuron, outputNeuron})
x.z_t = x.z_t1 x.z_t = x.z_t1
x.v_t = x.v_t1 x.v_t = x.v_t1
end end
@@ -28,24 +28,24 @@ no_negative!(x) = x < 0.0 ? 0.0 : x
precision(x::Array{<:Array}) = ( std(mean.(x)) / mean(mean.(x)) ) * 100 precision(x::Array{<:Array}) = ( std(mean.(x)) / mean(mean.(x)) ) * 100
# reset functions for LIF/ALIF neuron # reset functions for LIF/ALIF neuron
reset_last_firing_time!(n::compute_neuron) = n.lastFiringTime = 0.0 reset_last_firing_time!(n::computeNeuron) = n.lastFiringTime = 0.0
reset_refractory_state_active!(n::compute_neuron) = n.refractory_state_active = false reset_refractory_state_active!(n::computeNeuron) = n.refractory_state_active = false
reset_v_t!(n::neuron) = n.v_t = n.vRest reset_v_t!(n::neuron) = n.v_t = n.vRest
reset_z_t!(n::compute_neuron) = n.z_t = false reset_z_t!(n::computeNeuron) = n.z_t = false
reset_epsilonRec!(n::compute_neuron) = n.epsilonRec = n.epsilonRec * 0.0 reset_epsilonRec!(n::computeNeuron) = n.epsilonRec = n.epsilonRec * 0.0
reset_epsilonRec!(n::output_neuron) = n.epsilonRec = n.epsilonRec * 0.0 reset_epsilonRec!(n::outputNeuron) = n.epsilonRec = n.epsilonRec * 0.0
reset_epsilonRecA!(n::alif_neuron) = n.epsilonRecA = n.epsilonRecA * 0.0 reset_epsilonRecA!(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_epsilon_in!(n::computeNeuron) = 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_error!(n::Union{computeNeuron, 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_in_change!(n::computeNeuron) = n.w_in_change = isnothing(n.w_in_change) ? nothing : n.w_in_change * 0.0
reset_wRecChange!(n::compute_neuron) = n.wRecChange = n.wRecChange * 0.0 reset_wRecChange!(n::Union{computeNeuron, outputNeuron}) = n.wRecChange = n.wRecChange * 0.0
reset_a!(n::alif_neuron) = n.a = n.a * 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_a!(n::computeNeuron) = 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 reset_reg_voltage_b!(n::computeNeuron) = n.reg_voltage_b = n.reg_voltage_b * 0.0
reset_reg_voltage_error!(n::compute_neuron) = n.reg_voltage_error = n.reg_voltage_error * 0.0 reset_reg_voltage_error!(n::computeNeuron) = n.reg_voltage_error = n.reg_voltage_error * 0.0
reset_firing_counter!(n::compute_neuron) = n.firingCounter = n.firingCounter * 0.0 reset_firing_counter!(n::computeNeuron) = n.firingCounter = n.firingCounter * 0.0
reset_firing_diff!(n::Union{compute_neuron, linear_neuron}) = n.firingDiff = n.firingDiff * 0.0 reset_firing_diff!(n::Union{computeNeuron, linear_neuron}) = n.firingDiff = n.firingDiff * 0.0
reset_refractoryCounter!(n::Union{compute_neuron, linear_neuron}) = n.refractoryCounter = n.refractoryCounter * 0.0 reset_refractoryCounter!(n::Union{computeNeuron, linear_neuron}) = n.refractoryCounter = n.refractoryCounter * 0.0
# reset function for output neuron # reset function for output neuron
reset_epsilon_j!(n::linear_neuron) = n.epsilon_j = n.epsilon_j * 0.0 reset_epsilon_j!(n::linear_neuron) = n.epsilon_j = n.epsilon_j * 0.0
@@ -218,12 +218,12 @@ function cal_v_reg!(n::alif_neuron)
n.reg_voltage_b = n.reg_voltage_b + (component_b * (n.epsilonRec - n.epsilonRecA)) n.reg_voltage_b = n.reg_voltage_b + (component_b * (n.epsilonRec - n.epsilonRecA))
end end
function voltage_error!(n::compute_neuron) function voltage_error!(n::computeNeuron)
n.reg_voltage_error = 0.5 * n.reg_voltage_a n.reg_voltage_error = 0.5 * n.reg_voltage_a
return n.reg_voltage_error return n.reg_voltage_error
end end
function voltage_regulator!(n::compute_neuron) # running average function voltage_regulator!(n::computeNeuron) # running average
Δw = n.optimiser.eta * n.c_reg_v * n.reg_voltage_b Δw = n.optimiser.eta * n.c_reg_v * n.reg_voltage_b
return Δw return Δw
end end
@@ -233,7 +233,7 @@ function firingRateError(kfn::knowledgeFn)
return 0.5 * sum([(n.firingDiff)^2 for n in kfn.neuronsArray[start_id:end]]) return 0.5 * sum([(n.firingDiff)^2 for n in kfn.neuronsArray[start_id:end]])
end end
function firing_rate_regulator!(n::compute_neuron) function firing_rate_regulator!(n::computeNeuron)
# n.firingRate NOT running average (average over learning batch) # n.firingRate NOT running average (average over learning batch)
Δw = n.optimiser.eta * n.c_reg * Δw = n.optimiser.eta * n.c_reg *
(n.firingRate - n.firingRateTarget) * n.eRec (n.firingRate - n.firingRateTarget) * n.eRec
@@ -241,10 +241,10 @@ function firing_rate_regulator!(n::compute_neuron)
return Δw return Δw
end end
firing_rate!(n::compute_neuron) = n.firingRate = (n.firingCounter / n.timeStep) * 1000 firing_rate!(n::computeNeuron) = n.firingRate = (n.firingCounter / n.timeStep) * 1000
firing_diff!(n::compute_neuron) = n.firingDiff = n.firingRate - n.firingRateTarget firing_diff!(n::computeNeuron) = n.firingDiff = n.firingRate - n.firingRateTarget
function adjust_internal_learning_rate!(n::compute_neuron) function adjust_internal_learning_rate!(n::computeNeuron)
n.internal_learning_rate = n.error_diff[end] < 0.0 ? n.internal_learning_rate * 0.99 : n.internal_learning_rate = n.error_diff[end] < 0.0 ? n.internal_learning_rate * 0.99 :
n.internal_learning_rate * 1.005 n.internal_learning_rate * 1.005
end end
@@ -276,7 +276,7 @@ end
""" Compute all synaptic connection strength of a neuron. Also mark n.wRec to 0 if wRec goes """ Compute all synaptic connection strength of a neuron. Also mark n.wRec to 0 if wRec goes
below lowerlimit. below lowerlimit.
""" """
function synapticConnStrength!(n::Union{compute_neuron, output_neuron}) 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
updown = n.epsilonRec[i] == 0.0 ? "down" : "up" updown = n.epsilonRec[i] == 0.0 ? "down" : "up"
@@ -290,9 +290,9 @@ function synapticConnStrength!(n::Union{compute_neuron, output_neuron})
end end
end end
function synapticConnStrength!(n::input_neuron) end function synapticConnStrength!(n::inputNeuron) end
function neuroplasticity!(n::compute_neuron, firedNeurons::Vector) function neuroplasticity!(n::computeNeuron, firedNeurons::Vector)
# if there is 0-weight then replace it with new connection # if there is 0-weight then replace it with new connection
zero_weight_index = findall(iszero.(n.wRec)) zero_weight_index = findall(iszero.(n.wRec))
if length(zero_weight_index) != 0 if length(zero_weight_index) != 0

26
src/types.jl
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@@ -3,7 +3,7 @@ module types
export export
# struct # struct
IronpenStruct, model, knowledgeFn, lif_neuron, alif_neuron, linear_neuron, IronpenStruct, model, knowledgeFn, lif_neuron, alif_neuron, linear_neuron,
kfn_1, input_neuron, compute_neuron, neuron, output_neuron, passthrough_neuron, kfn_1, inputNeuron, computeNeuron, neuron, outputNeuron, passthrough_neuron,
# function # function
instantiate_custom_types, init_neuron, populate_neuron, instantiate_custom_types, init_neuron, populate_neuron,
@@ -16,9 +16,9 @@ using Random, Flux, LinearAlgebra
abstract type Ironpen end abstract type Ironpen end
abstract type knowledgeFn <: Ironpen end abstract type knowledgeFn <: Ironpen end
abstract type neuron <: Ironpen end abstract type neuron <: Ironpen end
abstract type input_neuron <: neuron end abstract type inputNeuron <: neuron end
abstract type output_neuron <: neuron end abstract type outputNeuron <: neuron end
abstract type compute_neuron <: neuron end abstract type computeNeuron <: neuron end
#------------------------------------------------------------------------------------------------100 #------------------------------------------------------------------------------------------------100
@@ -207,8 +207,8 @@ function kfn_1(kfnParams::Dict)
end end
# add compute neurons # add compute neurons
for (k, v) in kfn.kfnParams[:compute_neuron] for (k, v) in kfn.kfnParams[:computeNeuron]
current_type = kfn.kfnParams[:compute_neuron][k] current_type = kfn.kfnParams[:computeNeuron][k]
for i = 1:current_type[:numbers] for i = 1:current_type[:numbers]
n_id = length(kfn.neuronsArray) + 1 n_id = length(kfn.neuronsArray) + 1
neuron = init_neuron(n_id, current_type[:params], kfn.kfnParams) neuron = init_neuron(n_id, current_type[:params], kfn.kfnParams)
@@ -223,12 +223,12 @@ function kfn_1(kfnParams::Dict)
end end
for n in kfn.neuronsArray for n in kfn.neuronsArray
if typeof(n) <: compute_neuron if typeof(n) <: computeNeuron
n.firingRateTarget = kfn.kfnParams[:neuron_firing_rate_target] n.firingRateTarget = kfn.kfnParams[:neuron_firing_rate_target]
end end
end end
# excitatory neuron to inhabitory neuron = 60:40 % of compute_neuron # excitatory neuron to inhabitory neuron = 60:40 % of computeNeuron
ex_number = Int(floor(0.6 * kfn.kfnParams[:compute_neuron_number])) ex_number = Int(floor(0.6 * kfn.kfnParams[:compute_neuron_number]))
ex_n = [1 for i in 1:ex_number] ex_n = [1 for i in 1:ex_number]
in_number = kfn.kfnParams[:compute_neuron_number] - ex_number in_number = kfn.kfnParams[:compute_neuron_number] - ex_number
@@ -241,7 +241,7 @@ function kfn_1(kfnParams::Dict)
try n.ExInType = pop!(ex_in) catch end try n.ExInType = pop!(ex_in) catch end
end end
# add ExInType into each compute_neuron subExInType # add ExInType into each computeNeuron subExInType
for n in reverse(kfn.neuronsArray) for n in reverse(kfn.neuronsArray)
try # input neuron doest have n.subscriptionList try # input neuron doest have n.subscriptionList
for sub_id in n.subscriptionList for sub_id in n.subscriptionList
@@ -270,7 +270,7 @@ end
""" passthrough_neuron struct """ passthrough_neuron struct
""" """
Base.@kwdef mutable struct passthrough_neuron <: input_neuron Base.@kwdef mutable struct passthrough_neuron <: inputNeuron
id::Union{Int64,Nothing} = nothing # ID of this neuron which is it position in knowledgeFn array id::Union{Int64,Nothing} = nothing # ID of this neuron which is it position in knowledgeFn array
type::String = "passthrough_neuron" type::String = "passthrough_neuron"
knowledgeFnName::Union{String,Nothing} = nothing # knowledgeFn that this neuron belongs to knowledgeFnName::Union{String,Nothing} = nothing # knowledgeFn that this neuron belongs to
@@ -300,7 +300,7 @@ end
""" lif_neuron struct """ lif_neuron struct
""" """
Base.@kwdef mutable struct lif_neuron <: compute_neuron Base.@kwdef mutable struct lif_neuron <: computeNeuron
id::Union{Int64,Nothing} = nothing # this neuron ID i.e. position of this neuron in knowledgeFn id::Union{Int64,Nothing} = nothing # this neuron ID i.e. position of this neuron in knowledgeFn
type::String = "lif_neuron" type::String = "lif_neuron"
ExInType::Integer = 1 # 1 excitatory, -1 inhabitory ExInType::Integer = 1 # 1 excitatory, -1 inhabitory
@@ -392,7 +392,7 @@ end
""" alif_neuron struct """ alif_neuron struct
""" """
Base.@kwdef mutable struct alif_neuron <: compute_neuron Base.@kwdef mutable struct alif_neuron <: computeNeuron
id::Union{Int64,Nothing} = nothing # this neuron ID i.e. position of this neuron in knowledgeFn id::Union{Int64,Nothing} = nothing # this neuron ID i.e. position of this neuron in knowledgeFn
type::String = "alif_neuron" type::String = "alif_neuron"
ExInType::Integer = -1 # 1 excitatory, -1 inhabitory ExInType::Integer = -1 # 1 excitatory, -1 inhabitory
@@ -500,7 +500,7 @@ end
#------------------------------------------------------------------------------------------------100 #------------------------------------------------------------------------------------------------100
""" linear_neuron struct """ linear_neuron struct
""" """
Base.@kwdef mutable struct linear_neuron <: output_neuron Base.@kwdef mutable struct linear_neuron <: outputNeuron
id::Union{Int64,Nothing} = nothing # ID of this neuron which is it position in knowledgeFn array id::Union{Int64,Nothing} = nothing # ID of this neuron which is it position in knowledgeFn array
type::String = "linear_neuron" type::String = "linear_neuron"
knowledgeFnName::Union{String,Nothing} = nothing # knowledgeFn that this neuron belongs to knowledgeFnName::Union{String,Nothing} = nothing # knowledgeFn that this neuron belongs to