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refractoring

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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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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
end
function timestep_forward!(x::Union{compute_neuron, output_neuron})
function timestep_forward!(x::Union{computeNeuron, outputNeuron})
x.z_t = x.z_t1
x.v_t = x.v_t1
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
# reset functions for LIF/ALIF neuron
reset_last_firing_time!(n::compute_neuron) = n.lastFiringTime = 0.0
reset_refractory_state_active!(n::compute_neuron) = n.refractory_state_active = false
reset_last_firing_time!(n::computeNeuron) = n.lastFiringTime = 0.0
reset_refractory_state_active!(n::computeNeuron) = 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_epsilonRec!(n::compute_neuron) = n.epsilonRec = n.epsilonRec * 0.0
reset_epsilonRec!(n::output_neuron) = n.epsilonRec = n.epsilonRec * 0.0
reset_z_t!(n::computeNeuron) = n.z_t = false
reset_epsilonRec!(n::computeNeuron) = 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_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_wRecChange!(n::compute_neuron) = n.wRecChange = n.wRecChange * 0.0
reset_epsilon_in!(n::computeNeuron) = n.epsilon_in = isnothing(n.epsilon_in) ? nothing : n.epsilon_in * 0.0
reset_error!(n::Union{computeNeuron, linear_neuron}) = n.error = nothing
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::Union{computeNeuron, outputNeuron}) = 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
reset_reg_voltage_error!(n::compute_neuron) = n.reg_voltage_error = n.reg_voltage_error * 0.0
reset_firing_counter!(n::compute_neuron) = n.firingCounter = n.firingCounter * 0.0
reset_firing_diff!(n::Union{compute_neuron, linear_neuron}) = n.firingDiff = n.firingDiff * 0.0
reset_refractoryCounter!(n::Union{compute_neuron, linear_neuron}) = n.refractoryCounter = n.refractoryCounter * 0.0
reset_reg_voltage_a!(n::computeNeuron) = n.reg_voltage_a = n.reg_voltage_a * 0.0
reset_reg_voltage_b!(n::computeNeuron) = n.reg_voltage_b = n.reg_voltage_b * 0.0
reset_reg_voltage_error!(n::computeNeuron) = n.reg_voltage_error = n.reg_voltage_error * 0.0
reset_firing_counter!(n::computeNeuron) = n.firingCounter = n.firingCounter * 0.0
reset_firing_diff!(n::Union{computeNeuron, linear_neuron}) = n.firingDiff = n.firingDiff * 0.0
reset_refractoryCounter!(n::Union{computeNeuron, linear_neuron}) = n.refractoryCounter = n.refractoryCounter * 0.0
# reset function for output neuron
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))
end
function voltage_error!(n::compute_neuron)
function voltage_error!(n::computeNeuron)
n.reg_voltage_error = 0.5 * n.reg_voltage_a
return n.reg_voltage_error
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
return Δw
end
@@ -233,7 +233,7 @@ function firingRateError(kfn::knowledgeFn)
return 0.5 * sum([(n.firingDiff)^2 for n in kfn.neuronsArray[start_id:end]])
end
function firing_rate_regulator!(n::compute_neuron)
function firing_rate_regulator!(n::computeNeuron)
# n.firingRate NOT running average (average over learning batch)
Δw = n.optimiser.eta * n.c_reg *
(n.firingRate - n.firingRateTarget) * n.eRec
@@ -241,10 +241,10 @@ function firing_rate_regulator!(n::compute_neuron)
return Δw
end
firing_rate!(n::compute_neuron) = n.firingRate = (n.firingCounter / n.timeStep) * 1000
firing_diff!(n::compute_neuron) = n.firingDiff = n.firingRate - n.firingRateTarget
firing_rate!(n::computeNeuron) = n.firingRate = (n.firingCounter / n.timeStep) * 1000
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 * 1.005
end
@@ -276,7 +276,7 @@ end
""" Compute all synaptic connection strength of a neuron. Also mark n.wRec to 0 if wRec goes
below lowerlimit.
"""
function synapticConnStrength!(n::Union{compute_neuron, output_neuron})
function synapticConnStrength!(n::Union{computeNeuron, outputNeuron})
for (i, connStrength) in enumerate(n.synapticStrength)
# check whether connStrength increase or decrease based on usage from n.epsilonRec
updown = n.epsilonRec[i] == 0.0 ? "down" : "up"
@@ -290,9 +290,9 @@ function synapticConnStrength!(n::Union{compute_neuron, output_neuron})
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
zero_weight_index = findall(iszero.(n.wRec))
if length(zero_weight_index) != 0