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building knowledgeFn in GPU format

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This commit is contained in:
ton
2023-07-20 15:51:25 +07:00
parent 2e34679f73
commit 6b71450055
3 changed files with 86 additions and 36 deletions
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38
src/type.jl
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@@ -21,10 +21,14 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
timeStep::AbstractArray = [0]
learningStage::AbstractArray = [0] # 0 inference, 1 start, 2 during, 3 end learning
z_i_t1::Union{AbstractArray, Nothing} = nothing # 2D activation matrix
z_i_t0::Union{AbstractArray, Nothing} = nothing
z_i_t::Union{AbstractArray, Nothing} = nothing # 3D activation matrix
# ---------------------------------------------------------------------------- #
# LIF #
# ---------------------------------------------------------------------------- #
# a projection of kfn.z_i_t into lif dimension for broadcasting later)
lif_z_i_t::Union{AbstractArray, Nothing} = nothing
# LIF
lif_w::Union{AbstractArray, Nothing} = nothing
lif_recSignal::Union{AbstractArray, Nothing} = nothing
lif_vt0::Union{AbstractArray, Nothing} = nothing
@@ -39,7 +43,9 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
lif_delta::AbstractFloat = 1.0
lif_tau_m::AbstractFloat = 20.0
# ALIF
# ---------------------------------------------------------------------------- #
# ALIF #
# ---------------------------------------------------------------------------- #
alif_w::Union{AbstractArray, Nothing} = nothing
alif_recSignal::Union{AbstractArray, Nothing} = nothing
alif_zt0::Union{AbstractArray, Nothing} = nothing
@@ -55,7 +61,8 @@ end
function kfn_1(params::Dict)
kfn = kfn_1()
kfn.params = params
# initialize activation matrix
# ----------------------- initialize activation matrix ----------------------- #
# row*col is a 2D matrix represent all RSNN activation
row, col, batch = kfn.params[:inputPort][:signal][:numbers] # z-axis represent signal batch number
row += kfn.params[:inputPort][:noise][:numbers][1]
col += kfn.params[:inputPort][:signal][:numbers][2]
@@ -63,12 +70,13 @@ function kfn_1(params::Dict)
col += kfn.params[:computeNeuron][:alif][:numbers][2]
# activation matrix
kfn.z_i_t0 = zeros(row, col, batch)
kfn.z_i_t1 = zeros(row, col, batch)
kfn.z_i_t = zeros(row, col, batch)
# LIF
# -------------------------------- LIF config -------------------------------- #
# In 3D LIF matrix, z-axis represent each neuron while each 2D slice represent that neuron's
# synaptic subscription to other neurons (via activation matrix)
z = kfn.params[:computeNeuron][:lif][:numbers][1] * kfn.params[:computeNeuron][:lif][:numbers][2]
kfn.lif_w = zeros(row, col, z) # matrix z-axis represent each neurons
kfn.lif_recSignal = zeros(1, 1, z, batch)
kfn.lif_vt0 = zeros(1, 1, z, batch)
kfn.lif_vt1 = zeros(1, 1, z, batch)
@@ -81,19 +89,23 @@ function kfn_1(params::Dict)
kfn.lif_alpha = ones(1, 1, z, batch) .* (exp(-kfn.lif_delta / kfn.lif_tau_m))
# subscription
row, col, _ = size(kfn.lif_w) # row*col is synaptic subscribe weight for each neuron in z-axis
w = zeros(row, col, z)
synapticConnectionPercent = kfn.params[:computeNeuron][:lif][:params][:synapticConnectionPercent]
synapticConnection = Int(floor(row*col * synapticConnectionPercent/100))
for slice in eachslice(kfn.lif_w, dims=3)
for slice in eachslice(w, dims=3)
pool = shuffle!([1:row*col...])[1:synapticConnection]
for i in pool
slice[i] = randn()/10
slice[i] = randn()/10 # assign weight to synaptic connection
end
end
#WORKING project 3D w into 4D kfn.lif_w
kfn.lif_w = reshape(w, (row, col, z, 1)) .* ones(row, col, z, batch)
println(">>> lif_w ", size(kfn.lif_w))
error("end WORKING")
# ALIF
z = kfn.params[:computeNeuron][:alif][:numbers][1] * kfn.params[:computeNeuron][:alif][:numbers][2]
kfn.alif_w = zeros(row, col, z)
kfn.alif_w = zeros(row, col, z) # matrix z-axis represent each neurons
kfn.alif_recSignal = zeros(1, 1, z, batch)
kfn.alif_zt0 = zeros(1, 1, z, batch)
kfn.alif_zt1 = zeros(1, 1, z, batch)