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"ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "GPUArrays", "GPUArraysCore", "IRTools", "InteractiveUtils", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NaNMath", "PrecompileTools", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"] +git-tree-sha1 = "5be3ddb88fc992a7d8ea96c3f10a49a7e98ebc7b" +uuid = "e88e6eb3-aa80-5325-afca-941959d7151f" +version = "0.6.62" + + [deps.Zygote.extensions] + ZygoteColorsExt = "Colors" + ZygoteDistancesExt = "Distances" + ZygoteTrackerExt = "Tracker" + + [deps.Zygote.weakdeps] + Colors = "5ae59095-9a9b-59fe-a467-6f913c188581" + Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7" + Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" + +[[deps.ZygoteRules]] +deps = ["ChainRulesCore", "MacroTools"] +git-tree-sha1 = "977aed5d006b840e2e40c0b48984f7463109046d" +uuid = "700de1a5-db45-46bc-99cf-38207098b444" +version = "0.2.3" + +[[deps.cuDNN]] +deps = ["CEnum", "CUDA", "CUDNN_jll"] +git-tree-sha1 = "f65490d187861d6222cb38bcbbff3fd949a7ec3e" +uuid = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" +version = "1.0.4" + +[[deps.libblastrampoline_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" +version = "5.8.0+0" + +[[deps.micromamba_jll]] +deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl"] +git-tree-sha1 = "66d07957bcf7e4930d933195aed484078dd8cbb5" +uuid = "f8abcde7-e9b7-5caa-b8af-a437887ae8e4" +version = "1.4.9+0" + +[[deps.nghttp2_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" +version = "1.48.0+0" + +[[deps.p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" +version = "17.4.0+0" diff --git a/previousVersion/0.0.8.1/Project.toml b/previousVersion/0.0.8.1/Project.toml new file mode 100644 index 0000000..4dfcab3 --- /dev/null +++ b/previousVersion/0.0.8.1/Project.toml @@ -0,0 +1,16 @@ +name = "IronpenGPU" +uuid = "3d5396ea-818e-43fc-a9d3-164248e840cd" +authors = ["ton "] +version = "0.1.0" + +[deps] +CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" +CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab" +Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" +Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" +GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe" +JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" diff --git a/previousVersion/0.0.8.1/src/IronpenGPU.jl b/previousVersion/0.0.8.1/src/IronpenGPU.jl new file mode 100644 index 0000000..5f8e359 --- /dev/null +++ b/previousVersion/0.0.8.1/src/IronpenGPU.jl @@ -0,0 +1,91 @@ +module IronpenGPU # this is a parent module + +# export + + +""" Order by dependencies of each file. The 1st included file must not depend on any other +files and each file can only depend on the file included before it. +""" + +include("type.jl") +using .type # bring type into parent module namespace + +include("snnUtil.jl") +using .snnUtil + +include("forward.jl") +using .forward + +include("learn.jl") +using .learn + +include("interface.jl") +using .interface + + +#------------------------------------------------------------------------------------------------100 + +""" version 0.0.9 + Todo: + [1] +W 90% of most active conn + [2] -W 10% of less active conn + + [-] add temporal summation in addition to already used spatial summation. + CANCELLED, spatial summation every second until membrane potential reach a threshold + is in itself a temporal summation. + [4] implement dormant connection and pruning machanism. the longer the training the longer + 0 weight stay 0. + [] using RL to control learning signal + [] consider using Dates.now() instead of timestamp because time_stamp may overflow + [] Liquid time constant. training should include adjusting α, neuron membrane potential decay factor + which defined by neuron.tau_m formula in type.jl + + Change from version: 0.0.6 + - + + All features + - excitatory/inhabitory matrix + - neuroplasticity + - voltage regulator +""" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +end # module IronpenGPU diff --git a/previousVersion/0.0.8.1/src/forward.jl b/previousVersion/0.0.8.1/src/forward.jl new file mode 100644 index 0000000..d3023cb --- /dev/null +++ b/previousVersion/0.0.8.1/src/forward.jl @@ -0,0 +1,912 @@ +module forward + +# export + +using Flux, CUDA +using GeneralUtils +using ..type, ..snnUtil + +#------------------------------------------------------------------------------------------------100 + +""" kfn forward + input (row, col, batch) +""" +function (kfn::kfn_1)(input::AbstractArray) + + kfn.timeStep .+= 1 + + # what to do at the start of learning round + if view(kfn.learningStage, 1)[1] == 1 + # reset learning params + kfn.zitCumulative .= 0 + + kfn.lif_vt .= 0 + kfn.lif_wRecChange .= 0 + kfn.lif_epsilonRec .= 0 + kfn.lif_firingCounter .= 0 + kfn.lif_refractoryCounter .= 0 + kfn.lif_zt .= 0 + kfn.lif_synapticActivityCounter .= 0 + + kfn.alif_vt .= 0 + kfn.alif_a .= 0 + kfn.alif_epsilonRec .= 0 + kfn.alif_epsilonRecA .= 0 + kfn.alif_wRecChange .= 0 + kfn.alif_firingCounter .= 0 + kfn.alif_refractoryCounter .= 0 + kfn.alif_zt .= 0 + kfn.alif_synapticActivityCounter .= 0 + + kfn.on_vt .= 0 + kfn.on_epsilonRec .= 0 + kfn.on_wOutChange .= 0 + kfn.on_refractoryCounter .= 0 + + kfn.learningStage = [2] + end + + # update activation matrix with "lif_zt1" and "alif_zt1" by concatenating + # (input, lif_zt1, alif_zt1) to form activation matrix + _zit = cat(reshape(input, (size(input, 1), size(input, 2), 1, size(input, 3))), + reshape(kfn.lif_zt, (size(input, 1), :, 1, size(input, 3))), + reshape(kfn.alif_zt, (size(input, 1), :, 1, size(input, 3))), dims=2) + kfn.zit .= reshape(_zit, (size(input, 1), :, size(input, 3))) + + @sync begin + @async begin + # project 3D kfn zit into 4D lif zit + i1, i2, i3, i4 = size(kfn.lif_zit) + kfn.lif_zit .= reshape(kfn.zit, (i1, i2, 1, i4)) .* kfn.lif_arrayProjection4d + kfn.lif_exInType .= kfn.exInType .* kfn.lif_arrayProjection4d + + lifForward( kfn.lif_zit, + kfn.lif_wRec, + kfn.lif_vt, + kfn.lif_vth, + kfn.lif_vRest, + kfn.lif_zt4d, + kfn.lif_alpha, + kfn.lif_phi, + kfn.lif_epsilonRec, + kfn.lif_refractoryCounter, + kfn.lif_refractoryDuration, + kfn.lif_gammaPd, + kfn.lif_firingCounter, + kfn.lif_recSignal, + kfn.lif_exInType, + kfn.lif_wRecChange, + kfn.lif_neuronInactivityCounter, + kfn.lif_synapticActivityCounter, + ) + end + @async begin + # project 3D kfn zit into 4D alif zit + i1, i2, i3, i4 = size(kfn.alif_zit) + kfn.alif_zit .= reshape(kfn.zit, (i1, i2, 1, i4)) .* kfn.alif_arrayProjection4d + kfn.alif_exInType .= kfn.exInType .* kfn.alif_arrayProjection4d + + alifForward(kfn.alif_zit, + kfn.alif_wRec, + kfn.alif_vt, + kfn.alif_vth, + kfn.alif_vRest, + kfn.alif_zt4d, + kfn.alif_alpha, + kfn.alif_phi, + kfn.alif_epsilonRec, + kfn.alif_refractoryCounter, + kfn.alif_refractoryDuration, + kfn.alif_gammaPd, + kfn.alif_firingCounter, + kfn.alif_recSignal, + kfn.alif_exInType, + kfn.alif_wRecChange, + kfn.alif_neuronInactivityCounter, + kfn.alif_synapticActivityCounter, + kfn.alif_epsilonRecA, + kfn.alif_a, + kfn.alif_avth, + kfn.alif_beta, + kfn.alif_rho, + ) + end + end + + # reduce lif_zt4d and alif_zt4d into lif_zt, alif_zt (4d -> 1d) + kfn.lif_zt .= reduce(max, kfn.lif_zt4d, dims=(1,2)) + kfn.alif_zt .= reduce(max, kfn.alif_zt4d, dims=(1,2)) + + # update activation matrix with "lif_zt1" and "alif_zt1" by concatenating + # (input, lif_zt1, alif_zt1) to form activation matrix + _zit = cat(reshape(input, (size(input, 1), size(input, 2), 1, size(input, 3))), + reshape(kfn.lif_zt, (size(input, 1), :, 1, size(input, 3))), + reshape(kfn.alif_zt, (size(input, 1), :, 1, size(input, 3))), dims=2) + kfn.zit .= reshape(_zit, (size(input, 1), :, size(input, 3))) + kfn.zitCumulative .+= kfn.zit + + # project 3D kfn zit into 4D on zit + i1, i2, i3, i4 = size(kfn.on_zit) + kfn.on_zit .= reshape(kfn.zit, (i1, i2, 1, i4)) .* kfn.on_arrayProjection4d + + # read out + onForward( kfn.on_zit, + kfn.on_wOut, + kfn.on_vt, + kfn.on_vth, + kfn.on_vRest, + kfn.on_zt4d, + kfn.on_alpha, + kfn.on_phi, + kfn.on_epsilonRec, + kfn.on_refractoryCounter, + kfn.on_refractoryDuration, + kfn.on_gammaPd, + kfn.on_firingCounter, + kfn.on_recSignal, + ) + # get on_zt4d to on_zt + kfn.on_zt .= reduce(max, kfn.on_zt4d, dims=(1,2)) + logit = reshape(kfn.on_zt, (size(input, 1), :)) + + return logit, + kfn.zit +end + +# gpu launcher +function lifForward( zit::CuArray, + wRec::CuArray, + vt::CuArray, + vth::CuArray, + vRest::CuArray, + zt::CuArray, + alpha::CuArray, + phi::CuArray, + epsilonRec::CuArray, + refractoryCounter::CuArray, + refractoryDuration::CuArray, + gammaPd::CuArray, + firingCounter::CuArray, + recSignal::CuArray, + exInType::CuArray, + wRecChange::CuArray, + neuronInactivityCounter::CuArray, + synapticActivityCounter::CuArray, + ) + + kernel = @cuda launch=false lifForward( zit, + wRec, + vt, + vth, + vRest, + zt, + alpha, + phi, + epsilonRec, + refractoryCounter, + refractoryDuration, + gammaPd, + firingCounter, + recSignal, + exInType, + wRecChange, + neuronInactivityCounter, + synapticActivityCounter, + GeneralUtils.linear_to_cartesian, + ) + config = launch_configuration(kernel.fun) + + + # threads to be launched. Since one can't launch exact thread number the kernel needs, + # one just launch threads more than this kernel needs then use a guard inside the kernel + # to prevent unused threads to access memory. + threads = min(1024, config.threads) # depend on gpu. Most NVIDIA gpu has 1024 threads per block + + # total desired threads to launch to gpu. Usually 1 thread per 1 matrix element + totalThreads = length(wRec) + + blocks = cld(totalThreads, threads) + # println("launching gpu kernel") + CUDA.@sync begin + kernel( zit, + wRec, + vt, + vth, + vRest, + zt, + alpha, + phi, + epsilonRec, + refractoryCounter, + refractoryDuration, + gammaPd, + firingCounter, + recSignal, + exInType, + wRecChange, + neuronInactivityCounter, + synapticActivityCounter, + GeneralUtils.linear_to_cartesian; threads, blocks) + end +end + +# gpu kernel +function lifForward( zit, + wRec, + vt, + vth, + vRest, + zt, + alpha, + phi, + epsilonRec, + refractoryCounter, + refractoryDuration, + gammaPd, + firingCounter, + recSignal, + exInType, + wRecChange, + neuronInactivityCounter, + synapticActivityCounter, + linear_to_cartesian, + ) + i = (blockIdx().x - 1) * blockDim().x + threadIdx().x # gpu threads index + + if i <= length(wRec) + # cartesian index + i1, i2, i3, i4 = linear_to_cartesian(i, size(wRec)) + # @cuprintln("gpu thread $i $i1 $i2 $i3 $i4") + + if refractoryCounter[i1,i2,i3,i4] > 0 # refractory period is active + refractoryCounter[i1,i2,i3,i4] -= 1 + recSignal[i1,i2,i3,i4] = 0 + zt[i1,i2,i3,i4] = 0 + vt[i1,i2,i3,i4] = alpha[i1,i2,i3,i4] * vt[i1,i2,i3,i4] + phi[i1,i2,i3,i4] = 0 + + # compute epsilonRec + epsilonRec[i1,i2,i3,i4] = (alpha[i1,i2,i3,i4] * epsilonRec[i1,i2,i3,i4]) + + else # refractory period is inactive + recSignal[i1,i2,i3,i4] = wRec[i1,i2,i3,i4] * zit[i1,i2,i3,i4] * + exInType[i1,i2,i3,i4] + vt[i1,i2,i3,i4] = (alpha[i1,i2,i3,i4] * vt[i1,i2,i3,i4]) + + sum(@view(recSignal[:,:,i3,i4])) + + # fires if membrane potential exceed threshold + if vt[i1,i2,i3,i4] > vth[i1,i2,i3,i4] + zt[i1,i2,i3,i4] = 1 + refractoryCounter[i1,i2,i3,i4] = refractoryDuration[i1,i2,i3,i4] + firingCounter[i1,i2,i3,i4] += 1 + vt[i1,i2,i3,i4] = vRest[i1,i2,i3,i4] + + # reset counter if neuron fires + neuronInactivityCounter[i1,i2,i3,i4] = 0 + else + zt[i1,i2,i3,i4] = 0 + neuronInactivityCounter[i1,i2,i3,i4] -= 1 + end + + # compute phi, there is a difference from lif formula + phi[i1,i2,i3,i4] = (gammaPd[i1,i2,i3,i4] / vth[i1,i2,i3,i4]) * + max(0, 1 - ((vt[i1,i2,i3,i4] - vth[i1,i2,i3,i4]) / vth[i1,i2,i3,i4])) + + # compute epsilonRec + epsilonRec[i1,i2,i3,i4] = (alpha[i1,i2,i3,i4] * epsilonRec[i1,i2,i3,i4]) + + (zit[i1,i2,i3,i4] * !iszero(wRec[i1,i2,i3,i4])) + # !iszero indicates synaptic subscription + + # count synaptic inactivity + if !iszero(wRec[i1,i2,i3,i4]) # check if this is wRec subscription + if !iszero(zit[i1,i2,i3,i4]) # synapse is active + synapticActivityCounter[i1,i2,i3,i4] += 1 + else # synapse is inactive + synapticActivityCounter[i1,i2,i3,i4] += 0 + end + end + # voltage regulator + wRecChange[i1,i2,i3,i4] = -0.01*0.0001 * (vt[i1,i2,i3,i4] - vth[i1,i2,i3,i4]) * + zit[i1,i2,i3,i4] + end + end + return nothing +end + +# gpu launcher +function alifForward( zit::CuArray, + wRec::CuArray, + vt::CuArray, + vth::CuArray, + vRest::CuArray, + zt::CuArray, + alpha::CuArray, + phi::CuArray, + epsilonRec::CuArray, + refractoryCounter::CuArray, + refractoryDuration::CuArray, + gammaPd::CuArray, + firingCounter::CuArray, + recSignal::CuArray, + exInType::CuArray, + wRecChange::CuArray, + neuronInactivityCounter::CuArray, + synapticActivityCounter::CuArray, + epsilonRecA::CuArray, + a::CuArray, + avth::CuArray, + beta::CuArray, + rho::CuArray, + ) + + kernel = @cuda launch=false alifForward( zit, + wRec, + vt, + vth, + vRest, + zt, + alpha, + phi, + epsilonRec, + refractoryCounter, + refractoryDuration, + gammaPd, + firingCounter, + recSignal, + exInType, + wRecChange, + neuronInactivityCounter, + synapticActivityCounter, + epsilonRecA, + a, + avth, + beta, + rho, + GeneralUtils.linear_to_cartesian, + ) + config = launch_configuration(kernel.fun) + + # threads to be launched. Since one can't launch exact thread number the kernel needs, + # one just launch threads more than this kernel needs then use a guard inside the kernel + # to prevent unused threads to access memory. + threads = min(1024, config.threads) # depend on gpu. Most NVIDIA gpu has 1024 threads per block + + # total desired threads to launch to gpu. Usually 1 thread per 1 matrix element + totalThreads = length(wRec) + + blocks = cld(totalThreads, threads) + # println("launching gpu kernel") + CUDA.@sync begin + kernel( zit, + wRec, + vt, + vth, + vRest, + zt, + alpha, + phi, + epsilonRec, + refractoryCounter, + refractoryDuration, + gammaPd, + firingCounter, + recSignal, + exInType, + wRecChange, + neuronInactivityCounter, + synapticActivityCounter, + epsilonRecA, + a, + avth, + beta, + rho, + GeneralUtils.linear_to_cartesian; threads, blocks) + end +end + +# gpu kernel +function alifForward( zit, + wRec, + vt, + vth, + vRest, + zt, + alpha, + phi, + epsilonRec, + refractoryCounter, + refractoryDuration, + gammaPd, + firingCounter, + recSignal, + exInType, + wRecChange, + neuronInactivityCounter, + synapticActivityCounter, + epsilonRecA, + a, + avth, + beta, + rho, + linear_to_cartesian, + ) + i = (blockIdx().x - 1) * blockDim().x + threadIdx().x # gpu threads index + + if i <= length(wRec) + # cartesian index + i1, i2, i3, i4 = linear_to_cartesian(i, size(wRec)) + # @cuprintln("gpu thread $i $i1 $i2 $i3 $i4") + + if refractoryCounter[i1,i2,i3,i4] > 0 # refractory period is active + refractoryCounter[i1,i2,i3,i4] -= 1 + recSignal[i1,i2,i3,i4] = 0 + zt[i1,i2,i3,i4] = 0 + vt[i1,i2,i3,i4] = alpha[i1,i2,i3,i4] * vt[i1,i2,i3,i4] + phi[i1,i2,i3,i4] = 0 + a[i1,i2,i3,i4] = rho[i1,i2,i3,i4] * a[i1,i2,i3,i4] + + # compute epsilonRec + epsilonRec[i1,i2,i3,i4] = (alpha[i1,i2,i3,i4] * epsilonRec[i1,i2,i3,i4]) + + # compute epsilonRecA use eq.26 + epsilonRecA[i1,i2,i3,i4] = (rho[i1,i2,i3,i4] * + (phi[i1,i2,i3,i4] * epsilonRec[i1,i2,i3,i4])) + + # compute avth + avth[i1,i2,i3,i4] = vth[i1,i2,i3,i4] + (beta[i1,i2,i3,i4] * a[i1,i2,i3,i4]) + + else # refractory period is inactive + recSignal[i1,i2,i3,i4] = wRec[i1,i2,i3,i4] * zit[i1,i2,i3,i4] * + exInType[i1,i2,i3,i4] + vt[i1,i2,i3,i4] = (alpha[i1,i2,i3,i4] * vt[i1,i2,i3,i4]) + + sum(@view(recSignal[:,:,i3,i4])) + + # compute avth + avth[i1,i2,i3,i4] = vth[i1,i2,i3,i4] + (beta[i1,i2,i3,i4] * a[i1,i2,i3,i4]) + + # fires if membrane potential exceed threshold + if vt[i1,i2,i3,i4] > avth[i1,i2,i3,i4] + zt[i1,i2,i3,i4] = 1 + refractoryCounter[i1,i2,i3,i4] = refractoryDuration[i1,i2,i3,i4] + firingCounter[i1,i2,i3,i4] += 1 + vt[i1,i2,i3,i4] = vRest[i1,i2,i3,i4] + a[i1,i2,i3,i4] = (rho[i1,i2,i3,i4] * a[i1,i2,i3,i4]) + 1 + neuronInactivityCounter[i1,i2,i3,i4] = 0 + else + zt[i1,i2,i3,i4] = 0 + a[i1,i2,i3,i4] = (rho[i1,i2,i3,i4] * a[i1,i2,i3,i4]) + neuronInactivityCounter[i1,i2,i3,i4] -= 1 + end + + # compute phi, there is a difference from alif formula + phi[i1,i2,i3,i4] = (gammaPd[i1,i2,i3,i4] / vth[i1,i2,i3,i4]) * + max(0, 1 - ((vt[i1,i2,i3,i4] - vth[i1,i2,i3,i4]) / vth[i1,i2,i3,i4])) + + # compute epsilonRec + epsilonRec[i1,i2,i3,i4] = (alpha[i1,i2,i3,i4] * epsilonRec[i1,i2,i3,i4]) + + (zit[i1,i2,i3,i4] * !iszero(wRec[i1,i2,i3,i4])) + # compute epsilonRecA use eq.26 + epsilonRecA[i1,i2,i3,i4] = (rho[i1,i2,i3,i4] * + (phi[i1,i2,i3,i4] * epsilonRec[i1,i2,i3,i4])) + + (zit[i1,i2,i3,i4] * !iszero(wRec[i1,i2,i3,i4])) + + # count synaptic inactivity + if !iszero(wRec[i1,i2,i3,i4]) # check if this is wRec subscription + if !iszero(zit[i1,i2,i3,i4]) # synapse is active + synapticActivityCounter[i1,i2,i3,i4] += 1 + else # synapse is inactive + synapticActivityCounter[i1,i2,i3,i4] += 0 + end + end + # voltage regulator + wRecChange[i1,i2,i3,i4] = -0.01*0.0001 * (vt[i1,i2,i3,i4] - avth[i1,i2,i3,i4]) * + zit[i1,i2,i3,i4] + end + end + return nothing +end + +# gpu launcher +function onForward( zit::CuArray, + wOut::CuArray, + vt::CuArray, + vth::CuArray, + vRest::CuArray, + zt::CuArray, + alpha::CuArray, + phi::CuArray, + epsilonRec::CuArray, + refractoryCounter::CuArray, + refractoryDuration::CuArray, + gammaPd::CuArray, + firingCounter::CuArray, + recSignal::CuArray, + ) + + kernel = @cuda launch=false onForward( zit, + wOut, + vt, + vth, + vRest, + zt, + alpha, + phi, + epsilonRec, + refractoryCounter, + refractoryDuration, + gammaPd, + firingCounter, + recSignal, + GeneralUtils.linear_to_cartesian, + ) + config = launch_configuration(kernel.fun) + + # threads to be launched. Since one can't launch exact thread number the kernel needs, + # one just launch threads more than this kernel needs then use a guard inside the kernel + # to prevent unused threads to access memory. + threads = min(1024, config.threads) # depend on gpu. Most NVIDIA gpu has 1024 threads per block + + # total desired threads to launch to gpu. Usually 1 thread per 1 matrix element + totalThreads = length(wOut) + + blocks = cld(totalThreads, threads) + # println("launching gpu kernel") + CUDA.@sync begin + kernel( zit, + wOut, + vt, + vth, + vRest, + zt, + alpha, + phi, + epsilonRec, + refractoryCounter, + refractoryDuration, + gammaPd, + firingCounter, + recSignal, + GeneralUtils.linear_to_cartesian; threads, blocks) + end +end + +# gpu kernel +function onForward( zit, + wOut, + vt, + vth, + vRest, + zt, + alpha, + phi, + epsilonRec, + refractoryCounter, + refractoryDuration, + gammaPd, + firingCounter, + recSignal, + linear_to_cartesian, + ) + i = (blockIdx().x - 1) * blockDim().x + threadIdx().x # gpu threads index + + if i <= length(wOut) + # cartesian index + i1, i2, i3, i4 = linear_to_cartesian(i, size(wOut)) + # @cuprintln("gpu thread $i $i1 $i2 $i3 $i4") + + if refractoryCounter[i1,i2,i3,i4] > 0 # refractory period is active + refractoryCounter[i1,i2,i3,i4] -= 1 + recSignal[i1,i2,i3,i4] = 0 + zt[i1,i2,i3,i4] = 0 + vt[i1,i2,i3,i4] = alpha[i1,i2,i3,i4] * vt[i1,i2,i3,i4] + phi[i1,i2,i3,i4] = 0 + + # compute epsilonRec + epsilonRec[i1,i2,i3,i4] = (alpha[i1,i2,i3,i4] * epsilonRec[i1,i2,i3,i4]) + + else # refractory period is inactive + recSignal[i1,i2,i3,i4] = zit[i1,i2,i3,i4] * wOut[i1,i2,i3,i4] + vt[i1,i2,i3,i4] = (alpha[i1,i2,i3,i4] * vt[i1,i2,i3,i4]) + sum(@view(recSignal[:,:,i3,i4])) + + # fires if membrane potential exceed threshold + if vt[i1,i2,i3,i4] > vth[i1,i2,i3,i4] + zt[i1,i2,i3,i4] = 1 + refractoryCounter[i1,i2,i3,i4] = refractoryDuration[i1,i2,i3,i4] + firingCounter[i1,i2,i3,i4] += 1 + vt[i1,i2,i3,i4] = vRest[i1,i2,i3,i4] + else + zt[i1,i2,i3,i4] = 0 + end + + # compute phi, there is a difference from on formula + phi[i1,i2,i3,i4] = (gammaPd[i1,i2,i3,i4] / vth[i1,i2,i3,i4]) * + max(0, 1 - ((vt[i1,i2,i3,i4] - vth[i1,i2,i3,i4]) / vth[i1,i2,i3,i4])) + + # compute epsilonRec + epsilonRec[i1,i2,i3,i4] = (alpha[i1,i2,i3,i4] * epsilonRec[i1,i2,i3,i4]) + + (zit[i1,i2,i3,i4] * !iszero(wOut[i1,i2,i3,i4])) + end + end + return nothing +end + +# function lifForward(kfn_zit::Array{T}, +# zit::Array{T}, +# wRec::Array{T}, +# vt0::Array{T}, +# vt1::Array{T}, +# vth::Array{T}, +# vRest::Array{T}, +# zt1::Array{T}, +# alpha::Array{T}, +# phi::Array{T}, +# epsilonRec::Array{T}, +# refractoryCounter::Array{T}, +# refractoryDuration::Array{T}, +# gammaPd::Array{T}, +# firingCounter::Array{T}, +# arrayProjection4d::Array{T}, +# recSignal::Array{T}, +# decayed_vt0::Array{T}, +# decayed_epsilonRec::Array{T}, +# vt1_diff_vth::Array{T}, +# vt1_diff_vth_div_vth::Array{T}, +# gammaPd_div_vth::Array{T}, +# phiActivation::Array{T}, +# ) where T<:Number + +# # project 3D kfn zit into 4D lif zit +# i1, i2, i3, i4 = size(alif_wRec) +# lif_zit .= reshape(kfn_zit, (i1, i2, 1, i4)) .* lif_arrayProjection4d + +# for j in 1:size(wRec, 4), i in 1:size(wRec, 3) # compute along neurons axis of every batch +# if sum(@view(refractoryCounter[:,:,i,j])) > 0 # refractory period is active +# @. @views refractoryCounter[:,:,i,j] -= 1 +# @. @views zt1[:,:,i,j] = 0 +# @. @views vt1[:,:,i,j] = alpha[:,:,i,j] * vt0[:,:,i,j] +# @. @views phi[:,:,i,j] = 0 + +# # compute epsilonRec +# @. @views decayed_epsilonRec[:,:,i,j] = alpha[:,:,i,j] * epsilonRec[:,:,i,j] +# @. @views epsilonRec[:,:,i,j] = decayed_epsilonRec[:,:,i,j] +# else # refractory period is inactive +# @. @views recSignal[:,:,i,j] = zit[:,:,i,j] * wRec[:,:,i,j] +# @. @views decayed_vt0[:,:,i,j] = alpha[:,:,i,j] * vt0[:,:,i,j] +# @view(vt1[:,:,i,j]) .= @view(decayed_vt0[:,:,i,j]) .+ sum(@view(recSignal[:,:,i,j])) + +# if sum(@view(vt1[:,:,i,j])) > sum(@view(vth[:,:,i,j])) +# @. @views zt1[:,:,i,j] = 1 +# @. @views refractoryCounter[:,:,i,j] = refractoryDuration[:,:,i,j] +# @. @views firingCounter[:,:,i,j] += 1 +# @. @views vt1[:,:,i,j] = vRest[:,:,i,j] +# else +# @. @views zt1[:,:,i,j] = 0 +# end + +# # compute phi, there is a difference from alif formula +# @. @views gammaPd_div_vth[:,:,i,j] = gammaPd[:,:,i,j] / vth[:,:,i,j] +# @. @views vt1_diff_vth[:,:,i,j] = vt1[:,:,i,j] - vth[:,:,i,j] +# @. @views vt1_diff_vth_div_vth[:,:,i,j] = vt1_diff_vth[:,:,i,j] / vth[:,:,i,j] +# @view(phiActivation[:,:,i,j]) .= max(0, 1 - sum(@view(vt1_diff_vth_div_vth[:,:,i,j]))) +# @. @views phi[:,:,i,j] = gammaPd_div_vth[:,:,i,j] * phiActivation[:,:,i,j] + +# # compute epsilonRec +# @. @views decayed_epsilonRec[:,:,i,j] = alpha[:,:,i,j] * epsilonRec[:,:,i,j] +# @. @views epsilonRec[:,:,i,j] = decayed_epsilonRec[:,:,i,j] + zit[:,:,i,j] +# end +# end +# end + +# function alifForward(zit::Array{T}, +# wRec::Array{T}, +# vt0::Array{T}, +# vt1::Array{T}, +# vth::Array{T}, +# vRest::Array{T}, +# zt1::Array{T}, +# alpha::Array{T}, +# phi::Array{T}, +# epsilonRec::Array{T}, +# refractoryCounter::Array{T}, +# refractoryDuration::Array{T}, +# gammaPd::Array{T}, +# firingCounter::Array{T}, +# recSignal::Array{T}, +# decayed_vt0::Array{T}, +# decayed_epsilonRec::Array{T}, +# vt1_diff_vth::Array{T}, +# vt1_diff_vth_div_vth::Array{T}, +# gammaPd_div_vth::Array{T}, +# phiActivation::Array{T}, + +# epsilonRecA::Array{T}, +# avth::Array{T}, +# a::Array{T}, +# beta::Array{T}, +# rho::Array{T}, +# phi_x_epsilonRec::Array{T}, +# phi_x_beta::Array{T}, +# rho_diff_phi_x_beta::Array{T}, +# rho_div_phi_x_beta_x_epsilonRecA::Array{T}, +# beta_x_a::Array{T}, +# ) where T<:Number + +# for j in 1:size(wRec, 4), i in 1:size(wRec, 3) # compute along neurons axis of every batch +# if sum(@view(refractoryCounter[:,:,i,j])) > 0 # refractory period is active +# @. @views refractoryCounter[:,:,i,j] -= 1 +# @. @views zt1[:,:,i,j] = 0 +# @. @views vt1[:,:,i,j] = alpha[:,:,i,j] * vt0[:,:,i,j] +# @. @views phi[:,:,i,j] = 0 +# @. @views a[:,:,i,j] = rho[:,:,i,j] * a[:,:,i,j] + +# # compute epsilonRec +# @. @views decayed_epsilonRec[:,:,i,j] = alpha[:,:,i,j] * epsilonRec[:,:,i,j] +# @. @views epsilonRec[:,:,i,j] = decayed_epsilonRec[:,:,i,j] + +# # compute epsilonRecA +# @. @views phi_x_epsilonRec[:,:,i,j] = phi[:,:,i,j] * epsilonRec[:,:,i,j] +# @. @views phi_x_beta[:,:,i,j] = phi[:,:,i,j] * beta[:,:,i,j] +# @. @views rho_diff_phi_x_beta[:,:,i,j] = rho[:,:,i,j] - phi_x_beta[:,:,i,j] +# @. @views rho_div_phi_x_beta_x_epsilonRecA[:,:,i,j] = rho_diff_phi_x_beta[:,:,i,j] * epsilonRecA[:,:,i,j] +# @. @views epsilonRecA[:,:,i,j] = phi_x_epsilonRec[:,:,i,j] + rho_div_phi_x_beta_x_epsilonRecA[:,:,i,j] + +# # compute avth +# @. @views beta_x_a[:,:,i,j] = beta[:,:,i,j] * a[:,:,i,j] +# @. @views avth[:,:,i,j] = vth[:,:,i,j] + beta_x_a[:,:,i,j] + +# else # refractory period is inactive +# @. @views recSignal[:,:,i,j] = zit[:,:,i,j] * wRec[:,:,i,j] +# @. @views decayed_vt0[:,:,i,j] = alpha[:,:,i,j] * vt0[:,:,i,j] +# @view(vt1[:,:,i,j]) .= @view(decayed_vt0[:,:,i,j]) .+ sum(@view(recSignal[:,:,i,j])) + +# # compute avth +# @. @views beta_x_a[:,:,i,j] = beta[:,:,i,j] * a[:,:,i,j] +# @. @views avth[:,:,i,j] = vth[:,:,i,j] + beta_x_a[:,:,i,j] + +# if sum(@view(vt1[:,:,i,j])) > sum(@view(avth[:,:,i,j])) +# @. @views zt1[:,:,i,j] = 1 +# @. @views refractoryCounter[:,:,i,j] = refractoryDuration[:,:,i,j] +# @. @views firingCounter[:,:,i,j] += 1 +# @. @views vt1[:,:,i,j] = vRest[:,:,i,j] +# @. @views a[:,:,i,j] = rho[:,:,i,j] * a[:,:,i,j] +# @. @views a[:,:,i,j] = a[:,:,i,j] += 1 +# else +# @. @views zt1[:,:,i,j] = 0 +# @. @views a[:,:,i,j] = rho[:,:,i,j] * a[:,:,i,j] +# end + +# # compute phi, there is a difference from alif formula +# @. @views gammaPd_div_vth[:,:,i,j] = gammaPd[:,:,i,j] / vth[:,:,i,j] +# @. @views vt1_diff_vth[:,:,i,j] = vt1[:,:,i,j] - vth[:,:,i,j] +# @. @views vt1_diff_vth_div_vth[:,:,i,j] = vt1_diff_vth[:,:,i,j] / vth[:,:,i,j] +# @view(phiActivation[:,:,i,j]) .= max(0, 1 - sum(@view(vt1_diff_vth_div_vth[:,:,i,j]))) +# @. @views phi[:,:,i,j] = gammaPd_div_vth[:,:,i,j] * phiActivation[:,:,i,j] + +# # compute epsilonRec +# @. @views decayed_epsilonRec[:,:,i,j] = alpha[:,:,i,j] * epsilonRec[:,:,i,j] +# @. @views epsilonRec[:,:,i,j] = decayed_epsilonRec[:,:,i,j] + zit[:,:,i,j] + +# # compute epsilonRecA +# @. @views phi_x_epsilonRec[:,:,i,j] = phi[:,:,i,j] * epsilonRec[:,:,i,j] +# @. @views phi_x_beta[:,:,i,j] = phi[:,:,i,j] * beta[:,:,i,j] +# @. @views rho_diff_phi_x_beta[:,:,i,j] = rho[:,:,i,j] - phi_x_beta[:,:,i,j] +# @. @views rho_div_phi_x_beta_x_epsilonRecA[:,:,i,j] = rho_diff_phi_x_beta[:,:,i,j] * epsilonRecA[:,:,i,j] +# @. @views epsilonRecA[:,:,i,j] = phi_x_epsilonRec[:,:,i,j] + rho_div_phi_x_beta_x_epsilonRecA[:,:,i,j] +# end +# end +# end + +# function onForward(kfn_zit::Array{T}, +# zit::Array{T}, +# wOut::Array{T}, +# vt0::Array{T}, +# vt1::Array{T}, +# vth::Array{T}, +# vRest::Array{T}, +# zt1::Array{T}, +# alpha::Array{T}, +# phi::Array{T}, +# epsilonRec::Array{T}, +# refractoryCounter::Array{T}, +# refractoryDuration::Array{T}, +# gammaPd::Array{T}, +# firingCounter::Array{T}, +# arrayProjection4d::Array{T}, +# recSignal::Array{T}, +# decayed_vt0::Array{T}, +# decayed_epsilonRec::Array{T}, +# vt1_diff_vth::Array{T}, +# vt1_diff_vth_div_vth::Array{T}, +# gammaPd_div_vth::Array{T}, +# phiActivation::Array{T}, +# ) where T<:Number + +# # project 3D kfn zit into 4D lif zit +# zit .= reshape(kfn_zit, +# (size(wOut, 1), size(wOut, 2), 1, size(wOut, 4))) .* arrayProjection4d + +# for j in 1:size(wOut, 4), i in 1:size(wOut, 3) # compute along neurons axis of every batch +# if sum(@view(refractoryCounter[:,:,i,j])) > 0 # refractory period is active +# @. @views refractoryCounter[:,:,i,j] -= 1 +# @. @views zt1[:,:,i,j] = 0 +# @. @views vt1[:,:,i,j] = alpha[:,:,i,j] * vt0[:,:,i,j] +# @. @views phi[:,:,i,j] = 0 + +# # compute epsilonRec +# @. @views decayed_epsilonRec[:,:,i,j] = alpha[:,:,i,j] * epsilonRec[:,:,i,j] +# @. @views epsilonRec[:,:,i,j] = decayed_epsilonRec[:,:,i,j] +# else # refractory period is inactive +# @. @views recSignal[:,:,i,j] = zit[:,:,i,j] * wOut[:,:,i,j] +# @. @views decayed_vt0[:,:,i,j] = alpha[:,:,i,j] * vt0[:,:,i,j] +# @view(vt1[:,:,i,j]) .= @view(decayed_vt0[:,:,i,j]) .+ sum(@view(recSignal[:,:,i,j])) + +# if sum(@view(vt1[:,:,i,j])) > sum(@view(vth[:,:,i,j])) +# @. @views zt1[:,:,i,j] = 1 +# @. @views refractoryCounter[:,:,i,j] = refractoryDuration[:,:,i,j] +# @. @views firingCounter[:,:,i,j] += 1 +# @. @views vt1[:,:,i,j] = vRest[:,:,i,j] +# else +# @. @views zt1[:,:,i,j] = 0 +# end + +# # compute phi, there is a difference from alif formula +# @. @views gammaPd_div_vth[:,:,i,j] = gammaPd[:,:,i,j] / vth[:,:,i,j] +# @. @views vt1_diff_vth[:,:,i,j] = vt1[:,:,i,j] - vth[:,:,i,j] +# @. @views vt1_diff_vth_div_vth[:,:,i,j] = vt1_diff_vth[:,:,i,j] / vth[:,:,i,j] +# @view(phiActivation[:,:,i,j]) .= max(0, 1 - sum(@view(vt1_diff_vth_div_vth[:,:,i,j]))) +# @. @views phi[:,:,i,j] = gammaPd_div_vth[:,:,i,j] * phiActivation[:,:,i,j] + +# # compute epsilonRec +# @. @views decayed_epsilonRec[:,:,i,j] = alpha[:,:,i,j] * epsilonRec[:,:,i,j] +# @. @views epsilonRec[:,:,i,j] = decayed_epsilonRec[:,:,i,j] + zit[:,:,i,j] +# end +# end +# end + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +end # module \ No newline at end of file diff --git a/previousVersion/0.0.8.1/src/interface.jl b/previousVersion/0.0.8.1/src/interface.jl new file mode 100644 index 0000000..705220d --- /dev/null +++ b/previousVersion/0.0.8.1/src/interface.jl @@ -0,0 +1,87 @@ +module interface + + +# export + +# using Flux, CUDA + +#------------------------------------------------------------------------------------------------100 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +end # module \ No newline at end of file diff --git a/previousVersion/0.0.8.1/src/learn.jl b/previousVersion/0.0.8.1/src/learn.jl new file mode 100644 index 0000000..5822c9a --- /dev/null +++ b/previousVersion/0.0.8.1/src/learn.jl @@ -0,0 +1,642 @@ +module learn + +export learn!, compute_paramsChange! + +using Statistics, Random, LinearAlgebra, JSON3, Flux, CUDA, Dates +using GeneralUtils +using ..type, ..snnUtil + +#------------------------------------------------------------------------------------------------100 + +function compute_paramsChange!(kfn::kfn_1, modelError, outputError) + + lifComputeParamsChange!(kfn.timeStep, + kfn.lif_phi, + kfn.lif_epsilonRec, + kfn.lif_eta, + kfn.lif_eRec, + kfn.lif_wRec, + kfn.lif_exInType, + kfn.lif_wRecChange, + kfn.on_wOut, + kfn.lif_firingCounter, + kfn.lif_firingTargetFrequency, + kfn.lif_arrayProjection4d, + kfn.lif_error, + modelError, + + kfn.inputSize, + ) + + alifComputeParamsChange!(kfn.timeStep, + kfn.alif_phi, + kfn.alif_epsilonRec, + kfn.alif_eta, + kfn.alif_eRec, + kfn.alif_wRec, + kfn.alif_exInType, + kfn.alif_wRecChange, + kfn.on_wOut, + kfn.alif_firingCounter, + kfn.alif_firingTargetFrequency, + kfn.alif_arrayProjection4d, + kfn.alif_error, + modelError, + + kfn.alif_epsilonRecA, + kfn.alif_beta, + + kfn.inputSize, + ) + + onComputeParamsChange!(kfn.on_phi, + kfn.on_epsilonRec, + kfn.on_eta, + kfn.on_eRec, + kfn.on_wOutChange, + kfn.on_arrayProjection4d, + kfn.on_error, + outputError, + ) + # error("DEBUG -> kfn compute_paramsChange! $(Dates.now())") +end + +function lifComputeParamsChange!( timeStep::CuArray, + phi::CuArray, + epsilonRec::CuArray, + eta::CuArray, + eRec::CuArray, + wRec::CuArray, + exInType::CuArray, + wRecChange::CuArray, + wOut::CuArray, + firingCounter::CuArray, + firingTargetFrequency::CuArray, + arrayProjection4d::CuArray, + nError::CuArray, + modelError, + + inputSize::CuArray + ) + + eRec .= phi .* epsilonRec + + # 2D wRec matrix contain input, lif, alif neurons. I need only lif neurons + startIndex = prod(inputSize) +1 + stopIndex = startIndex + size(wRec, 3) -1 + startCol = CartesianIndices(wRec)[startIndex][2] + stopCol = CartesianIndices(wRec)[stopIndex][2] + + _,_,i3,_ = size(wOut) + for i in 1:i3 + # get output weight associated with lif neurons, use as Bₖⱼ in paper + onW = @view(wOut[:, startCol:stopCol, i, 1]) + nError = onW .* @view(modelError[i]) # error wrt iᵗʰ output neuron + nError = reshape(nError, (1,1,:,1)) # reshape so nError in on 3ʳᵈ dim (neuron axis) + + # compute wRecChange of all neurons wrt to iᵗʰ output neuron + wRecChange .+= (-eta .* nError .* eRec) + end + + # frequency regulator + wRecChange .+= 0.001 .* ((firingTargetFrequency - (firingCounter./timeStep)) ./ timeStep) .* + eta .* eRec + + # reset epsilonRec + epsilonRec .= 0 + + # error("DEBUG lifComputeParamsChange!") + + + # wOutSum_all = reshape( abs.(sum(wOut, dims=3)), (1,1,:, size(wOut, 4)) ) # (1,1,allNeuron,batch) + + # # get only each lif neuron's wOut, leaving out other neuron's wOut + # startIndex = prod(inputSize) +1 + # stopIndex = startIndex + size(wRec, 3) -1 + # wOutSum = @view(wOutSum_all[1,1, startIndex:stopIndex, :]) + # wOutSum = reshape(wOutSum, (1, 1, size(wOutSum, 1), size(wOutSum, 2))) # (1,1,n,batch) + + # # nError a.k.a. learning signal use dopamine concept, + # # this neuron receive summed error signal (modelError) + # nError .= (modelError .* wOutSum) .* arrayProjection4d + # eRec .= phi .* epsilonRec + # wRecChange .+= (-eta .* nError .* eRec) + + # # frequency regulator + # wRecChange .+= 0.001 .* ((firingTargetFrequency - (firingCounter./timeStep)) ./ timeStep) .* + # eta .* eRec + + # if sum(timeStep) == 785 + # epsilonRec_cpu = epsilonRec |> cpu + # println("modelError ", modelError) + # println("") + # wchange = (-eta .* nError .* eRec) |> cpu + # println("wchange 5 1 ", wchange[:,:,5,1]) + # println("") + # println("wchange 5 2 ", wchange[:,:,5,2]) + # println("") + # println("epsilonRec 5 1 ", epsilonRec_cpu[:,:,5,1]) + # println("") + # println("epsilonRec 5 2 ", epsilonRec_cpu[:,:,5,2]) + # println("") + # error("DEBUG lifComputeParamsChange!") + # end +end + +# function alifComputeParamsChange!( timeStep::CuArray, +# phi::CuArray, +# epsilonRec::CuArray, +# eta::CuArray, +# eRec::CuArray, +# wRec::CuArray, +# exInType::CuArray, +# wRecChange::CuArray, +# wOut::CuArray, +# firingCounter::CuArray, +# firingTargetFrequency::CuArray, +# arrayProjection4d::CuArray, +# nError::CuArray, +# modelError::CuArray, + +# epsilonRecA::CuArray, +# beta::CuArray +# ) + +# # Bₖⱼ in paper, sum() to get each neuron's total wOut weight, +# # use absolute because only magnitude is needed +# wOutSum_all = reshape( abs.(sum(wOut, dims=3)), (1,1,:, size(wOut, 4)) ) # (1,1,allNeuron,batch) + +# # get only each lif neuron's wOut, leaving out other neuron's wOut +# wOutSum = @view(wOutSum_all[1,1, end-size(wRec, 3)+1:end, :]) +# wOutSum = reshape(wOutSum, (1, 1, size(wOutSum, 1), size(wOutSum, 2))) # (1,1,n,batch) + +# # nError a.k.a. learning signal use dopamine concept, +# # this neuron receive summed error signal (modelError) +# nError .= (modelError .* wOutSum) .* arrayProjection4d +# eRec .= phi .* (epsilonRec .- (beta .* epsilonRecA)) # use eq. 25 +# wRecChange .+= (-eta .* nError .* eRec) + +# # frequency regulator +# wRecChange .+= 0.001 .* ((firingTargetFrequency - (firingCounter./timeStep)) ./ timeStep) .* +# eta .* eRec + +# # reset epsilonRec +# epsilonRec .= 0 +# epsilonRecA .= 0 + +# # error("DEBUG -> alifComputeParamsChange! $(Dates.now())") +# end + +function alifComputeParamsChange!( timeStep::CuArray, + phi::CuArray, + epsilonRec::CuArray, + eta::CuArray, + eRec::CuArray, + wRec::CuArray, + exInType::CuArray, + wRecChange::CuArray, + wOut::CuArray, + firingCounter::CuArray, + firingTargetFrequency::CuArray, + arrayProjection4d::CuArray, + nError::CuArray, + modelError, + + epsilonRecA::CuArray, + beta::CuArray, + + inputSize::CuArray + ) + + eRec .= phi .* epsilonRec + + # get only each lif neuron's wOut, leaving out other neuron's wOut + startIndex = prod(inputSize) +1 + stopIndex = startIndex + size(wRec, 3) -1 + + # 2D wRec matrix contain input, lif, alif neurons. I need only lif neurons + startCol = CartesianIndices(wRec)[startIndex][2] + stopCol = CartesianIndices(wRec)[stopIndex][2] + + _,_,i3,_ = size(wOut) + for i in 1:i3 + # get output weight associated with lif neurons, use as Bₖⱼ in paper + onW = @view(wOut[:, startCol:stopCol, i, 1]) + nError = onW .* @view(modelError[i]) # error wrt iᵗʰ output neuron + nError = reshape(nError, (1,1,:,1)) # reshape so nError in on 3ʳᵈ dim (neuron axis) + + # compute wRecChange of all neurons wrt to iᵗʰ output neuron + wRecChange .+= (-eta .* nError .* eRec) + end + + # frequency regulator + wRecChange .+= 0.001 .* ((firingTargetFrequency - (firingCounter./timeStep)) ./ timeStep) .* + eta .* eRec + + # reset epsilonRec + epsilonRec .= 0 + epsilonRecA .= 0 + + # error("DEBUG -> alifComputeParamsChange! $(Dates.now())") +end + +function onComputeParamsChange!(phi::CuArray, + epsilonRec::CuArray, + eta::CuArray, + eRec::CuArray, + wOutChange::CuArray, + arrayProjection4d::CuArray, + nError::CuArray, + outputError::CuArray # outputError is output neuron's error + ) + + eRec .= phi .* epsilonRec + nError .= reshape(outputError, (1, 1, :, size(outputError, 2))) .* arrayProjection4d + wOutChange .+= (-eta .* nError .* eRec) + + # reset epsilonRec + epsilonRec .= 0 + + # error("DEBUG -> onComputeParamsChange! $(Dates.now())") +end + +function lifComputeParamsChange!( phi::AbstractArray, + epsilonRec::AbstractArray, + eta::AbstractArray, + wRec::AbstractArray, + wRecChange::AbstractArray, + wOut::AbstractArray, + modelError::AbstractArray) + d1, d2, d3, d4 = size(epsilonRec) + # Bₖⱼ in paper, sum() to get each neuron's total wOut weight + wOutSum = reshape(sum(wOut, dims=3), (d1, :, d4)) + + for j in 1:d4, i in 1:d3 # compute along neurons axis of every batch + # how much error of this neuron 1-spike causing each output neuron's error + + view(wRecChange, :, :, i, j) .+= (-1 * view(eta, :, :, i, j)[1]) .* + # eRec + ( + (view(phi, :, :, i, j)[1] .* view(epsilonRec, :, :, i, j)) .* + # nError a.k.a. learning signal + ( + view(modelError, :, j)[1] * # dopamine concept, this neuron receive summed error signal + # RSNN neuron's total wOut weight (neuron synaptic subscription .* wOutSum) + view(wOutSum, :, :, j)[i] + ) + ) + end +end + +function alifComputeParamsChange!( phi::AbstractArray, + epsilonRec::AbstractArray, + epsilonRecA::AbstractArray, + eta::AbstractArray, + wRec::AbstractArray, + wRecChange::AbstractArray, + beta::AbstractArray, + wOut::AbstractArray, + modelError::AbstractArray) + d1, d2, d3, d4 = size(epsilonRec) + + # Bₖⱼ in paper, sum() to get each neuron's total wOut weight + wOutSum = reshape(sum(wOut, dims=3), (d1, :, d4)) + + for j in 1:d4, i in 1:d3 # compute along neurons axis of every batch + # how much error of this neuron 1-spike causing each output neuron's error + + view(wRecChange, :, :, i, j) .+= (-1 * view(eta, :, :, i, j)[1]) .* + # eRec + ( + # eRec_v + (view(phi, :, :, i, j)[1] .* view(epsilonRec, :, :, i, j)) .+ + # eRec_a + ((view(phi, :, :, i, j)[1] * view(beta, :, :, i, j)[1]) .* + view(epsilonRecA, :, :, i, j)) + ) .* + # nError a.k.a. learning signal + ( + view(modelError, :, j)[1] * + # RSNN neuron's total wOut weight (neuron synaptic subscription .* wOutSum) + view(wOutSum, :, :, j)[i] + # sum(GeneralUtils.isNotEqual.(view(wRec, :, :, i, j), 0) .* + # view(wOutSum, :, :, j)) + ) + end +end + +function onComputeParamsChange!(phi::AbstractArray, + epsilonRec::AbstractArray, + eta::AbstractArray, + wOutChange::AbstractArray, + outputError::AbstractArray) + d1, d2, d3, d4 = size(epsilonRec) + + for j in 1:d4, i in 1:d3 # compute along neurons axis of every batch + # how much error of this neuron 1-spike causing each output neuron's error + + view(wOutChange, :, :, i, j) .+= (-1 * view(eta, :, :, i, j)[1]) .* + # eRec + ( + (view(phi, :, :, i, j)[1] .* view(epsilonRec, :, :, i, j)) .* + # nError a.k.a. learning signal, output neuron receives error of its own answer - correct answer. + view(outputError, :, j)[i] + ) + end +end + +function learn!(kfn::kfn_1, device=cpu) + # lif learn + kfn.lif_wRec, kfn.lif_neuronInactivityCounter, kfn.lif_synapticActivityCounter = + lifLearn(kfn.lif_wRec, + kfn.lif_exInType, + kfn.lif_wRecChange, + kfn.lif_arrayProjection4d, + kfn.lif_neuronInactivityCounter, + kfn.lif_synapticActivityCounter, + kfn.lif_synapseConnectionNumber, + kfn.lif_synapticWChangeCounter, + kfn.lif_eta, + kfn.zitCumulative, + device) + + # alif learn + kfn.alif_wRec, kfn.alif_neuronInactivityCounter, kfn.alif_synapticActivityCounter = + alifLearn(kfn.alif_wRec, + kfn.alif_exInType, + kfn.alif_wRecChange, + kfn.alif_arrayProjection4d, + kfn.alif_neuronInactivityCounter, + kfn.alif_synapticActivityCounter, + kfn.alif_synapseConnectionNumber, + kfn.alif_synapticWChangeCounter, + kfn.zitCumulative, + device) + + # on learn + onLearn!(kfn.on_wOut, + kfn.on_wOutChange, + kfn.on_arrayProjection4d) + + # wrap up learning session + if kfn.learningStage == [3] + kfn.learningStage = [0] + end + # error("DEBUG -> kfn learn! $(Dates.now())") +end + +function lifLearn(wRec, + exInType, + wRecChange, + arrayProjection4d, + neuronInactivityCounter, + synapticActivityCounter, + synapseConnectionNumber, + synapticWChangeCounter, #TODO + eta, + zitCumulative, + device) + + # merge learning weight with average learning weight of all batch + wch = sum(wRecChange, dims=4) ./ (size(wRec, 4)) .* arrayProjection4d + wRec .= (exInType .* wRec) .+ wch + + arrayProjection4d_cpu = arrayProjection4d |> cpu + wRec_cpu = wRec |> cpu + wRec_cpu = wRec_cpu[:,:,:,1] # since every batch has the same neuron wRec, (row, col, n) + eta_cpu = eta |> cpu + eta_cpu = eta_cpu[:,:,:,1] + neuronInactivityCounter_cpu = neuronInactivityCounter |> cpu + neuronInactivityCounter_cpu = neuronInactivityCounter_cpu[:,:,:,1] # (row, col, n) + synapticActivityCounter_cpu = synapticActivityCounter |> cpu + synapticActivityCounter_cpu = synapticActivityCounter_cpu[:,:,:,1] + zitCumulative_cpu = zitCumulative |> cpu + zitCumulative_cpu = zitCumulative_cpu[:,:,1] # (row, col) + + # # -W if less than 10% of repeat avg, +W otherwise + # _, _, i3 = size(wRec_cpu) + # for i in 1:i3 + # x = 0.1 * (sum(synapticActivityCounter[:,:,i]) / length(synapticActivityCounter[:,:,i])) + # mask = GeneralUtils.replaceLessThan.(wRec_cpu[:,:,i], x, -1, 1) + # wRec_cpu[:,:,i] .+= mask .* eta_cpu[:,:,i] .* wRec_cpu[:,:,i] + # end + + # weak / negative synaptic connection will get randomed in neuroplasticity() + wRec_cpu = GeneralUtils.replaceBetween.(wRec_cpu, 0.0, 0.01, -1.0) # mark with -1.0 + + # # synaptic connection that has no activity will get randomed in neuroplasticity() + # mask = isless.(synapticActivityCounter_cpu, -100000) + # GeneralUtils.replaceElements!(mask, 1, wRec_cpu, -1.0) + # # reset lif_inactivity elements to base value + # GeneralUtils.replaceElements!(mask, 1, synapticActivityCounter_cpu, 0.0) + + # neuroplasticity, work on CPU side + wRec_cpu = neuroplasticity(synapseConnectionNumber, + zitCumulative_cpu, + wRec_cpu, + neuronInactivityCounter_cpu, + synapticActivityCounter_cpu) + + wRec_cpu = wRec_cpu .* arrayProjection4d_cpu + wRec = wRec_cpu |> device + + neuronInactivityCounter_cpu = neuronInactivityCounter_cpu .* arrayProjection4d_cpu + neuronInactivityCounter = neuronInactivityCounter_cpu |> device + + synapticActivityCounter_cpu = synapticActivityCounter_cpu .* arrayProjection4d_cpu + synapticActivityCounter = synapticActivityCounter_cpu |> device + + + return wRec, neuronInactivityCounter, synapticActivityCounter +end + +function alifLearn(wRec, + exInType, + wRecChange, + arrayProjection4d, + neuronInactivityCounter, + synapticActivityCounter, + synapseConnectionNumber, + synapticWChangeCounter, #TODO + zitCumulative, + device) + + # merge learning weight with average learning weight of all batch + wch = sum(wRecChange, dims=4) ./ (size(wRec, 4)) .* arrayProjection4d + wRec .= (exInType .* wRec) .+ wch + + arrayProjection4d_cpu = arrayProjection4d |> cpu + wRec_cpu = wRec |> cpu + wRec_cpu = wRec_cpu[:,:,:,1] # since every batch has the same neuron wRec, (row, col, n) + neuronInactivityCounter_cpu = neuronInactivityCounter |> cpu + neuronInactivityCounter_cpu = neuronInactivityCounter_cpu[:,:,:,1] # (row, col, n) + synapticActivityCounter_cpu = synapticActivityCounter |> cpu + synapticActivityCounter_cpu = synapticActivityCounter_cpu[:,:,:,1] + zitCumulative_cpu = zitCumulative |> cpu + zitCumulative_cpu = zitCumulative_cpu[:,:,1] # (row, col) + + # weak / negative synaptic connection will get randomed in neuroplasticity() + wRec_cpu = GeneralUtils.replaceBetween.(wRec_cpu, 0.0, 0.01, -1.0) # mark with -1.0 + + # synaptic connection that has no activity will get randomed in neuroplasticity() + mask = isless.(synapticActivityCounter_cpu, -100000) + GeneralUtils.replaceElements!(mask, 1, wRec_cpu, -1.0) + # reset alif_inactivity elements to base value + GeneralUtils.replaceElements!(mask, 1, synapticActivityCounter_cpu, 0.0) + + # neuroplasticity, work on CPU side + wRec_cpu = neuroplasticity(synapseConnectionNumber, + zitCumulative_cpu, + wRec_cpu, + neuronInactivityCounter_cpu, + synapticActivityCounter_cpu) + + wRec_cpu = wRec_cpu .* arrayProjection4d_cpu + wRec = wRec_cpu |> device + + neuronInactivityCounter_cpu = neuronInactivityCounter_cpu .* arrayProjection4d_cpu + neuronInactivityCounter = neuronInactivityCounter_cpu |> device + + synapticActivityCounter_cpu = synapticActivityCounter_cpu .* arrayProjection4d_cpu + synapticActivityCounter = synapticActivityCounter_cpu |> device + + # error("DEBUG -> alifLearn! $(Dates.now())") + return wRec, neuronInactivityCounter, synapticActivityCounter +end + +function onLearn!(wOut, + wOutChange, + arrayProjection4d) + # merge learning weight with average learning weight + wOut .+= (sum(wOutChange, dims=4) ./ (size(wOut, 4))) .* arrayProjection4d + + # adaptive wOut to help convergence using c_decay + wOut .-= 0.001 .* wOut + + #TODO synaptic strength + + #TODO neuroplasticity + +end + +function neuroplasticity(synapseConnectionNumber, + zitCumulative, # (row, col) + wRec, # (row, col, n) + neuronInactivityCounter, + synapticActivityCounter) # (row, col, n) + + i1,i2,i3 = size(wRec) + + # for each neuron, find total number of synaptic conn that should draw + # new connection to firing and non-firing neurons pool + subToFireNeuron_toBe = Int(floor(0.7 * synapseConnectionNumber)) + + # for each neuron, count how many synap already subscribed to firing-neurons + zw = zitCumulative .* wRec + subToFireNeuron_current = sum(GeneralUtils.isBetween.(zw, 0.0, 100.0), dims=(1,2)) # (1, 1, n) + zitMask = (!iszero).(zitCumulative) # zitMask of firing neurons = 1, non-firing = 0 + projection = ones(i1,i2,i3) + zitMask = zitMask .* projection # (row, col, n) + totalNewConn = sum(isequal.(wRec, -1.0), dims=(1,2)) # count new conn mark (-1.0), (1, 1, n) + if sum(totalNewConn) != 0 + println("neuroplasticity, from $(synapseConnectionNumber*size(totalNewConn, 3)) conn, $(sum(totalNewConn)) are replaced") + end + + # clear -1.0 marker + GeneralUtils.replaceElements!(wRec, -1.0, synapticActivityCounter, -0.99) + GeneralUtils.replaceElements!(wRec, -1.0, 0.0) # -1.0 marker is no longer required + + for i in 1:i3 + if neuronInactivityCounter[1:1:i][1] < -10000 # neuron die i.e. reset all weight + println("neuron die") + neuronInactivityCounter[:,:,i] .= 0 # reset + w = random_wRec(i1,i2,1,synapseConnectionNumber) + wRec[:,:,i] .= w + + a = similar(w) .= -0.99 # synapseConnectionNumber of this neuron + mask = (!iszero).(w) + GeneralUtils.replaceElements!(mask, 1, a, 0) + synapticActivityCounter[:,:,i] = a + else + remaining = 0 + if subToFireNeuron_current[1,1,i] < subToFireNeuron_toBe + toAddConn = subToFireNeuron_toBe - subToFireNeuron_current[1,1,i] + totalNewConn[1,1,i] = totalNewConn[1,1,i] - toAddConn + # add new conn to firing neurons pool + remaining = addNewSynapticConn!(zitMask[:,:,i], 1, + @view(wRec[:,:,i]), + @view(synapticActivityCounter[:,:,i]), + toAddConn) + totalNewConn[1,1,i] += remaining + end + + # add new conn to non-firing neurons pool + remaining = addNewSynapticConn!(zitMask[:,:,i], 0, + @view(wRec[:,:,i]), + @view(synapticActivityCounter[:,:,i]), + totalNewConn[1,1,i]) + if remaining > 0 # final get-all round if somehow non-firing pool has not enough slot + remaining = addNewSynapticConn!(zitMask[:,:,i], 1, + @view(wRec[:,:,i]), + @view(synapticActivityCounter[:,:,i]), + remaining) + end + end + end + + # error("DEBUG -> neuroplasticity $(Dates.now())") + return wRec +end + +# learningLiquidity(x) = -0.0001x + 1 # -10000 to +10000; f(x) = -5e-05x+0.5 + +function learningLiquidity(x) + if x > 10000 + y = 0.0 + elseif x < -10000 + y = 1.0 + else + y = -5e-05x+0.5 # range -10000 to +10000 + end + return y +end + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +end # module \ No newline at end of file diff --git a/previousVersion/0.0.8.1/src/snnUtil.jl b/previousVersion/0.0.8.1/src/snnUtil.jl new file mode 100644 index 0000000..e7bd345 --- /dev/null +++ b/previousVersion/0.0.8.1/src/snnUtil.jl @@ -0,0 +1,118 @@ +module snnUtil + +export refractoryStatus!, addNewSynapticConn! + +using Random + +#------------------------------------------------------------------------------------------------100 + +function refractoryStatus!(refractoryCounter, refractoryActive, refractoryInactive) + d1, d2, d3, d4 = size(refractoryCounter) + for j in 1:d4 + for i in 1:d3 + if refractoryCounter[1, 1, i, j] > 0 # inactive + view(refractoryActive, 1, 1, i, j) .= 0 + view(refractoryInactive, 1, 1, i, j) .= 1 + else # active + view(refractoryActive, 1, 1, i, j) .= 1 + view(refractoryInactive, 1, 1, i, j) .= 0 + end + end + end +end + +# function frobenius_distance(A, B) +# # Check if the matrices have the same size +# if size(A) != size(B) +# error("The matrices must have the same size") +# end +# # Initialize the distance to zero +# distance = 0.0 +# # Loop over the elements of the matrices and add the squared differences +# for i in 1:size(A, 1) +# for j in 1:size(A, 2) +# distance += (A[i, j] - B[i, j])^2 +# end +# end +# # Return the square root of the distance +# return sqrt(distance) +# end + +function addNewSynapticConn!(mask::AbstractArray{<:Any}, x::Number, wRec::AbstractArray{<:Any}, + counter::AbstractArray{<:Any}, n=0; + rng::AbstractRNG=MersenneTwister(1234)) + # println("mask ", mask, size(mask)) + # println("") + # println("x ", x, size(x)) + # println("") + # println("wRec ", wRec, size(wRec)) + # println("") + # println("counter ", counter, size(counter)) + # println("") + # println("n ", n, size(n)) + # println("") + + # check if mask and wRec have the same size + if size(mask) != size(wRec) + error("mask and wRec must have the same size") + end + # get the indices of elements in mask that equal x + indices = findall(x -> x == x, mask) + alreadySub = findall(x -> x != 0, wRec) # get already subscribe + setdiff!(indices, alreadySub) # remove already sub conn from pool + + remaining = 0 + if n == 0 || n > length(indices) + remaining = n - length(indices) + n = length(indices) + end + + # shuffle the indices using the rng function + shuffle!(rng, indices) + # select the first n indices + n > length(indices) ? println(">>> ", total_x_tobeReplced) : nothing + selected = indices[1:n] + # replace the elements in wRec at the selected positions with a + for i in selected + wRec[i] = rand(0.01:0.01:0.1) + counter[i] = 0 # counting start from 0 + end + # error("DEBUG addNewSynapticConn!") + return remaining +end + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +end # module \ No newline at end of file diff --git a/previousVersion/0.0.8.1/src/type.jl b/previousVersion/0.0.8.1/src/type.jl new file mode 100644 index 0000000..e34ab45 --- /dev/null +++ b/previousVersion/0.0.8.1/src/type.jl @@ -0,0 +1,452 @@ +module type + +export + # struct + kfn_1, + + # function + random_wRec + +using Random, GeneralUtils + +#------------------------------------------------------------------------------------------------100 +rng = MersenneTwister(1234) + +abstract type Ironpen end +abstract type knowledgeFn <: Ironpen end + +#------------------------------------------------------------------------------------------------100 + +Base.@kwdef mutable struct kfn_1 <: knowledgeFn + params::Union{Dict, Nothing} = nothing # store params of knowledgeFn itself for later use + + timeStep::Union{AbstractArray, Nothing} = nothing + learningStage::Union{AbstractArray, Nothing} = nothing # 0 inference, 1 start, 2 during, 3 end learning + inputSize::Union{AbstractArray, Nothing} = nothing + zit::Union{AbstractArray, Nothing} = nothing # 3D activation matrix + zitCumulative::Union{AbstractArray, Nothing} = nothing + exInType::Union{AbstractArray, Nothing} = nothing + modelError::Union{AbstractArray, Nothing} = nothing # store RSNN error + outputError::Union{AbstractArray, Nothing} = nothing # store output neurons error + + # ---------------------------------------------------------------------------- # + # LIF Neurons # + # ---------------------------------------------------------------------------- # + # a projection of kfn.zit into lif dimension for broadcasting later) + lif_size::Union{AbstractArray, Nothing} = nothing + lif_zit::Union{AbstractArray, Nothing} = nothing + + # main variables according to papers + lif_wRec::Union{AbstractArray, Nothing} = nothing + lif_vt::Union{AbstractArray, Nothing} = nothing + lif_vth::Union{AbstractArray, Nothing} = nothing + lif_vRest::Union{AbstractArray, Nothing} = nothing + lif_zt::Union{AbstractArray, Nothing} = nothing + lif_zt4d::Union{AbstractArray, Nothing} = nothing + lif_refractoryCounter::Union{AbstractArray, Nothing} = nothing + lif_refractoryDuration::Union{AbstractArray, Nothing} = nothing + lif_alpha::Union{AbstractArray, Nothing} = nothing + lif_delta::Union{AbstractFloat, Nothing} = nothing + lif_tau_m::Union{AbstractFloat, Nothing} = nothing + lif_phi::Union{AbstractArray, Nothing} = nothing + lif_epsilonRec::Union{AbstractArray, Nothing} = nothing + lif_eRec::Union{AbstractArray, Nothing} = nothing + lif_eta::Union{AbstractArray, Nothing} = nothing + lif_gammaPd::Union{AbstractArray, Nothing} = nothing + lif_wRecChange::Union{AbstractArray, Nothing} = nothing + lif_error::Union{AbstractArray, Nothing} = nothing + + lif_firingCounter::Union{AbstractArray, Nothing} = nothing + lif_firingTargetFrequency::Union{AbstractArray, Nothing} = nothing + lif_neuronInactivityCounter::Union{AbstractArray, Nothing} = nothing + lif_synapticActivityCounter::Union{AbstractArray, Nothing} = nothing + lif_synapseConnectionNumber::Union{Int, Nothing} = nothing + lif_synapticWChangeCounter::Union{AbstractArray, Nothing} = nothing + + # pre-allocation array + lif_arrayProjection4d::Union{AbstractArray, Nothing} = nothing # use to project 3d array to 4d + lif_recSignal::Union{AbstractArray, Nothing} = nothing + lif_exInType::Union{AbstractArray, Nothing} = nothing + # lif_decayed_epsilonRec::Union{AbstractArray, Nothing} = nothing + # lif_vt_diff_vth::Union{AbstractArray, Nothing} = nothing + # lif_vt_diff_vth_div_vth::Union{AbstractArray, Nothing} = nothing + # lif_gammaPd_div_vth::Union{AbstractArray, Nothing} = nothing + # lif_phiActivation::Union{AbstractArray, Nothing} = nothing + + # ---------------------------------------------------------------------------- # + # ALIF Neurons # + # ---------------------------------------------------------------------------- # + alif_size::Union{AbstractArray, Nothing} = nothing + alif_zit::Union{AbstractArray, Nothing} = nothing + + alif_wRec::Union{AbstractArray, Nothing} = nothing + alif_vt::Union{AbstractArray, Nothing} = nothing + alif_vth::Union{AbstractArray, Nothing} = nothing + alif_vRest::Union{AbstractArray, Nothing} = nothing + alif_zt::Union{AbstractArray, Nothing} = nothing + alif_zt4d::Union{AbstractArray, Nothing} = nothing + alif_refractoryCounter::Union{AbstractArray, Nothing} = nothing + alif_refractoryDuration::Union{AbstractArray, Nothing} = nothing + alif_alpha::Union{AbstractArray, Nothing} = nothing + alif_delta::Union{AbstractFloat, Nothing} = nothing + alif_tau_m::Union{AbstractFloat, Nothing} = nothing + alif_phi::Union{AbstractArray, Nothing} = nothing + alif_epsilonRec::Union{AbstractArray, Nothing} = nothing + alif_eRec::Union{AbstractArray, Nothing} = nothing + alif_eta::Union{AbstractArray, Nothing} = nothing + alif_gammaPd::Union{AbstractArray, Nothing} = nothing + alif_wRecChange::Union{AbstractArray, Nothing} = nothing + alif_error::Union{AbstractArray, Nothing} = nothing + + alif_firingCounter::Union{AbstractArray, Nothing} = nothing + alif_firingTargetFrequency::Union{AbstractArray, Nothing} = nothing + alif_neuronInactivityCounter::Union{AbstractArray, Nothing} = nothing + alif_synapticActivityCounter::Union{AbstractArray, Nothing} = nothing + alif_synapseConnectionNumber::Union{Int, Nothing} = nothing + alif_synapticWChangeCounter::Union{AbstractArray, Nothing} = nothing + + # pre-allocation array + alif_arrayProjection4d::Union{AbstractArray, Nothing} = nothing # use to project 3d array to 4d + alif_recSignal::Union{AbstractArray, Nothing} = nothing + alif_exInType::Union{AbstractArray, Nothing} = nothing + # alif_decayed_epsilonRec::Union{AbstractArray, Nothing} = nothing + # alif_vt_diff_vth::Union{AbstractArray, Nothing} = nothing + # alif_vt_diff_vth_div_vth::Union{AbstractArray, Nothing} = nothing + # alif_gammaPd_div_vth::Union{AbstractArray, Nothing} = nothing + # alif_phiActivation::Union{AbstractArray, Nothing} = nothing + + # alif specific variables + alif_epsilonRecA::Union{AbstractArray, Nothing} = nothing + alif_avth::Union{AbstractArray, Nothing} = nothing + alif_a::Union{AbstractArray, Nothing} = nothing # threshold adaptation + alif_beta::Union{AbstractArray, Nothing} = nothing # β, constant, value from paper + alif_rho::Union{AbstractArray, Nothing} = nothing # ρ, threshold adaptation decay factor + alif_tau_a::Union{AbstractFloat, Nothing} = nothing # τ_a, adaption time constant in millisecond + + # alif specific pre-allocation array + # alif_phi_x_epsilonRec::Union{AbstractArray, Nothing} = nothing + # alif_phi_x_beta::Union{AbstractArray, Nothing} = nothing + # alif_rho_diff_phi_x_beta::Union{AbstractArray, Nothing} = nothing + # alif_rho_div_phi_x_beta_x_epsilonRecA::Union{AbstractArray, Nothing} = nothing + # alif_beta_x_a::Union{AbstractArray, Nothing} = nothing + + # ---------------------------------------------------------------------------- # + # Output Neurons # + # ---------------------------------------------------------------------------- # + # output neuron is based on LIF + on_zit::Union{AbstractArray, Nothing} = nothing + + # main variables according to papers + on_wOut::Union{AbstractArray, Nothing} = nothing # wOut is wRec, just use the name from paper + on_vt::Union{AbstractArray, Nothing} = nothing + on_vth::Union{AbstractArray, Nothing} = nothing + on_vRest::Union{AbstractArray, Nothing} = nothing + on_zt::Union{AbstractArray, Nothing} = nothing + on_zt4d::Union{AbstractArray, Nothing} = nothing + on_refractoryCounter::Union{AbstractArray, Nothing} = nothing + on_refractoryDuration::Union{AbstractArray, Nothing} = nothing + on_alpha::Union{AbstractArray, Nothing} = nothing + on_delta::Union{AbstractFloat, Nothing} = nothing + on_tau_m::Union{AbstractFloat, Nothing} = nothing + on_phi::Union{AbstractArray, Nothing} = nothing + on_epsilonRec::Union{AbstractArray, Nothing} = nothing + on_eRec::Union{AbstractArray, Nothing} = nothing + on_eta::Union{AbstractArray, Nothing} = nothing + on_gammaPd::Union{AbstractArray, Nothing} = nothing + on_wOutChange::Union{AbstractArray, Nothing} = nothing + on_error::Union{AbstractArray, Nothing} = nothing + on_subscription::Union{AbstractArray, Nothing} = nothing + + on_firingCounter::Union{AbstractArray, Nothing} = nothing + + # pre-allocation array + on_arrayProjection4d::Union{AbstractArray, Nothing} = nothing # use to project 3d array to 4d + on_recSignal::Union{AbstractArray, Nothing} = nothing + # on_decayed_epsilonRec::Union{AbstractArray, Nothing} = nothing + # on_vt_diff_vth::Union{AbstractArray, Nothing} = nothing + # on_vt_diff_vth_div_vth::Union{AbstractArray, Nothing} = nothing + # on_gammaPd_div_vth::Union{AbstractArray, Nothing} = nothing + # on_phiActivation::Union{AbstractArray, Nothing} = nothing +end + +# outer constructor +function kfn_1(params::Dict; device=cpu) + kfn = kfn_1() + kfn.params = params + kfn.timeStep = [0] |> device + kfn.learningStage = [0] |> device + + # ---------------------------------------------------------------------------- # + # initialize activation matrix # + # ---------------------------------------------------------------------------- # + # row*col is a 2D matrix represent all RSNN activation + row, signal_col, batch = kfn.params[:inputPort][:signal][:numbers] # z-axis represent signal batch number + + kfn.inputSize = [row, signal_col] |> device + lif_col = kfn.params[:computeNeuron][:lif][:numbers][2] + alif_col = kfn.params[:computeNeuron][:alif][:numbers][2] + + col = signal_col + lif_col + alif_col + + # activation matrix + kfn.zit = zeros(row, col, batch) |> device + kfn.zitCumulative = (similar(kfn.zit) .= 0) + kfn.modelError = zeros(1) |> device + + # ---------------------------------------------------------------------------- # + # LIF config # + # ---------------------------------------------------------------------------- # + kfn.lif_size = [row, lif_col] |> device + + # 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) + lif_n = kfn.params[:computeNeuron][:lif][:numbers][1] * kfn.params[:computeNeuron][:lif][:numbers][2] + + # subscription + synapticConnectionPercent = kfn.params[:computeNeuron][:lif][:params][:synapticConnectionPercent] + kfn.lif_synapseConnectionNumber = Int(floor(row*col * synapticConnectionPercent/100)) + w = random_wRec(row, col, lif_n, kfn.lif_synapseConnectionNumber) + + # project 3D w into 4D kfn.lif_wRec (row, col, n, batch) + kfn.lif_wRec = reshape(w, (row, col, lif_n, 1)) .* ones(row, col, lif_n, batch) |> device + kfn.lif_zit = (similar(kfn.lif_wRec) .= 0) + kfn.lif_vt = (similar(kfn.lif_wRec) .= 0) + kfn.lif_vth = (similar(kfn.lif_wRec) .= 1) + kfn.lif_vRest = (similar(kfn.lif_wRec) .= 0) + kfn.lif_zt = zeros(1, 1, lif_n, batch) |> device + kfn.lif_zt4d = (similar(kfn.lif_wRec) .= 0) + kfn.lif_refractoryCounter = (similar(kfn.lif_wRec) .= 0) + kfn.lif_refractoryDuration = (similar(kfn.lif_wRec) .= 3) + kfn.lif_delta = 1.0 + kfn.lif_tau_m = 20.0 + kfn.lif_alpha = (similar(kfn.lif_wRec) .= (exp(-kfn.lif_delta / kfn.lif_tau_m))) + kfn.lif_phi = (similar(kfn.lif_wRec) .= 0) + kfn.lif_epsilonRec = (similar(kfn.lif_wRec) .= 0) + kfn.lif_eRec = (similar(kfn.lif_wRec) .= 0) + kfn.lif_eta = (similar(kfn.lif_wRec) .= 0.01) + kfn.lif_gammaPd = (similar(kfn.lif_wRec) .= 0.3) + kfn.lif_wRecChange = (similar(kfn.lif_wRec) .= 0) + kfn.lif_error = (similar(kfn.lif_wRec) .= 0) + + kfn.lif_firingCounter = (similar(kfn.lif_wRec) .= 0) + kfn.lif_firingTargetFrequency = (similar(kfn.lif_wRec) .= 0.1) + kfn.lif_neuronInactivityCounter = (similar(kfn.lif_wRec) .= 0) + + # count subscribed synapse activity, just like epsilonRec but without decay. + # use to adjust weight based on how often neural pathway is used + kfn.lif_synapticActivityCounter = Array(similar(kfn.lif_wRec) .= -0.99) # -0.99 for non-sub conn + mask = Array((!iszero).(kfn.lif_wRec)) + # initial value subscribed conn, synapticActivityCounter range -10000 to +10000 + GeneralUtils.replaceElements!(mask, 1, kfn.lif_synapticActivityCounter, 0) + kfn.lif_synapticActivityCounter = kfn.lif_synapticActivityCounter |> device + + kfn.lif_synapticWChangeCounter = Array(similar(kfn.lif_wRec) .= -0.99) # -0.99 for non-sub conn + mask = Array((!iszero).(kfn.lif_wRec)) + # initial value subscribed conn, synapticActivityCounter range -10000 to +10000 + GeneralUtils.replaceElements!(mask, 1, kfn.lif_synapticWChangeCounter, 1.0) + kfn.lif_synapticWChangeCounter = kfn.lif_synapticWChangeCounter |> device + + kfn.lif_arrayProjection4d = (similar(kfn.lif_wRec) .= 1) + kfn.lif_recSignal = (similar(kfn.lif_wRec) .= 0) + kfn.lif_exInType = (similar(kfn.lif_wRec) .= 0) + # kfn.lif_decayed_epsilonRec = (similar(kfn.lif_wRec) .= 0) + # kfn.lif_vt_diff_vth = (similar(kfn.lif_wRec) .= 0) + # kfn.lif_vt_diff_vth_div_vth = (similar(kfn.lif_wRec) .= 0) + # kfn.lif_gammaPd_div_vth = (similar(kfn.lif_wRec) .= 0) + # kfn.lif_phiActivation = (similar(kfn.lif_wRec) .= 0) + + # ---------------------------------------------------------------------------- # + # ALIF config # + # ---------------------------------------------------------------------------- # + kfn.alif_size = [row, alif_col] |> device + alif_n = kfn.params[:computeNeuron][:alif][:numbers][1] * kfn.params[:computeNeuron][:alif][:numbers][2] + + # subscription + synapticConnectionPercent = kfn.params[:computeNeuron][:alif][:params][:synapticConnectionPercent] + kfn.alif_synapseConnectionNumber = Int(floor(row*col * synapticConnectionPercent/100)) + w = random_wRec(row, col, alif_n, kfn.alif_synapseConnectionNumber) + + # project 3D w into 4D kfn.alif_wRec + kfn.alif_wRec = reshape(w, (row, col, alif_n, 1)) .* ones(row, col, alif_n, batch) |> device + kfn.alif_zit = (similar(kfn.alif_wRec) .= 0) + kfn.alif_vt = (similar(kfn.alif_wRec) .= 0) + kfn.alif_vth = (similar(kfn.alif_wRec) .= 1) + kfn.alif_vRest = (similar(kfn.alif_wRec) .= 0) + kfn.alif_zt = zeros(1, 1, alif_n, batch) |> device + kfn.alif_zt4d = (similar(kfn.alif_wRec) .= 0) + kfn.alif_refractoryCounter = (similar(kfn.alif_wRec) .= 0) + kfn.alif_refractoryDuration = (similar(kfn.alif_wRec) .= 3) + kfn.alif_delta = 1.0 + kfn.alif_tau_m = 20.0 + kfn.alif_alpha = (similar(kfn.alif_wRec) .= (exp(-kfn.alif_delta / kfn.alif_tau_m))) + kfn.alif_phi = (similar(kfn.alif_wRec) .= 0) + kfn.alif_epsilonRec = (similar(kfn.alif_wRec) .= 0) + kfn.alif_eRec = (similar(kfn.alif_wRec) .= 0) + kfn.alif_eta = (similar(kfn.alif_wRec) .= 0.01) + kfn.alif_gammaPd = (similar(kfn.alif_wRec) .= 0.3) + kfn.alif_wRecChange = (similar(kfn.alif_wRec) .= 0) + kfn.alif_error = (similar(kfn.alif_wRec) .= 0) + + kfn.alif_firingCounter = (similar(kfn.alif_wRec) .= 0) + kfn.alif_firingTargetFrequency = (similar(kfn.alif_wRec) .= 0.1) + kfn.alif_neuronInactivityCounter = (similar(kfn.alif_wRec) .= 0) + kfn.alif_synapticActivityCounter = Array(similar(kfn.alif_wRec) .= -0.99) # -9 for non-sub conn + mask = Array((!iszero).(kfn.alif_wRec)) + # initial value subscribed conn, synapticActivityCounter range -10000 to +10000 + GeneralUtils.replaceElements!(mask, 1, kfn.alif_synapticActivityCounter, 0) + kfn.alif_synapticActivityCounter = kfn.alif_synapticActivityCounter |> device + kfn.alif_synapticWChangeCounter = Array(similar(kfn.alif_wRec) .= -0.99) # -9 for non-sub conn + mask = Array((!iszero).(kfn.alif_wRec)) + # initial value subscribed conn, synapticActivityCounter range -10000 to +10000 + GeneralUtils.replaceElements!(mask, 1, kfn.alif_synapticWChangeCounter, 1.0) + kfn.alif_synapticWChangeCounter = kfn.alif_synapticWChangeCounter |> device + + kfn.alif_arrayProjection4d = (similar(kfn.alif_wRec) .= 1) + kfn.alif_recSignal = (similar(kfn.alif_wRec) .= 0) + kfn.alif_exInType = (similar(kfn.alif_wRec) .= 0) + # kfn.alif_decayed_epsilonRec = (similar(kfn.alif_wRec) .= 0) + # kfn.alif_vt_diff_vth = (similar(kfn.alif_wRec) .= 0) + # kfn.alif_vt_diff_vth_div_vth = (similar(kfn.alif_wRec) .= 0) + # kfn.alif_gammaPd_div_vth = (similar(kfn.alif_wRec) .= 0) + # kfn.alif_phiActivation = (similar(kfn.alif_wRec) .= 0) + + # alif specific variables + kfn.alif_epsilonRecA = (similar(kfn.alif_wRec) .= 0) + kfn.alif_avth = (similar(kfn.alif_wRec) .= 0) + kfn.alif_a = (similar(kfn.alif_wRec) .= 0) + kfn.alif_beta = (similar(kfn.alif_wRec) .= 0.07) + kfn.alif_tau_a = 800.0 + kfn.alif_rho = (similar(kfn.alif_wRec) .= (exp(-kfn.alif_delta / kfn.alif_tau_a))) |> device + # kfn.alif_phi_x_epsilonRec = (similar(kfn.alif_wRec) .= 0) + # kfn.alif_phi_x_beta = (similar(kfn.alif_wRec) .= 0) + # kfn.alif_rho_diff_phi_x_beta = (similar(kfn.alif_wRec) .= 0) + # kfn.alif_rho_div_phi_x_beta_x_epsilonRecA = (similar(kfn.alif_wRec) .= 0) + # kfn.alif_beta_x_a = (similar(kfn.alif_wRec) .= 0) + + # ---------------------------------------------------------------------------- # + # output config # + # ---------------------------------------------------------------------------- # + n = kfn.params[:outputPort][:numbers][1] * kfn.params[:outputPort][:numbers][2] + + # subscription + w = zeros(row, col, n) + synapticConnectionPercent = kfn.params[:outputPort][:params][:synapticConnectionPercent] + subable = size(kfn.lif_wRec, 3) + size(kfn.alif_wRec, 3) # sub to lif, alif only + synapticConnection = Int(floor(subable * synapticConnectionPercent/100)) + for slice in eachslice(w, dims=3) # each slice is a neuron + startInd = row*col - subable + 1 # e.g. 100(row*col) - 50(subable) = 50 -> startInd = 51 + # pool must contain only lif, alif neurons + pool = shuffle!([startInd:row*col...])[1:synapticConnection] + for i in pool + slice[i] = rand() # assign weight to synaptic connection. /10 to start small, + # otherwise RSNN's vt Usually stay negative (-) + end + end + + # 10% of neuron connection should be enough to start to make neuron fires + should_be_avg_weight = 1 / (0.1 * n) + w = w .* (should_be_avg_weight / maximum(w)) # adjust overall weight + + # project 3D w into 4D kfn.lif_wOut (row, col, n, batch) + kfn.on_wOut = reshape(w, (row, col, n, 1)) .* ones(row, col, n, batch) |> device + kfn.on_zit = (similar(kfn.on_wOut) .= 0) + kfn.on_vt = (similar(kfn.on_wOut) .= 0) + kfn.on_vth = (similar(kfn.on_wOut) .= 1) + kfn.on_vRest = (similar(kfn.on_wOut) .= 0) + kfn.on_zt = zeros(1, 1, n, batch) |> device + kfn.on_zt4d = (similar(kfn.on_wOut) .= 0) + kfn.on_refractoryCounter = (similar(kfn.on_wOut) .= 0) + kfn.on_refractoryDuration = (similar(kfn.on_wOut) .= 0) + kfn.on_delta = 1.0 + kfn.on_tau_m = 20.0 + kfn.on_alpha = (similar(kfn.on_wOut) .= (exp(-kfn.on_delta / kfn.on_tau_m))) + kfn.on_phi = (similar(kfn.on_wOut) .= 0) + kfn.on_epsilonRec = (similar(kfn.on_wOut) .= 0) + kfn.on_eRec = (similar(kfn.on_wOut) .= 0) + kfn.on_eta = (similar(kfn.on_wOut) .= 0.01) + kfn.on_gammaPd = (similar(kfn.on_wOut) .= 0.3) + kfn.on_wOutChange = (similar(kfn.on_wOut) .= 0) + kfn.on_error = (similar(kfn.on_wOut) .= 0) + kfn.on_subscription = (GeneralUtils.isNotEqual.(kfn.on_wOut, 0)) |> device + + kfn.on_firingCounter = (similar(kfn.on_wOut) .= 0) + + kfn.on_arrayProjection4d = (similar(kfn.on_wOut) .= 1) + kfn.on_recSignal = (similar(kfn.on_wOut) .= 0) + + kfn.outputError = zeros(n, batch) |> device + totalComputeNeurons = lif_n + alif_n + inhabitoryNeurons = Int(floor(totalComputeNeurons * 30/100)) + mask1 = ones(row, signal_col) + mask2 = GeneralUtils.multiply_random_elements(ones(row, lif_col + alif_col), + -1, inhabitoryNeurons, MersenneTwister(1234)) + kfn.exInType = cat(mask1, mask2, dims=2) |> device + + return kfn +end + +function random_wRec(row, col, n, synapseConnectionNumber) + # subscription + w = zeros(row, col, n) + + for slice in eachslice(w, dims=3) + pool = shuffle!([1:row*col...])[1:synapseConnectionNumber] + for i in pool + slice[i] = rand(0.01:0.01:0.1) # assign weight to synaptic connection. /10 to start small, + # otherwise RSNN's vt Usually stay negative (-) + end + end + + # adjust weight so that RSNN fires small amount of neurons at the beginning to avoid overwhelming + # all-fire situation. it also better than not-fire-at-all situation. + avgWeight = sum(w)/length(w) + w = w .* (0.01 / avgWeight) # adjust overall weight + + return w #(row, col, n) +end + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +end # module \ No newline at end of file diff --git a/previousVersion/0.0.8.1/userFolderExample/.gitignore b/previousVersion/0.0.8.1/userFolderExample/.gitignore new file mode 100644 index 0000000..624779e --- /dev/null +++ b/previousVersion/0.0.8.1/userFolderExample/.gitignore @@ -0,0 +1 @@ +.CondaPkg \ No newline at end of file diff --git a/previousVersion/0.0.8.1/userFolderExample/Manifest.toml b/previousVersion/0.0.8.1/userFolderExample/Manifest.toml new file mode 100644 index 0000000..17eccf4 --- /dev/null +++ b/previousVersion/0.0.8.1/userFolderExample/Manifest.toml @@ -0,0 +1,2193 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.9.3" +manifest_format = "2.0" +project_hash = "9fdb6bb7da84fba5c911447c82dd61b17cda434d" + +[[deps.AbstractFFTs]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef" +uuid = 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+[[deps.p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" +version = "17.4.0+0" + +[[deps.x264_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" +uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" +version = "2021.5.5+0" + +[[deps.x265_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" +uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" +version = "3.5.0+0" diff --git a/previousVersion/0.0.8.1/userFolderExample/Project.toml b/previousVersion/0.0.8.1/userFolderExample/Project.toml new file mode 100644 index 0000000..8bc4111 --- /dev/null +++ b/previousVersion/0.0.8.1/userFolderExample/Project.toml @@ -0,0 +1,24 @@ +[deps] +BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0" +BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" +CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" +CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab" +Cthulhu = "f68482b8-f384-11e8-15f7-abe071a5a75f" +Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" +GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" +GPUArrays = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7" +GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe" +IronpenGPU = "3d5396ea-818e-43fc-a9d3-164248e840cd" +JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1" +MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458" +MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54" +MethodAnalysis = "85b6ec6f-f7df-4429-9514-a64bcd9ee824" +OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f" +ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca" +PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" +Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b" +SliceMap = "82cb661a-3f19-5665-9e27-df437c7e54c8" +StaticArrays = "90137ffa-7385-5640-81b9-e52037218182" +cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" diff --git a/previousVersion/0.0.8.1/userFolderExample/etc.jl b/previousVersion/0.0.8.1/userFolderExample/etc.jl new file mode 100644 index 0000000..9dc7a96 --- /dev/null +++ b/previousVersion/0.0.8.1/userFolderExample/etc.jl @@ -0,0 +1,868 @@ +# ---------------------------------------------------------------------------- # +# if one need to reinstall all python packages # +# ---------------------------------------------------------------------------- # + # 1. delete .CondaPkg folder in working folder + # 2. delete CondaPkg.toml file in working folder +# using Pkg; Pkg.activate("."); +# pythonPkg = ["CondaPkg", "PythonCall"] +# for i in pythonPkg try Pkg.rm(i) catch end end +# for i in pythonPkg Pkg.add(i) end +# using CondaPkg, PythonCall +# channels = ["anaconda", "conda-forge", "pytorch"] +# for i in channels CondaPkg.add_channel(i) end +# condapackage = ["numpy", "pytorch", "snntorch"] +# for i in condapackage CondaPkg.add(i) end + +using Pkg; Pkg.activate("."); Pkg.resolve(), Pkg.instantiate() +using Revise +using BenchmarkTools, Cthulhu +using Flux, CUDA +using BSON, JSON3 +using MLDatasets: MNIST +using MLUtils, ProgressMeter, Dates, Random, + Serialization, OneHotArrays , GLMakie + +using CondaPkg, PythonCall +np = pyimport("numpy") +torch = pyimport("torch") +spikegen = pyimport("snntorch.spikegen") # https://github.com/jeshraghian/snntorch + +using IronpenGPU +using GeneralUtils + +sep = Sys.iswindows() ? "\\" : "/" +rootDir = pwd() + +# select compute device +# device = Flux.CUDA.functional() ? gpu : cpu # Flux provide "cpu" and "gpu" keywork +device = gpu +if device == gpu CUDA.device!(0) end #CHANGE +# CUDA.allowscalar(false) # turn off scalar indexing in CPU to make it easier when moving to GPU +#------------------------------------------------------------------------------------------------100 + + + +""" + Todo: + - [] + + Change from version: + - + + All features + - +""" + + +# communication config --------------------------------------------------------------------------100 + +database_ip = "localhost" +# database_ip = "192.168.0.8" + +#------------------------------------------------------------------------------------------------100 +modelname = "runOn_gpu_0" #CHANGE +imageBatch = 1 + + +function generate_snn(filename::String, location::String) + signalInput_portnumbers = (10, 20, imageBatch) # 2nd dim needs to match + # input signal + copied input signal + noise. + # 3rd dim is input batch size + noise_portnumbers = (signalInput_portnumbers[1], 1) + output_portnumbers = (10, 1) + + # 5000 neurons are maximum for 64GB memory i.e. 300 LIF : 200 ALIF + lif_neuron_number = (signalInput_portnumbers[1], 30) # CHANGE + alif_neuron_number = (signalInput_portnumbers[1], 20) # CHANGE from Allen Institute, ALIF is 20-40% of LIF + + # totalNeurons = computeNeuronNumber + noise_portnumbers + signalInput_portnumbers + # totalInputPort = noise_portnumbers + signalInput_portnumbers + + # kfn and neuron config + passthrough_neuron_params = Dict( + :type => "passthroughNeuron" + ) + + lif_neuron_params = Dict{Symbol, Any}( + :type => "lifNeuron", + :v_t_default => 0.0, + :v_th => 1.0, # neuron firing threshold (this value is treated as maximum bound if I use auto generate) + :tau_m => 50.0, # membrane time constant in millisecond. + :eta => 1e-6, + # Good starting value is 1/10th of tau_a + # This is problem specific parameter. It controls how leaky the neuron is. + # Too high(less leaky) makes learning algo harder to move model into direction that reduce error + # resulting in model's error to explode exponantially likely because learning algo will try to + # exert more force (larger w_out_change) to move neuron into direction that reduce error + # For example, model error from 7 to 2e6. + + :synapticConnectionPercent => 20, # % coverage of total neurons in kfn + ) + + alif_neuron_params = Dict{Symbol, Any}( + :type => "alifNeuron", + :v_t_default => 0.0, + :v_th => 1.0, # neuron firing threshold (this value is treated as maximum bound if I use auto generate) + :tau_m => 50.0, # membrane time constant in millisecond. + :eta => 1e-6, + # Good starting value is 1/10th of tau_a + # This is problem specific parameter. It controls how leaky the neuron is. + # Too high(less leaky) makes learning algo harder to move model into direction that reduce error + # resulting in model's error to explode exponantially likely because learning algo will try to + # exert more force (larger w_out_change) to move neuron into direction that reduce error + # For example, model error from 7 to 2e6. + + :tau_a => 800.0, # adaptation time constant in millisecond. it defines neuron memory length. + # This is problem specific parameter + # Good starting value is 0.5 to 2 times of info STORE-RECALL length i.e. total time SNN takes to + # perform a task, for example, equals to episode length. + # From "Spike frequency adaptation supports network computations on temporally dispersed + # information" + + :synapticConnectionPercent => 20, # % coverage of total neurons in kfn + ) + + linear_neuron_params = Dict{Symbol, Any}( + :type => "linearNeuron", + :v_th => 1.0, # neuron firing threshold (this value is treated as maximum bound if I use auto generate) + :tau_out => 100.0, # output time constant in millisecond. + :synapticConnectionPercent => 20, # % coverage of total neurons in kfn + # Good starting value is 1/50th of tau_a + # This is problem specific parameter. + # It controls how leaky the neuron is. + # Too high(less leaky) makes learning algo harder to move model into direction that reduce error + # resulting in model's error to explode exponantially. For example, model error from 7 to 2e6 + # One can image training output neuron is like Tetris Game. + ) + + integrate_neuron_params = Dict{Symbol, Any}( + :type => "integrateNeuron", + :synapticConnectionPercent => 100, # % coverage of total neurons in kfn + :eta => 1e-6, + :tau_out => 100.0, + # Good starting value is 1/50th of tau_a + # This is problem specific parameter. + # It controls how leaky the neuron is. + # Too high(less leaky) makes learning algo harder to move model into direction that reduce error + # resulting in model's error to explode exponantially. For example, model error from 7 to 2e6 + # One can image training output neuron is like Tetris Game. + ) + + I_kfnparams = Dict{Symbol, Any}( + :knowledgeFnName=> "I", + :neuronFiringRateTarget=> 20.0, # Hz + + # group relavent info + :inputPort=> Dict( + :noise=> Dict( + :numbers=> noise_portnumbers, + :params=> passthrough_neuron_params, + ), + :signal=> Dict( + :numbers=> signalInput_portnumbers, # in case of GloVe word encoding, it is 300 + :params=> passthrough_neuron_params, + ), + ), + :outputPort=> Dict( + :numbers=> output_portnumbers, # output neuron, this is also the output length + :params=> linear_neuron_params, + ), + :computeNeuron=> Dict( + :lif=> Dict( + :numbers=> lif_neuron_number, # number in (row, col) tuple format + :params=> lif_neuron_params, + ), + :alif=> Dict( + :numbers=> alif_neuron_number, # number in (row, col) tuple format + :params=> alif_neuron_params, + ), + ), + ) + + #------------------------------------------------------------------------------------------------100 + + model = IronpenGPU.kfn_1(I_kfnparams, device=device); + + + # serialize(location * sep * filename, model) + println("SNN generated") + + return model +end + +function data_loader() + # test problem + trainDataset = MNIST(:train)[1:3] # total 60000 + # validateDataset = MNIST(:test) + validateDataset = MNIST(:train)[1:3] + labelDict = [0:9...] + + trainData = MLUtils.DataLoader( + trainDataset; # fullTrainDataset or trainDataset + batchsize=imageBatch, + collate=true, + shuffle=true, + buffer=true, + partial=false, # better for gpu memory if batchsize is fixed + # parallel=true, #BUG ?? causing dataloader into forever loop + ) + + validateData = MLUtils.DataLoader( + validateDataset; + batchsize=imageBatch, + collate=true, + shuffle=true, + buffer=true, + partial=false, # better for gpu memory if batchsize is fixed + # parallel=true, #BUG ?? causing dataloader into forever loop + ) + + # dummy data used to debug + # trainData = [(rand(10, 10), [5]), (rand(10, 10), [2])] + # trainData = [(rand(10, 10), [5]),] + + return trainData, validateData, labelDict +end + +function train_snn(model, trainData, validateData, labelDict::Vector) + + # random seed + # rng = MersenneTwister(1234) + + logitLog = zeros(10, 2) + firedNeurons_t1 = zeros(1) + var1 = zeros(10, 2) + var2 = zeros(10, 2) + var3 = zeros(10, 2) + var4 = zeros(10, 2) + + # ----------------------------------- plot ----------------------------------- # + plot10 = Observable(firedNeurons_t1) + + plot20 = Observable(logitLog[1 , :]) + plot21 = Observable(logitLog[2 , :]) + plot22 = Observable(logitLog[3 , :]) + plot23 = Observable(logitLog[4 , :]) + plot24 = Observable(logitLog[5 , :]) + plot25 = Observable(logitLog[6 , :]) + plot26 = Observable(logitLog[7 , :]) + plot27 = Observable(logitLog[8 , :]) + plot28 = Observable(logitLog[9 , :]) + plot29 = Observable(logitLog[10, :]) + + plot30 = Observable(var1[1 , :]) + plot31 = Observable(var1[2 , :]) + plot32 = Observable(var1[3 , :]) + plot33 = Observable(var1[4 , :]) + plot34 = Observable(var1[5 , :]) + plot35 = Observable(var1[6 , :]) + plot36 = Observable(var1[7 , :]) + plot37 = Observable(var1[8 , :]) + plot38 = Observable(var1[9 , :]) + plot39 = Observable(var1[10, :]) + + plot40 = Observable(var2[1 , :]) + plot41 = Observable(var2[2 , :]) + plot42 = Observable(var2[3 , :]) + plot43 = Observable(var2[4 , :]) + plot44 = Observable(var2[5 , :]) + plot45 = Observable(var2[6 , :]) + plot46 = Observable(var2[7 , :]) + plot47 = Observable(var2[8 , :]) + plot48 = Observable(var2[9 , :]) + plot49 = Observable(var2[10, :]) + + plot50 = Observable(var3[1 , :]) + plot51 = Observable(var3[2 , :]) + plot52 = Observable(var3[3 , :]) + plot53 = Observable(var3[4 , :]) + plot54 = Observable(var3[5 , :]) + plot55 = Observable(var3[6 , :]) + plot56 = Observable(var3[7 , :]) + plot57 = Observable(var3[8 , :]) + plot58 = Observable(var3[9 , :]) + plot59 = Observable(var3[10, :]) + + plot60 = Observable(var4[1 , :]) + plot61 = Observable(var4[2 , :]) + plot62 = Observable(var4[3 , :]) + plot63 = Observable(var4[4 , :]) + plot64 = Observable(var4[5 , :]) + plot65 = Observable(var4[6 , :]) + plot66 = Observable(var4[7 , :]) + plot67 = Observable(var4[8 , :]) + plot68 = Observable(var4[9 , :]) + plot69 = Observable(var4[10, :]) + + # main figure + fig1 = Figure() + + subfig1 = GLMakie.Axis(fig1[1, 1], # define position of this subfigure inside a figure + title = "RSNN firedNeurons_t1", + xlabel = "time", + ylabel = "data" + ) + lines!(subfig1, plot10, label = "firedNeurons_t1") + # axislegend(subfig1, position = :lb) + + subfig2 = GLMakie.Axis(fig1[2, 1], # define position of this subfigure inside a figure + title = "output neurons logit", + xlabel = "time", + ylabel = "data" + ) + + lines!(subfig2, plot20, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot21, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot22, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot23, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot24, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot25, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot26, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot27, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot28, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot29, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10)) + # axislegend(subfig2, position = :lb) + + + subfig3 = GLMakie.Axis(fig1[3, 1], # define position of this subfigure inside a figure + title = "last RSNN wRec", + xlabel = "time", + ylabel = "data" + ) + lines!(subfig3, plot30, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot31, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot32, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot33, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot34, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot35, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot36, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot37, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot38, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot39, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10)) + # axislegend(subfig3, position = :lb) + + subfig4 = GLMakie.Axis(fig1[4, 1], # define position of this subfigure inside a figure + title = "RSNN v_t1", + xlabel = "time", + ylabel = "data" + ) + lines!(subfig4, plot40, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot41, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot42, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot43, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot44, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot45, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot46, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot47, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot48, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot49, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10)) + # axislegend(subfig4, position = :lb) + + subfig5 = GLMakie.Axis(fig1[5, 1], # define position of this subfigure inside a figure + title = "output neuron epsilonRec", + xlabel = "time", + ylabel = "data" + ) + lines!(subfig5, plot50, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot51, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot52, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot53, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot54, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot55, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot56, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot57, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot58, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot59, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10)) + # axislegend(subfig5, position = :lb) + + subfig6 = GLMakie.Axis(fig1[6, 1], # define position of this subfigure inside a figure + title = "output neuron wRecChange", + xlabel = "time", + ylabel = "data" + ) + lines!(subfig6, plot60, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot61, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot62, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot63, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot64, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot65, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot66, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot67, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot68, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot69, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10)) + # axislegend(subfig6, position = :lb) + + # wait(display(fig1)) + # display(fig1) + # --------------------------------- end plot --------------------------------- # + + # model learning + thinkingPeriod = 16 # 1000-784 = 216 + bestAccuracy = 0.0 + finalAnswer = [0] |> device # store model prediction in (logit of choices, batch) + stop = 0 + for epoch = 1:1000 + stop == 3 ? break : false + println("epoch $epoch") + n = length(trainData) + println("n $n") + p = Progress(n, dt=1.0) # minimum update interval: 1 second + for (imgBatch, labels) in trainData # imgBatch (28, 28, 4) i.e. (row, col, batch) + for rep in 1:1000 + stop == 3 ? break : false + + #WORKING prepare image into input signal (10, 2, 784, 4) i.e. (row, col, timestep, batch) + s1 = nothing + s2 = nothing + + + + + + + + signal = spikeGenerator(imgBatch, [0.05, 0.1, 0.2, 0.3, 0.5], noise=(true, 1, 0.1), copies=18) + if length(size(signal)) == 3 + row, col, sequence = size(signal) + batch = 1 + else + row, col, sequence, batch = size(signal) + end + + # encode labels + correctAnswer = onehotbatch(labels, labelDict) # (choices, batch) + + # insert data into model sequencially + for timestep in 1:(sequence + thinkingPeriod) # sMNIST has 784 timestep(pixel) + thinking period = 1000 timestep + if timestep <= sequence + current_pixel = view(signal, :, :, timestep, :) |> device + else + current_pixel = zeros(row, col, batch) |> device # dummy input in "thinking" period + end + + if timestep == 1 # tell a model to start learning. 1-time only + model.learningStage = [1] + finalAnswer = [0] |> device + elseif timestep == (sequence+thinkingPeriod) + model.learningStage = [3] + else + end + + # predict + logit, _firedNeurons_t1 = model(current_pixel) + + # # log answer of all timestep + # logitLog = [logitLog;; logit] + # firedNeurons_t1 = push!(firedNeurons_t1, _firedNeurons_t1) + # var1 = [var1;; _var1] + # var2 = [var2;; _var2] + # var3 = [var3;; _var3] + # var4 = [var4;; _var4] + + if timestep < sequence # online learning, 1-by-1 timestep + # no error calculation + elseif timestep == sequence # online learning, 1-by-1 timestep + # no error calculation + elseif timestep > sequence && timestep < sequence+thinkingPeriod # collect answer + finalAnswer = length(finalAnswer) == 1 ? logit : finalAnswer .+ logit # (logit, batch) + predict_cpu = logit |> cpu + + modelError = (predict_cpu .- correctAnswer) + modelError = reshape(modelError, (1,1,:, size(modelError, 2))) + modelError = sum(modelError, dims=3) |> device + outputError = (predict_cpu .- correctAnswer) |> device + + lif_epsilonRec_cpu = model.lif_epsilonRec |> cpu + on_zt_cpu = model.on_zt |> cpu + + IronpenGPU.compute_paramsChange!(model, modelError, outputError) + + lif_wRecChange_cpu = model.lif_wRecChange |> cpu + + # if sum(lif_wRecChange_cpu) != 0 + # println("") + # lif_vt_cpu = model.lif_vt |> cpu + # lif_zt_cpu = model.lif_zt |> cpu + + # lif_recSignal = model.lif_recSignal |> cpu + + # on_vt_cpu = model.on_vt |> cpu + # on_vt_cpu = on_vt_cpu[1,1,:,1] + + # on_zt_cpu = on_zt_cpu[1,1,:,1] + # on_wOutChange_cpu = model.on_wOutChange |> cpu + # on_wOutChange_cpu = sum(on_wOutChange_cpu, dims=(1,2)) + # println("lif vt $(lif_vt_cpu[1,1,5,1]) lif zt $(lif_zt_cpu[1,1,5,1]) on_vt $on_vt_cpu on_zt $on_zt_cpu on_wOutChange_cpu $on_wOutChange_cpu") + # println("lif_recSignal ", lif_recSignal) + # println("") + # println("lif_epsilonRec_cpu ", lif_epsilonRec_cpu) + # println("") + # println("lif_wRecChange ", lif_wRecChange_cpu) + # println("") + # zit_cumulative = model.zit_cumulative |> cpu + # println("zit_cumulative ", zit_cumulative) + + # # error("DEBUG -> main $(Dates.now())") + # end + + elseif timestep == sequence+thinkingPeriod + finalAnswer = length(finalAnswer) == 1 ? logit : finalAnswer .+ logit # (logit, batch) + predict_cpu = logit |> cpu + + modelError = (predict_cpu .- correctAnswer) + modelError = reshape(modelError, (1,1,:, size(modelError, 2))) + modelError = sum(modelError, dims=3) |> device + outputError = (predict_cpu .- correctAnswer) |> device + + lif_epsilonRec_cpu = model.lif_epsilonRec |> cpu + on_zt_cpu = model.on_zt |> cpu + + IronpenGPU.compute_paramsChange!(model, modelError, outputError) + + lif_wRecChange_cpu = model.lif_wRecChange |> cpu + + + println("") + lif_recSignal_cpu = model.lif_recSignal |> cpu + lif_recSignal_cpu = sum(lif_recSignal_cpu[:,:,5,1]) + lif_vt_cpu = model.lif_vt |> cpu + lif_vt_cpu = lif_vt_cpu[1,1,5,1] + lif_zt_cpu = model.lif_zt |> cpu + lif_zt_cpu = lif_zt_cpu[1,1,5,1] + lif_epsilonRec_cpu = model.lif_epsilonRec |> cpu + lif_epsilonRec_cpu = sum(lif_epsilonRec_cpu[:,:,5,1]) + lif_wRecChange_cpu = sum(lif_wRecChange_cpu[:,:,5,1]) + on_vt_cpu = model.on_vt |> cpu + on_vt_cpu = on_vt_cpu[1,1,:,1] + + on_zt_cpu = on_zt_cpu[1,1,:,1] + on_wOutChange_cpu = model.on_wOutChange |> cpu + on_wOutChange_cpu = sum(on_wOutChange_cpu, dims=(1,2)) + println("lif recSignal $lif_recSignal_cpu lif vt $lif_vt_cpu lif zt $lif_zt_cpu lif_epsilonRec_cpu $lif_epsilonRec_cpu lif_wRecChange_cpu $lif_wRecChange_cpu on_vt $on_vt_cpu on_zt $on_zt_cpu on_wOutChange_cpu $on_wOutChange_cpu") + # println("lif_recSignal ", lif_recSignal) + # println("") + # println("lif_epsilonRec_cpu ", lif_epsilonRec_cpu) + # println("") + # println("lif_wRecChange ", lif_wRecChange_cpu) + # println("") + # zit_cumulative = model.zit_cumulative |> cpu + # println("zit_cumulative ", zit_cumulative) + + # error("DEBUG -> main $(Dates.now())") + + # commit learned weight only if the model answer incorrectly + finalAnswer_cpu = finalAnswer |> cpu + # println("label $(labels[1]) finalAnswer $finalAnswer_cpu") + max = isequal.(finalAnswer_cpu[:,1], maximum(finalAnswer_cpu[:,1])) + if sum(finalAnswer_cpu) == 0 + println("modelname $modelname epoch $epoch label $(labels[1]) finalAnswer ZERO answer LEARNING") + IronpenGPU.learn!(model, device) + elseif sum(max) == 1 && findall(max)[1] -1 == labels[1] + finalAnswer_cpu = findall(max)[1] - 1 + println("modelname $modelname epoch $epoch label $(labels[1]) finalAnswer $finalAnswer_cpu CORRECT") + elseif sum(max) == 1 && findall(max)[1] -1 != labels[1] + finalAnswer = findall(max)[1] - 1 + println("modelname $modelname epoch $epoch label $(labels[1]) finalAnswer $finalAnswer_cpu LEARNING") + IronpenGPU.learn!(model, device) + else + println("modelname $modelname epoch $epoch label $(labels[1]) finalAnswer $finalAnswer_cpu LEARNING") + IronpenGPU.learn!(model, device) + end + + # error("DEBUG -> main $(Dates.now())") + else + error("undefined condition line $(@__LINE__)") + # error("DEBUG -> main $(Dates.now())") + end + + # update plot + # plot10[] = firedNeurons_t1 + + # plot20[] = view(logitLog, 1 , :) + # plot21[] = view(logitLog, 2 , :) + # plot22[] = view(logitLog, 3 , :) + # plot23[] = view(logitLog, 4 , :) + # plot24[] = view(logitLog, 5 , :) + # plot25[] = view(logitLog, 6 , :) + # plot26[] = view(logitLog, 7 , :) + # plot27[] = view(logitLog, 8 , :) + # plot28[] = view(logitLog, 9 , :) + # plot29[] = view(logitLog, 10, :) + + # plot30[] = view(var1, 1 , :) + # plot31[] = view(var1, 2 , :) + # plot32[] = view(var1, 3 , :) + # plot33[] = view(var1, 4 , :) + # plot34[] = view(var1, 5 , :) + # plot35[] = view(var1, 6 , :) + # plot36[] = view(var1, 7 , :) + # plot37[] = view(var1, 8 , :) + # plot38[] = view(var1, 9 , :) + # plot39[] = view(var1, 10, :) + + # plot40[] = view(var2, 1 , :) + # plot41[] = view(var2, 2 , :) + # plot42[] = view(var2, 3 , :) + # plot43[] = view(var2, 4 , :) + # plot44[] = view(var2, 5 , :) + # plot45[] = view(var2, 6 , :) + # plot46[] = view(var2, 7 , :) + # plot47[] = view(var2, 8 , :) + # plot48[] = view(var2, 9 , :) + # plot49[] = view(var2, 10, :) + + # plot50[] = view(var3, 1 , :) + # plot51[] = view(var3, 2 , :) + # plot52[] = view(var3, 3 , :) + # plot53[] = view(var3, 4 , :) + # plot54[] = view(var3, 5 , :) + # plot55[] = view(var3, 6 , :) + # plot56[] = view(var3, 7 , :) + # plot57[] = view(var3, 8 , :) + # plot58[] = view(var3, 9 , :) + # plot59[] = view(var3, 10, :) + + # plot60[] = view(var4, 1 , :) + # plot61[] = view(var4, 2 , :) + # plot62[] = view(var4, 3 , :) + # plot63[] = view(var4, 4 , :) + # plot64[] = view(var4, 5 , :) + # plot65[] = view(var4, 6 , :) + # plot66[] = view(var4, 7 , :) + # plot67[] = view(var4, 8 , :) + # plot68[] = view(var4, 9 , :) + # plot69[] = view(var4, 10, :) + end + # end-thinkingPeriod+2; +2 because initialize logitLog = zeros(10, 2) + # _modelRespond = logitLog[:, end-thinkingPeriod+2:end] # answer count during thinking period + # _modelRespond = [sum(i) for i in eachrow(_modelRespond)] + # modelRespond = isequal.(isequal.(_modelRespond, 0), 0) + + # display(fig1) + # sleep(1) + # if k % 3 == 0 + # firedNeurons_t1 = zeros(1) + # logitLog = zeros(10, 2) + # var1 = zeros(10, 2) + # var2 = zeros(10, 2) + # var3 = zeros(10, 2) + # var4 = zeros(10, 2) + # end + + # # if predict == OneHotArrays.onehot(label, labelDict) + # # println("model train $label successfully, $k tries") + # # # wait(display(fig1)) + + # # firedNeurons_t1 = zeros(1) + # # logitLog = zeros(10, 2) + # # var1 = zeros(10, 2) + # # var2 = zeros(10, 2) + # # var3 = zeros(10, 2) + # # var4 = zeros(10, 2) + # # break + # # end + + # if k == maxRepeatRound + # # println("model train $label unsuccessfully, $maxRepeatRound tries, skip training") + # # display(fig1) + # firedNeurons_t1 = zeros(1) + # logitLog = zeros(10, 2) + # var1 = zeros(10, 2) + # var2 = zeros(10, 2) + # var3 = zeros(10, 2) + # var4 = zeros(10, 2) + # break + # end + end + + next!(p) + end + + if epoch > 200 + # check accuracy + println("validating model") + percentCorrect = validate(model, validateData, labelDict) + bestAccuracy = percentCorrect > bestAccuracy ? percentCorrect : bestAccuracy + println("$modelname model accuracy is $percentCorrect %, best accuracy is $bestAccuracy") + end + end +end + +function validate(model, dataset, labelDict) + totalAnswerCorrectly = 0 # score + totalSignal = 0 + thinkingPeriod = 16 # 1000-784 = 216 + predict = [0] |> device + + n = length(dataset) + println("n $n") + p = Progress(n, dt=1.0) # minimum update interval: 1 second + for (imgBatch, labels) in dataset + signal = spikeGenerator(imgBatch, [0.05, 0.1, 0.2, 0.3, 0.5], noise=(true, 1, 0.5), copies=18) + if length(size(signal)) == 3 + row, col, sequence = size(signal) + batch = 1 + else + row, col, sequence, batch = size(signal) + end + + # encode labels + correctAnswer = onehotbatch(labels, labelDict) # (choices, batch) + + # insert data into model sequencially + for timestep in 1:(sequence + thinkingPeriod) # sMNIST has 784 timestep(pixel) + thinking period = 1000 timestep + if timestep <= sequence + current_pixel = view(signal, :, :, timestep, :) |> device + else + current_pixel = zeros(row, col, batch) |> device # dummy input in "thinking" period + end + + if timestep == 1 # tell a model to start learning. 1-time only + predict = [0] |> device + elseif timestep == (sequence+thinkingPeriod) + else + end + + # predict + logit, _ = model(current_pixel) + + if timestep < sequence # online learning, 1-by-1 timestep + # no error calculation + elseif timestep == sequence # online learning, 1-by-1 timestep + # no error calculation + elseif timestep > sequence && timestep < sequence+thinkingPeriod # collect answer + predict = length(predict) == 1 ? logit : predict .+ logit # (logit, batch) + elseif timestep == sequence+thinkingPeriod + predict = length(predict) == 1 ? logit : predict .+ logit # (logit, batch) + else + error("undefined condition line $(@__LINE__)") + end + end + + predict_cpu = predict |> cpu + _predict_label = mapslices(GeneralUtils.vectorMax, predict_cpu; dims=1) + s = sum(_predict_label, dims=1) + if 0 ∉ s + predict_label = [] + for i in eachcol(_predict_label) + _label = findall(i) .- 1 + if length(_label) == 1 + append!(predict_label, _label) + else + push!(predict_label, -1) # predict more than 1 label. add non-count label. + end + end + answerCorrectly = sum([x == y for (x,y) in zip(predict_label, labels)]) + totalAnswerCorrectly += answerCorrectly + totalSignal += batch + end + + next!(p) + end + + percentCorrect = totalAnswerCorrectly * 100.0 / totalSignal + + return percentCorrect::Float64 +end + +""" inputsignals is normal column-major julia matrix in (row, col, batch) dimension + - each threshold scan return 2 vectors. 1 for +, 1 for - + - noise = (true/false, row, col, probability) +""" +function spikeGenerator(inputsignals, thresholds=[1.0]; noise=(false, 1, 0.5), copies=0) + s = length(size(inputsignals)) + ar = [] # holding all signals that are scanned + for slice in eachslice(inputsignals, dims=s) + signal_jl = nothing + for col in eachcol(slice) + signal_jl = length(signal_jl) === nothing ? col : cat(signal_jl, col, dim) + + + + + + + + + + signal_jl = reshape(slice, (:, 1)) # python array is row-major + signal_pytensor = torch.from_numpy( np.asarray(signal_jl) ) + + arr = [] # holding signal that is scanned by several thresholds + for threshold in thresholds + spike_py = spikegen.delta(signal_pytensor, threshold=threshold, off_spike=true) + _spike_jl = pyconvert(Array, spike_py.data.numpy()) + spike_jl = reshape(_spike_jl, (1, :)) # reshape back to julia's column-major + spike_jl1 = isequal.(spike_jl, 1) + spike_jl2 = isequal.(spike_jl, -1) + arr = length(arr) == 0 ? [spike_jl1; spike_jl2] : [arr; spike_jl1; spike_jl2] + end + arrSize = [size(arr)...] + arr = reshape(arr, (arrSize[1], 1, arrSize[2])) # reshape into (row, 1, timestep) + + # multiply col + if copies > 0 + a = deepcopy(arr) + for i in 1:copies + arr = cat(arr, a, dims=2) + end + end + + if noise[1] == true + arrSize = [size(arr)...] + n = noiseGenerator(arrSize[1], noise[2], arrSize[3], prob=noise[3]) + arr = cat(arr, n, dims=2) # concatenate into (row, signal:noise, timestep) + end + + # concatenate into (row, signal:noise, timestep, batch) + ar = length(ar) == 0 ? arr : [ar;;;;arr] + end + return ar +end + +function noiseGenerator(row, col, z; prob=0.5) + spike_prob = torch.rand(row, col, z) * prob + spike_rand = spikegen.rate_conv(spike_prob) + noise = isequal.(pyconvert(Array, spike_rand.data.numpy()), 1) + + return noise +end + +# function arrayMax(x) +# if sum(GeneralUtils.isNotEqual.(x, 0)) == 0 # guard against all-zeros array +# return GeneralUtils.isNotEqual.(x, 0) +# else +# return isequal.(x, maximum(x)) +# end +# end +# arraySliceMax(x) = mapslices(arrayMax, x; dims=1) + +function main() + filelocation = string(@__DIR__) + + filename = "$modelname.jl163" + + training_start_time = Dates.now() + println("$modelname program started $training_start_time") + + model = generate_snn(filename, filelocation) + + trainDataset, validateDataset, labelDict = data_loader() + + train_snn(model, trainDataset, validateDataset, labelDict) + + finish_training_time = Dates.now() + println("training done, $training_start_time ==> $finish_training_time ") + println(" ///////////////////////////////////////////////////////////////////////") +end + +# only runs main() if julia isn’t started interactively +# https://discourse.julialang.org/t/scripting-like-a-julian/50707 +!isinteractive() && main() +#------------------------------------------------------------------------------------------------100 + + + + + + diff --git a/previousVersion/0.0.8.1/userFolderExample/main_gpu_0.jl b/previousVersion/0.0.8.1/userFolderExample/main_gpu_0.jl new file mode 100644 index 0000000..54c9d0a --- /dev/null +++ b/previousVersion/0.0.8.1/userFolderExample/main_gpu_0.jl @@ -0,0 +1,845 @@ +# ---------------------------------------------------------------------------- # +# if one need to reinstall all python packages # +# ---------------------------------------------------------------------------- # + # 1. delete .CondaPkg folder in working folder + # 2. delete CondaPkg.toml file in working folder +# using Pkg; Pkg.activate("."); +# pythonPkg = ["CondaPkg", "PythonCall"] +# for i in pythonPkg try Pkg.rm(i) catch end end +# for i in pythonPkg Pkg.add(i) end +# using CondaPkg, PythonCall +# channels = ["anaconda", "conda-forge", "pytorch"] +# for i in channels CondaPkg.add_channel(i) end +# condapackage = ["numpy", "pytorch", "snntorch"] +# for i in condapackage CondaPkg.add(i) end + +using Pkg; Pkg.activate("."); Pkg.resolve(), Pkg.instantiate() +using Revise +using BenchmarkTools, Cthulhu +using Flux, CUDA +using BSON, JSON3 +using MLDatasets: MNIST +using MLUtils, ProgressMeter, Dates, Random, + Serialization, OneHotArrays , GLMakie + +using CondaPkg, PythonCall +np = pyimport("numpy") +torch = pyimport("torch") +spikegen = pyimport("snntorch.spikegen") # https://github.com/jeshraghian/snntorch + +using IronpenGPU +using GeneralUtils + +sep = Sys.iswindows() ? "\\" : "/" +rootDir = pwd() + +# select compute device +# device = Flux.CUDA.functional() ? gpu : cpu # Flux provide "cpu" and "gpu" keywork +device = gpu +if device == gpu CUDA.device!(0) end #CHANGE +# CUDA.allowscalar(false) # turn off scalar indexing in CPU to make it easier when moving to GPU +#------------------------------------------------------------------------------------------------100 + + + +""" + Todo: + - [] + + Change from version: + - + + All features + - +""" + + +# communication config --------------------------------------------------------------------------100 + +database_ip = "localhost" +# database_ip = "192.168.0.8" + +#------------------------------------------------------------------------------------------------100 +modelname = "runOn_gpu_0" #CHANGE +imageBatch = 1 + + +function generate_snn(filename::String, location::String) + signalInput_portnumbers = (10, 20, imageBatch) # 2nd dim needs to match + # input signal + copied input signal + noise. + # 3rd dim is input batch size + noise_portnumbers = (signalInput_portnumbers[1], 1) + output_portnumbers = (10, 1) + + # 5000 neurons are maximum for 64GB memory i.e. 300 LIF : 200 ALIF + lif_neuron_number = (signalInput_portnumbers[1], 30) # CHANGE + alif_neuron_number = (signalInput_portnumbers[1], 20) # CHANGE from Allen Institute, ALIF is 20-40% of LIF + + # totalNeurons = computeNeuronNumber + noise_portnumbers + signalInput_portnumbers + # totalInputPort = noise_portnumbers + signalInput_portnumbers + + # kfn and neuron config + passthrough_neuron_params = Dict( + :type => "passthroughNeuron" + ) + + lif_neuron_params = Dict{Symbol, Any}( + :type => "lifNeuron", + :v_t_default => 0.0, + :v_th => 1.0, # neuron firing threshold (this value is treated as maximum bound if I use auto generate) + :tau_m => 50.0, # membrane time constant in millisecond. + :eta => 1e-6, + # Good starting value is 1/10th of tau_a + # This is problem specific parameter. It controls how leaky the neuron is. + # Too high(less leaky) makes learning algo harder to move model into direction that reduce error + # resulting in model's error to explode exponantially likely because learning algo will try to + # exert more force (larger w_out_change) to move neuron into direction that reduce error + # For example, model error from 7 to 2e6. + + :synapticConnectionPercent => 80, # % coverage of total neurons in kfn + ) + + alif_neuron_params = Dict{Symbol, Any}( + :type => "alifNeuron", + :v_t_default => 0.0, + :v_th => 1.0, # neuron firing threshold (this value is treated as maximum bound if I use auto generate) + :tau_m => 50.0, # membrane time constant in millisecond. + :eta => 1e-6, + # Good starting value is 1/10th of tau_a + # This is problem specific parameter. It controls how leaky the neuron is. + # Too high(less leaky) makes learning algo harder to move model into direction that reduce error + # resulting in model's error to explode exponantially likely because learning algo will try to + # exert more force (larger w_out_change) to move neuron into direction that reduce error + # For example, model error from 7 to 2e6. + + :tau_a => 800.0, # adaptation time constant in millisecond. it defines neuron memory length. + # This is problem specific parameter + # Good starting value is 0.5 to 2 times of info STORE-RECALL length i.e. total time SNN takes to + # perform a task, for example, equals to episode length. + # From "Spike frequency adaptation supports network computations on temporally dispersed + # information" + + :synapticConnectionPercent => 80, # % coverage of total neurons in kfn + ) + + linear_neuron_params = Dict{Symbol, Any}( + :type => "linearNeuron", + :v_th => 1.0, # neuron firing threshold (this value is treated as maximum bound if I use auto generate) + :tau_out => 100.0, # output time constant in millisecond. + :synapticConnectionPercent => 100, # % coverage of total neurons in kfn + # Good starting value is 1/50th of tau_a + # This is problem specific parameter. + # It controls how leaky the neuron is. + # Too high(less leaky) makes learning algo harder to move model into direction that reduce error + # resulting in model's error to explode exponantially. For example, model error from 7 to 2e6 + # One can image training output neuron is like Tetris Game. + ) + + integrate_neuron_params = Dict{Symbol, Any}( + :type => "integrateNeuron", + :synapticConnectionPercent => 100, # % coverage of total neurons in kfn + :eta => 1e-6, + :tau_out => 100.0, + # Good starting value is 1/50th of tau_a + # This is problem specific parameter. + # It controls how leaky the neuron is. + # Too high(less leaky) makes learning algo harder to move model into direction that reduce error + # resulting in model's error to explode exponantially. For example, model error from 7 to 2e6 + # One can image training output neuron is like Tetris Game. + ) + + I_kfnparams = Dict{Symbol, Any}( + :knowledgeFnName=> "I", + :neuronFiringRateTarget=> 20.0, # Hz + + # group relavent info + :inputPort=> Dict( + :noise=> Dict( + :numbers=> noise_portnumbers, + :params=> passthrough_neuron_params, + ), + :signal=> Dict( + :numbers=> signalInput_portnumbers, # in case of GloVe word encoding, it is 300 + :params=> passthrough_neuron_params, + ), + ), + :outputPort=> Dict( + :numbers=> output_portnumbers, # output neuron, this is also the output length + :params=> linear_neuron_params, + ), + :computeNeuron=> Dict( + :lif=> Dict( + :numbers=> lif_neuron_number, # number in (row, col) tuple format + :params=> lif_neuron_params, + ), + :alif=> Dict( + :numbers=> alif_neuron_number, # number in (row, col) tuple format + :params=> alif_neuron_params, + ), + ), + ) + + #------------------------------------------------------------------------------------------------100 + + model = IronpenGPU.kfn_1(I_kfnparams, device=device); + + + # serialize(location * sep * filename, model) + println("SNN generated") + + return model +end + +function data_loader() + # test problem + trainDataset = MNIST(:train)[1:10] # total 60000 + # validateDataset = MNIST(:test) + validateDataset = MNIST(:train)[1:10] + labelDict = [0:9...] + + trainData = MLUtils.DataLoader( + trainDataset; # fullTrainDataset or trainDataset + batchsize=imageBatch, + collate=true, + shuffle=true, + buffer=true, + partial=false, # better for gpu memory if batchsize is fixed + # parallel=true, #BUG ?? causing dataloader into forever loop + ) + + validateData = MLUtils.DataLoader( + validateDataset; + batchsize=imageBatch, + collate=true, + shuffle=true, + buffer=true, + partial=false, # better for gpu memory if batchsize is fixed + # parallel=true, #BUG ?? causing dataloader into forever loop + ) + + # dummy data used to debug + # trainData = [(rand(10, 10), [5]), (rand(10, 10), [2])] + # trainData = [(rand(10, 10), [5]),] + + return trainData, validateData, labelDict +end + +function train_snn(model, trainData, validateData, labelDict::Vector) + + # random seed + # rng = MersenneTwister(1234) + + logitLog = zeros(10, 2) + firedNeurons_t1 = zeros(1) + var1 = zeros(10, 2) + var2 = zeros(10, 2) + var3 = zeros(10, 2) + var4 = zeros(10, 2) + + # ----------------------------------- plot ----------------------------------- # + plot10 = Observable(firedNeurons_t1) + + plot20 = Observable(logitLog[1 , :]) + plot21 = Observable(logitLog[2 , :]) + plot22 = Observable(logitLog[3 , :]) + plot23 = Observable(logitLog[4 , :]) + plot24 = Observable(logitLog[5 , :]) + plot25 = Observable(logitLog[6 , :]) + plot26 = Observable(logitLog[7 , :]) + plot27 = Observable(logitLog[8 , :]) + plot28 = Observable(logitLog[9 , :]) + plot29 = Observable(logitLog[10, :]) + + plot30 = Observable(var1[1 , :]) + plot31 = Observable(var1[2 , :]) + plot32 = Observable(var1[3 , :]) + plot33 = Observable(var1[4 , :]) + plot34 = Observable(var1[5 , :]) + plot35 = Observable(var1[6 , :]) + plot36 = Observable(var1[7 , :]) + plot37 = Observable(var1[8 , :]) + plot38 = Observable(var1[9 , :]) + plot39 = Observable(var1[10, :]) + + plot40 = Observable(var2[1 , :]) + plot41 = Observable(var2[2 , :]) + plot42 = Observable(var2[3 , :]) + plot43 = Observable(var2[4 , :]) + plot44 = Observable(var2[5 , :]) + plot45 = Observable(var2[6 , :]) + plot46 = Observable(var2[7 , :]) + plot47 = Observable(var2[8 , :]) + plot48 = Observable(var2[9 , :]) + plot49 = Observable(var2[10, :]) + + plot50 = Observable(var3[1 , :]) + plot51 = Observable(var3[2 , :]) + plot52 = Observable(var3[3 , :]) + plot53 = Observable(var3[4 , :]) + plot54 = Observable(var3[5 , :]) + plot55 = Observable(var3[6 , :]) + plot56 = Observable(var3[7 , :]) + plot57 = Observable(var3[8 , :]) + plot58 = Observable(var3[9 , :]) + plot59 = Observable(var3[10, :]) + + plot60 = Observable(var4[1 , :]) + plot61 = Observable(var4[2 , :]) + plot62 = Observable(var4[3 , :]) + plot63 = Observable(var4[4 , :]) + plot64 = Observable(var4[5 , :]) + plot65 = Observable(var4[6 , :]) + plot66 = Observable(var4[7 , :]) + plot67 = Observable(var4[8 , :]) + plot68 = Observable(var4[9 , :]) + plot69 = Observable(var4[10, :]) + + # main figure + fig1 = Figure() + + subfig1 = GLMakie.Axis(fig1[1, 1], # define position of this subfigure inside a figure + title = "RSNN firedNeurons_t1", + xlabel = "time", + ylabel = "data" + ) + lines!(subfig1, plot10, label = "firedNeurons_t1") + # axislegend(subfig1, position = :lb) + + subfig2 = GLMakie.Axis(fig1[2, 1], # define position of this subfigure inside a figure + title = "output neurons logit", + xlabel = "time", + ylabel = "data" + ) + + lines!(subfig2, plot20, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot21, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot22, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot23, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot24, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot25, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot26, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot27, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot28, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig2, plot29, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10)) + # axislegend(subfig2, position = :lb) + + + subfig3 = GLMakie.Axis(fig1[3, 1], # define position of this subfigure inside a figure + title = "last RSNN wRec", + xlabel = "time", + ylabel = "data" + ) + lines!(subfig3, plot30, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot31, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot32, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot33, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot34, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot35, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot36, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot37, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot38, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig3, plot39, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10)) + # axislegend(subfig3, position = :lb) + + subfig4 = GLMakie.Axis(fig1[4, 1], # define position of this subfigure inside a figure + title = "RSNN v_t1", + xlabel = "time", + ylabel = "data" + ) + lines!(subfig4, plot40, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot41, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot42, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot43, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot44, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot45, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot46, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot47, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot48, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig4, plot49, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10)) + # axislegend(subfig4, position = :lb) + + subfig5 = GLMakie.Axis(fig1[5, 1], # define position of this subfigure inside a figure + title = "output neuron epsilonRec", + xlabel = "time", + ylabel = "data" + ) + lines!(subfig5, plot50, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot51, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot52, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot53, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot54, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot55, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot56, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot57, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot58, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig5, plot59, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10)) + # axislegend(subfig5, position = :lb) + + subfig6 = GLMakie.Axis(fig1[6, 1], # define position of this subfigure inside a figure + title = "output neuron wRecChange", + xlabel = "time", + ylabel = "data" + ) + lines!(subfig6, plot60, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot61, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot62, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot63, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot64, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot65, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot66, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot67, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot68, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) ) + lines!(subfig6, plot69, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10)) + # axislegend(subfig6, position = :lb) + + # wait(display(fig1)) + # display(fig1) + # --------------------------------- end plot --------------------------------- # + + # model learning + thinkingPeriod = 16 # 1000-784 = 216 + bestAccuracy = 0.0 + finalAnswer = [0] |> device # store model prediction in (logit of choices, batch) + stop = 0 + for epoch = 1:1000 + stop == 3 ? break : false + println("epoch $epoch") + n = length(trainData) + println("n $n") + p = Progress(n, dt=1.0) # minimum update interval: 1 second + for (imgBatch, labels) in trainData # imgBatch (28, 28, 4) i.e. (row, col, batch) + for rep in 1:10 + stop == 3 ? break : false + # signal (10, 2, 784, 4) i.e. (row, col, timestep, batch) + signal = spikeGenerator(imgBatch, [0.05, 0.1, 0.2, 0.3, 0.5], noise=(true, 1, 0.5), copies=18) + if length(size(signal)) == 3 + row, col, sequence = size(signal) + batch = 1 + else + row, col, sequence, batch = size(signal) + end + + # encode labels + correctAnswer = onehotbatch(labels, labelDict) # (choices, batch) + + # insert data into model sequencially + for timestep in 1:(sequence + thinkingPeriod) # sMNIST has 784 timestep(pixel) + thinking period = 1000 timestep + if timestep <= sequence + current_pixel = view(signal, :, :, timestep, :) |> device + else + current_pixel = zeros(row, col, batch) |> device # dummy input in "thinking" period + end + + if timestep == 1 # tell a model to start learning. 1-time only + model.learningStage = [1] + finalAnswer = [0] |> device + elseif timestep == (sequence+thinkingPeriod) + model.learningStage = [3] + else + end + + # predict + logit, _firedNeurons_t1 = model(current_pixel) + + # # log answer of all timestep + # logitLog = [logitLog;; logit] + # firedNeurons_t1 = push!(firedNeurons_t1, _firedNeurons_t1) + # var1 = [var1;; _var1] + # var2 = [var2;; _var2] + # var3 = [var3;; _var3] + # var4 = [var4;; _var4] + + if timestep < sequence # online learning, 1-by-1 timestep + # no error calculation + elseif timestep == sequence # online learning, 1-by-1 timestep + # no error calculation + elseif timestep > sequence && timestep < sequence+thinkingPeriod # collect answer + finalAnswer = length(finalAnswer) == 1 ? logit : finalAnswer .+ logit # (logit, batch) + predict_cpu = logit |> cpu + + modelError = (predict_cpu .- correctAnswer) + modelError = reshape(modelError, (1,1,:, size(modelError, 2))) + modelError = sum(modelError, dims=3) |> device + outputError = (predict_cpu .- correctAnswer) |> device + + lif_epsilonRec_cpu = model.lif_epsilonRec |> cpu + on_zt_cpu = model.on_zt |> cpu + labels = labels |> device + IronpenGPU.compute_paramsChange!(model, outputError, outputError) + + # lif_wRecChange_cpu = model.lif_wRecChange |> cpu + + # if sum(lif_wRecChange_cpu) != 0 + # println("") + # lif_vt_cpu = model.lif_vt |> cpu + # lif_zt_cpu = model.lif_zt |> cpu + + # lif_recSignal = model.lif_recSignal |> cpu + + # on_vt_cpu = model.on_vt |> cpu + # on_vt_cpu = on_vt_cpu[1,1,:,1] + + # on_zt_cpu = on_zt_cpu[1,1,:,1] + # on_wOutChange_cpu = model.on_wOutChange |> cpu + # on_wOutChange_cpu = sum(on_wOutChange_cpu, dims=(1,2)) + # println("lif vt $(lif_vt_cpu[1,1,5,1]) lif zt $(lif_zt_cpu[1,1,5,1]) on_vt $on_vt_cpu on_zt $on_zt_cpu on_wOutChange_cpu $on_wOutChange_cpu") + # println("lif_recSignal ", lif_recSignal) + # println("") + # println("lif_epsilonRec_cpu ", lif_epsilonRec_cpu) + # println("") + # println("lif_wRecChange ", lif_wRecChange_cpu) + # println("") + # zit_cumulative = model.zit_cumulative |> cpu + # println("zit_cumulative ", zit_cumulative) + + # # error("DEBUG -> main $(Dates.now())") + # end + + elseif timestep == sequence+thinkingPeriod + finalAnswer = length(finalAnswer) == 1 ? logit : finalAnswer .+ logit # (logit, batch) + predict_cpu = logit |> cpu + + modelError = (predict_cpu .- correctAnswer) + modelError = reshape(modelError, (1,1,:, size(modelError, 2))) + modelError = sum(modelError, dims=3) |> device + outputError = (predict_cpu .- correctAnswer) |> device + + lif_epsilonRec_cpu = model.lif_epsilonRec |> cpu + on_zt_cpu = model.on_zt |> cpu + labels = labels |> device + IronpenGPU.compute_paramsChange!(model, outputError, outputError) + + lif_wRecChange_cpu = model.lif_wRecChange |> cpu + + + println("") + lif_recSignal_cpu = model.lif_recSignal |> cpu + lif_recSignal_cpu = sum(lif_recSignal_cpu[:,:,5,1]) + lif_vt_cpu = model.lif_vt |> cpu + lif_vt_cpu = lif_vt_cpu[1,1,5,1] + lif_zt_cpu = model.lif_zt |> cpu + lif_zt_cpu = lif_zt_cpu[1,1,5,1] + lif_epsilonRec_cpu = model.lif_epsilonRec |> cpu + lif_epsilonRec_cpu = sum(lif_epsilonRec_cpu[:,:,5,1]) + lif_wRecChange_cpu = sum(lif_wRecChange_cpu[:,:,5,1]) + on_vt_cpu = model.on_vt |> cpu + on_vt_cpu = on_vt_cpu[1,1,:,1] + + on_zt_cpu = on_zt_cpu[1,1,:,1] + on_wOutChange_cpu = model.on_wOutChange |> cpu + on_wOutChange_cpu = sum(on_wOutChange_cpu, dims=(1,2)) + println("lif recSignal $lif_recSignal_cpu lif vt $lif_vt_cpu lif zt $lif_zt_cpu lif_epsilonRec_cpu $lif_epsilonRec_cpu lif_wRecChange_cpu $lif_wRecChange_cpu on_vt $on_vt_cpu on_zt $on_zt_cpu on_wOutChange_cpu $on_wOutChange_cpu") + # println("lif_recSignal ", lif_recSignal) + # println("") + # println("lif_epsilonRec_cpu ", lif_epsilonRec_cpu) + # println("") + # println("lif_wRecChange ", lif_wRecChange_cpu) + # println("") + # zit_cumulative = model.zit_cumulative |> cpu + # println("zit_cumulative ", zit_cumulative) + + # error("DEBUG -> main $(Dates.now())") + + # commit learned weight only if the model answer incorrectly + finalAnswer_cpu = finalAnswer |> cpu + # println("label $(labels[1]) finalAnswer $finalAnswer_cpu") + max = isequal.(finalAnswer_cpu[:,1], maximum(finalAnswer_cpu[:,1])) + if sum(finalAnswer_cpu) == 0 + println("modelname $modelname epoch $epoch rep $rep label $(labels[1]) finalAnswer __ LEARNING") + IronpenGPU.learn!(model, device) + elseif sum(max) == 1 && findall(max)[1] -1 == labels[1] + println("modelname $modelname epoch $epoch rep $rep label $(labels[1]) finalAnswer $finalAnswer_cpu CORRECT") + IronpenGPU.learn!(model, device) + elseif sum(max) == 1 && findall(max)[1] -1 != labels[1] + println("modelname $modelname epoch $epoch rep $rep label $(labels[1]) finalAnswer $finalAnswer_cpu LEARNING") + IronpenGPU.learn!(model, device) + else + println("modelname $modelname epoch $epoch rep $rep label $(labels[1]) finalAnswer $finalAnswer_cpu LEARNING") + IronpenGPU.learn!(model, device) + end + + # error("DEBUG -> main $(Dates.now())") + else + error("undefined condition line $(@__LINE__)") + # error("DEBUG -> main $(Dates.now())") + end + + # update plot + # plot10[] = firedNeurons_t1 + + # plot20[] = view(logitLog, 1 , :) + # plot21[] = view(logitLog, 2 , :) + # plot22[] = view(logitLog, 3 , :) + # plot23[] = view(logitLog, 4 , :) + # plot24[] = view(logitLog, 5 , :) + # plot25[] = view(logitLog, 6 , :) + # plot26[] = view(logitLog, 7 , :) + # plot27[] = view(logitLog, 8 , :) + # plot28[] = view(logitLog, 9 , :) + # plot29[] = view(logitLog, 10, :) + + # plot30[] = view(var1, 1 , :) + # plot31[] = view(var1, 2 , :) + # plot32[] = view(var1, 3 , :) + # plot33[] = view(var1, 4 , :) + # plot34[] = view(var1, 5 , :) + # plot35[] = view(var1, 6 , :) + # plot36[] = view(var1, 7 , :) + # plot37[] = view(var1, 8 , :) + # plot38[] = view(var1, 9 , :) + # plot39[] = view(var1, 10, :) + + # plot40[] = view(var2, 1 , :) + # plot41[] = view(var2, 2 , :) + # plot42[] = view(var2, 3 , :) + # plot43[] = view(var2, 4 , :) + # plot44[] = view(var2, 5 , :) + # plot45[] = view(var2, 6 , :) + # plot46[] = view(var2, 7 , :) + # plot47[] = view(var2, 8 , :) + # plot48[] = view(var2, 9 , :) + # plot49[] = view(var2, 10, :) + + # plot50[] = view(var3, 1 , :) + # plot51[] = view(var3, 2 , :) + # plot52[] = view(var3, 3 , :) + # plot53[] = view(var3, 4 , :) + # plot54[] = view(var3, 5 , :) + # plot55[] = view(var3, 6 , :) + # plot56[] = view(var3, 7 , :) + # plot57[] = view(var3, 8 , :) + # plot58[] = view(var3, 9 , :) + # plot59[] = view(var3, 10, :) + + # plot60[] = view(var4, 1 , :) + # plot61[] = view(var4, 2 , :) + # plot62[] = view(var4, 3 , :) + # plot63[] = view(var4, 4 , :) + # plot64[] = view(var4, 5 , :) + # plot65[] = view(var4, 6 , :) + # plot66[] = view(var4, 7 , :) + # plot67[] = view(var4, 8 , :) + # plot68[] = view(var4, 9 , :) + # plot69[] = view(var4, 10, :) + end + # end-thinkingPeriod+2; +2 because initialize logitLog = zeros(10, 2) + # _modelRespond = logitLog[:, end-thinkingPeriod+2:end] # answer count during thinking period + # _modelRespond = [sum(i) for i in eachrow(_modelRespond)] + # modelRespond = isequal.(isequal.(_modelRespond, 0), 0) + + # display(fig1) + # sleep(1) + # if k % 3 == 0 + # firedNeurons_t1 = zeros(1) + # logitLog = zeros(10, 2) + # var1 = zeros(10, 2) + # var2 = zeros(10, 2) + # var3 = zeros(10, 2) + # var4 = zeros(10, 2) + # end + + # # if predict == OneHotArrays.onehot(label, labelDict) + # # println("model train $label successfully, $k tries") + # # # wait(display(fig1)) + + # # firedNeurons_t1 = zeros(1) + # # logitLog = zeros(10, 2) + # # var1 = zeros(10, 2) + # # var2 = zeros(10, 2) + # # var3 = zeros(10, 2) + # # var4 = zeros(10, 2) + # # break + # # end + + # if k == maxRepeatRound + # # println("model train $label unsuccessfully, $maxRepeatRound tries, skip training") + # # display(fig1) + # firedNeurons_t1 = zeros(1) + # logitLog = zeros(10, 2) + # var1 = zeros(10, 2) + # var2 = zeros(10, 2) + # var3 = zeros(10, 2) + # var4 = zeros(10, 2) + # break + # end + end + + next!(p) + end + + if epoch > 200 + # check accuracy + println("validating model") + percentCorrect = validate(model, validateData, labelDict) + bestAccuracy = percentCorrect > bestAccuracy ? percentCorrect : bestAccuracy + println("$modelname model accuracy is $percentCorrect %, best accuracy is $bestAccuracy") + end + end +end + +function validate(model, dataset, labelDict) + totalAnswerCorrectly = 0 # score + totalSignal = 0 + thinkingPeriod = 16 # 1000-784 = 216 + predict = [0] |> device + + n = length(dataset) + println("n $n") + p = Progress(n, dt=1.0) # minimum update interval: 1 second + for (imgBatch, labels) in dataset + signal = spikeGenerator(imgBatch, [0.05, 0.1, 0.2, 0.3, 0.5], noise=(true, 1, 0.5), copies=18) + if length(size(signal)) == 3 + row, col, sequence = size(signal) + batch = 1 + else + row, col, sequence, batch = size(signal) + end + + # encode labels + correctAnswer = onehotbatch(labels, labelDict) # (choices, batch) + + # insert data into model sequencially + for timestep in 1:(sequence + thinkingPeriod) # sMNIST has 784 timestep(pixel) + thinking period = 1000 timestep + if timestep <= sequence + current_pixel = view(signal, :, :, timestep, :) |> device + else + current_pixel = zeros(row, col, batch) |> device # dummy input in "thinking" period + end + + if timestep == 1 # tell a model to start learning. 1-time only + predict = [0] |> device + elseif timestep == (sequence+thinkingPeriod) + else + end + + # predict + logit, _ = model(current_pixel) + + if timestep < sequence # online learning, 1-by-1 timestep + # no error calculation + elseif timestep == sequence # online learning, 1-by-1 timestep + # no error calculation + elseif timestep > sequence && timestep < sequence+thinkingPeriod # collect answer + predict = length(predict) == 1 ? logit : predict .+ logit # (logit, batch) + elseif timestep == sequence+thinkingPeriod + predict = length(predict) == 1 ? logit : predict .+ logit # (logit, batch) + else + error("undefined condition line $(@__LINE__)") + end + end + + predict_cpu = predict |> cpu + _predict_label = mapslices(GeneralUtils.vectorMax, predict_cpu; dims=1) + s = sum(_predict_label, dims=1) + if 0 ∉ s + predict_label = [] + for i in eachcol(_predict_label) + _label = findall(i) .- 1 + if length(_label) == 1 + append!(predict_label, _label) + else + push!(predict_label, -1) # predict more than 1 label. add non-count label. + end + end + answerCorrectly = sum([x == y for (x,y) in zip(predict_label, labels)]) + totalAnswerCorrectly += answerCorrectly + totalSignal += batch + end + + next!(p) + end + + percentCorrect = totalAnswerCorrectly * 100.0 / totalSignal + + return percentCorrect::Float64 +end + +""" inputsignals is normal column-major julia matrix in (row, col, batch) dimension + - each threshold scan return 2 vectors. 1 for +, 1 for - + - noise = (true/false, row, col, probability) +""" +function spikeGenerator(inputsignals, thresholds=[1.0]; noise=(false, 1, 0.5), copies=0) + s = length(size(inputsignals)) + ar = [] # holding all signals that are scanned + for slice in eachslice(inputsignals, dims=s) + signal_jl = reshape(slice, (:, 1)) # python array is row-major + signal_pytensor = torch.from_numpy( np.asarray(signal_jl) ) + + arr = [] # holding signal that is scanned by several thresholds + for threshold in thresholds + spike_py = spikegen.delta(signal_pytensor, threshold=threshold, off_spike=true) + _spike_jl = pyconvert(Array, spike_py.data.numpy()) + spike_jl = reshape(_spike_jl, (1, :)) # reshape back to julia's column-major + spike_jl1 = isequal.(spike_jl, 1) + spike_jl2 = isequal.(spike_jl, -1) + arr = length(arr) == 0 ? [spike_jl1; spike_jl2] : [arr; spike_jl1; spike_jl2] + end + arrSize = [size(arr)...] + arr = reshape(arr, (arrSize[1], 1, arrSize[2])) # reshape into (row, 1, timestep) + + # multiply col + if copies > 0 + a = deepcopy(arr) + for i in 1:copies + arr = cat(arr, a, dims=2) + end + end + + if noise[1] == true + arrSize = [size(arr)...] + n = noiseGenerator(arrSize[1], noise[2], arrSize[3], prob=noise[3]) + arr = cat(arr, n, dims=2) # concatenate into (row, signal:noise, timestep) + end + + # concatenate into (row, signal:noise, timestep, batch) + ar = length(ar) == 0 ? arr : [ar;;;;arr] + end + return ar +end + +function noiseGenerator(row, col, z; prob=0.5) + spike_prob = torch.rand(row, col, z) * prob + spike_rand = spikegen.rate_conv(spike_prob) + noise = isequal.(pyconvert(Array, spike_rand.data.numpy()), 1) + + return noise +end + +# function arrayMax(x) +# if sum(GeneralUtils.isNotEqual.(x, 0)) == 0 # guard against all-zeros array +# return GeneralUtils.isNotEqual.(x, 0) +# else +# return isequal.(x, maximum(x)) +# end +# end +# arraySliceMax(x) = mapslices(arrayMax, x; dims=1) + +function main() + filelocation = string(@__DIR__) + + filename = "$modelname.jl163" + + training_start_time = Dates.now() + println("$modelname program started $training_start_time") + + model = generate_snn(filename, filelocation) + + trainDataset, validateDataset, labelDict = data_loader() + + train_snn(model, trainDataset, validateDataset, labelDict) + + finish_training_time = Dates.now() + println("training done, $training_start_time ==> $finish_training_time ") + println(" ///////////////////////////////////////////////////////////////////////") +end + +# only runs main() if julia isn’t started interactively +# https://discourse.julialang.org/t/scripting-like-a-julian/50707 +!isinteractive() && main() +#------------------------------------------------------------------------------------------------100 + + + + + + diff --git a/previousVersion/0.0.9/userFolderExample/main_gpu_0.jl b/previousVersion/0.0.9/userFolderExample/main_gpu_0.jl index e020c37..86fbc0d 100644 --- a/previousVersion/0.0.9/userFolderExample/main_gpu_0.jl +++ b/previousVersion/0.0.9/userFolderExample/main_gpu_0.jl @@ -14,19 +14,6 @@ # for i in condapackage CondaPkg.add(i) end using Pkg; Pkg.activate("."); Pkg.resolve(), Pkg.instantiate() - - -# ---------------------------------------------------------------------------- # -# for debugging purpose # -# ---------------------------------------------------------------------------- # -# https://discourse.julialang.org/t/debugging-extremely-slow/53801/3 -# using MethodAnalysis -# visit(Base) do item -# isa(item, Module) && push!(JuliaInterpreter.compiled_modules, item) -# true -# end - - using Revise using BenchmarkTools, Cthulhu using Flux, CUDA @@ -139,7 +126,7 @@ function generate_snn(filename::String, location::String) :type => "linearNeuron", :v_th => 1.0, # neuron firing threshold (this value is treated as maximum bound if I use auto generate) :tau_out => 100.0, # output time constant in millisecond. - :synapticConnectionPercent => 20, # % coverage of total neurons in kfn + :synapticConnectionPercent => 100, # % coverage of total neurons in kfn # Good starting value is 1/50th of tau_a # This is problem specific parameter. # It controls how leaky the neuron is. @@ -414,19 +401,17 @@ function train_snn(model, trainData, validateData, labelDict::Vector) bestAccuracy = 0.0 finalAnswer = [0] |> device # store model prediction in (logit of choices, batch) stop = 0 - vt0 = 0.0 # store vt to compute learning progress for epoch = 1:1000 stop == 3 ? break : false println("epoch $epoch") n = length(trainData) println("n $n") p = Progress(n, dt=1.0) # minimum update interval: 1 second - for (imgBatch, labels) in trainData # imgBatch(28, 28, 4) i.e. (row, col, batch), labels(label, batch) + for (imgBatch, labels) in trainData # imgBatch (28, 28, 4) i.e. (row, col, batch) for rep in 1:10 stop == 3 ? break : false - - # prepare image into input signal (10, 2, 784, 4) i.e. (row, col, timestep, batch) - signal = dualTrackSpikeGen(imgBatch, [0.05, 0.1, 0.2, 0.3, 0.5], noise=(true, 1, 0.1), copies=18) + # signal (10, 2, 784, 4) i.e. (row, col, timestep, batch) + signal = spikeGenerator(imgBatch, [0.05, 0.1, 0.2, 0.3, 0.5], noise=(true, 1, 0.1), copies=18) if length(size(signal)) == 3 row, col, sequence = size(signal) batch = 1 @@ -435,7 +420,7 @@ function train_snn(model, trainData, validateData, labelDict::Vector) end # encode labels - correctAnswer = onehotbatch(labels, labelDict) # (correctAnswer, batch) + correctAnswer = onehotbatch(labels, labelDict) # (choices, batch) # insert data into model sequencially for timestep in 1:(sequence + thinkingPeriod) # sMNIST has 784 timestep(pixel) + thinking period = 1000 timestep @@ -448,7 +433,6 @@ function train_snn(model, trainData, validateData, labelDict::Vector) if timestep == 1 # tell a model to start learning. 1-time only model.learningStage = [1] finalAnswer = [0] |> device - vt0 = 0.0 elseif timestep == (sequence+thinkingPeriod) model.learningStage = [3] else @@ -469,20 +453,9 @@ function train_snn(model, trainData, validateData, labelDict::Vector) # no error calculation elseif timestep == sequence # online learning, 1-by-1 timestep # no error calculation - - #WORKING answer time windows, collect logit to get finalAnswer - elseif timestep > sequence && timestep < sequence+thinkingPeriod - logit_cpu = logit |> cpu - logit_cpu = logit_cpu[:,1] + elseif timestep > sequence && timestep < sequence+thinkingPeriod # collect answer finalAnswer = length(finalAnswer) == 1 ? logit : finalAnswer .+ logit # (logit, batch) - finalAnswer_cpu = finalAnswer |> cpu - on_vt_cpu = model.on_vt |> cpu - on_vt_cpu = on_vt_cpu[1,1,:,1] - - modelError = loss(vt0, on_vt_cpu, logit_cpu, finalAnswer_cpu, labels[1]) - vt0 = on_vt_cpu # update vt0 for this timestep - - error("DEBUG -> main $(Dates.now())") + predict_cpu = logit |> cpu modelError = (predict_cpu .- correctAnswer) modelError = reshape(modelError, (1,1,:, size(modelError, 2))) @@ -522,28 +495,14 @@ function train_snn(model, trainData, validateData, labelDict::Vector) # # error("DEBUG -> main $(Dates.now())") # end - elseif timestep == sequence+thinkingPeriod #TODO update code - logit_cpu = logit |> cpu + elseif timestep == sequence+thinkingPeriod finalAnswer = length(finalAnswer) == 1 ? logit : finalAnswer .+ logit # (logit, batch) - finalAnswer_cpu = finalAnswer |> cpu - on_vt_cpu = model.on_vt |> cpu - on_vt_cpu = on_vt_cpu[1,1,:,1] - - # get vt of correct neuron, julia array is 1-based index - labelPosition = labels[1] + 1 - on_vt_cpu = on_vt_cpu[labelPosition] + predict_cpu = logit |> cpu - modelError = loss(vt0, on_vt_cpu, logit_cpu, finalAnswer_cpu) - vt0 = on_vt_cpu - - error("DEBUG -> main $(Dates.now())") - - - - modelError = (logit_cpu .- correctAnswer) + modelError = (predict_cpu .- correctAnswer) modelError = reshape(modelError, (1,1,:, size(modelError, 2))) modelError = sum(modelError, dims=3) |> device - outputError = (logit_cpu .- correctAnswer) |> device + outputError = (predict_cpu .- correctAnswer) |> device lif_epsilonRec_cpu = model.lif_epsilonRec |> cpu on_zt_cpu = model.on_zt |> cpu @@ -796,33 +755,6 @@ function validate(model, dataset, labelDict) return percentCorrect::Float64 end -function dualTrackSpikeGen(inputsignals, thresholds=[1.0]; noise=(false, 1, 0.5), copies=0) - rowInputSignal = nothing - colInputSignal = nothing - - for slice in eachslice(inputsignals, dims=3) - srow = nothing - scol = nothing - for row in eachrow(slice) - srow = srow === nothing ? row : cat(srow, row, dims=1) - end - - for col in eachcol(slice) - scol = scol === nothing ? col : cat(scol, col, dims=1) - end - - rowInputSignal = rowInputSignal === nothing ? srow : cat(rowInputSignal, srow, dims=3) - colInputSignal = colInputSignal === nothing ? scol : cat(colInputSignal, scol, dims=3) - end - rowInputSignal = reshape(rowInputSignal, (size(rowInputSignal, 1), 1, size(inputsignals, 3))) - colInputSignal = reshape(colInputSignal, (size(colInputSignal, 1), 1, size(inputsignals, 3))) - rowInputSignal = spikeGenerator(rowInputSignal, thresholds, noise=noise, copies=8) - colInputSignal = spikeGenerator(colInputSignal, thresholds, noise=noise, copies=8) - - signal = cat(rowInputSignal, colInputSignal, dims=2) - return signal -end - """ inputsignals is normal column-major julia matrix in (row, col, batch) dimension - each threshold scan return 2 vectors. 1 for +, 1 for - - noise = (true/false, row, col, probability) @@ -874,33 +806,6 @@ function noiseGenerator(row, col, z; prob=0.5) return noise end -function loss(vt0::AbstractArray, vt1::AbstractArray, logit::AbstractArray, - finalAnswer::AbstractArray, correctAnswer::Number) - labelPosition = correctAnswer + 1 # julia array is 1-based index - - # get vt of correct neuron - vt1 = vt1[labelPosition] - - # get zt of correct neuron - zt = logit[labelPosition] - - modelError = nothing - - if zt == 1 - modelError = 0.0 # already correct, no weight update - elseif vt1 > vt0 # progress increase - modelError = 1.0 - vt1 - elseif vt1 == vt0 # no progress - modelError = 0.11111111 # special signal - elseif vt1 < vt0 # setback - modelError = vt0 - vt1 - else - error("undefined condition line $(@__LINE__)") - end - - return modelError -end - # function arrayMax(x) # if sum(GeneralUtils.isNotEqual.(x, 0)) == 0 # guard against all-zeros array # return GeneralUtils.isNotEqual.(x, 0) diff --git a/src/IronpenGPU.jl b/src/IronpenGPU.jl index 2487b10..ec02dec 100644 --- a/src/IronpenGPU.jl +++ b/src/IronpenGPU.jl @@ -27,8 +27,8 @@ using .interface """ version 0.0.9 Todo: - [0*] change madel error calculation in user script, (progress based) - [1] +W 90% of most active conn + [DONE] change madel error calculation in user script, (progress based) + [1*] +W 90% of most active conn [2] -W 10% of less active conn [3] synapse reconnect delay counter [-] add temporal summation in addition to already used spatial summation. diff --git a/src/learn.jl b/src/learn.jl index 257db8f..41edd09 100644 --- a/src/learn.jl +++ b/src/learn.jl @@ -8,8 +8,8 @@ using ..type, ..snnUtil #------------------------------------------------------------------------------------------------100 -function compute_paramsChange!(kfn::kfn_1, modelError, outputError) - +function compute_paramsChange!(kfn::kfn_1, modelError::CuArray, outputError::CuArray, label) + lifComputeParamsChange!(kfn.timeStep, kfn.lif_phi, kfn.lif_epsilonRec, @@ -24,29 +24,35 @@ function compute_paramsChange!(kfn::kfn_1, modelError, outputError) kfn.lif_arrayProjection4d, kfn.lif_error, modelError, - + outputError, kfn.inputSize, + kfn.bk, + label, ) alifComputeParamsChange!(kfn.timeStep, - kfn.alif_phi, - kfn.alif_epsilonRec, - kfn.alif_eta, - kfn.alif_eRec, - kfn.alif_wRec, - kfn.alif_exInType, - kfn.alif_wRecChange, - kfn.on_wOut, - kfn.alif_firingCounter, - kfn.alif_firingTargetFrequency, - kfn.alif_arrayProjection4d, - kfn.alif_error, - modelError, + kfn.alif_phi, + kfn.alif_epsilonRec, + kfn.alif_eta, + kfn.alif_eRec, + kfn.alif_wRec, + kfn.alif_exInType, + kfn.alif_wRecChange, + kfn.on_wOut, + kfn.alif_firingCounter, + kfn.alif_firingTargetFrequency, + kfn.alif_arrayProjection4d, + kfn.alif_error, + modelError, + outputError, + kfn.inputSize, + kfn.bk, + label, - kfn.alif_epsilonRecA, - kfn.alif_beta, + kfn.alif_epsilonRecA, + kfn.alif_beta, ) - + onComputeParamsChange!(kfn.on_phi, kfn.on_epsilonRec, kfn.on_eta, @@ -58,7 +64,7 @@ function compute_paramsChange!(kfn::kfn_1, modelError, outputError) ) # error("DEBUG -> kfn compute_paramsChange! $(Dates.now())") end - +#WORKING implement 90% +w, 10% -w function lifComputeParamsChange!( timeStep::CuArray, phi::CuArray, epsilonRec::CuArray, @@ -73,45 +79,59 @@ function lifComputeParamsChange!( timeStep::CuArray, arrayProjection4d::CuArray, nError::CuArray, modelError::CuArray, - + outputError::CuArray, inputSize::CuArray, + bk::CuArray, + label, ) - # Bₖⱼ in paper, sum() to get each neuron's total wOut weight, - # use absolute because only magnitude is needed - wOutSum_all = reshape( abs.(sum(wOut, dims=3)), (1,1,:, size(wOut, 4)) ) # (1,1,allNeuron,batch) - - # get only each lif neuron's wOut, leaving out other neuron's wOut + eRec .= phi .* epsilonRec + + # 2D wRec matrix contain input, lif, alif neurons. I need only lif neurons startIndex = prod(inputSize) +1 stopIndex = startIndex + size(wRec, 3) -1 - wOutSum = @view(wOutSum_all[1,1, startIndex:stopIndex, :]) - wOutSum = reshape(wOutSum, (1, 1, size(wOutSum, 1), size(wOutSum, 2))) # (1,1,n,batch) - - # nError a.k.a. learning signal use dopamine concept, - # this neuron receive summed error signal (modelError) - nError .= (modelError .* wOutSum) .* arrayProjection4d - eRec .= phi .* epsilonRec - wRecChange .+= (-eta .* nError .* eRec) + startCol = CartesianIndices(wRec)[startIndex][2] + stopCol = CartesianIndices(wRec)[stopIndex][2] + + _bk = @view(bk[:, startCol:stopCol, 1]) + nError = _bk .* modelError + nError = reshape(nError, (1,1,:,1)) + + # _,_,i3,_ = size(wOut) + # for i in 1:i3 + # # nError a.k.a. learning signal use dopamine concept, + # # this neuron receive summed error signal (modelError) + # onW = @view(wOut[:, startCol:stopCol, i, 1]) + # _bk = @view(bk[:, startCol:stopCol, i, 1]) + # mask = (iszero).(onW) + # bk_ = mask .* _bk + # bkComposed = onW .+ bk_ + + # nError = bkComposed .* modelError + # nError = reshape(nError, (1,1,:,1)) + + # # compute wRecChange of all neurons wrt to iᵗʰ output neuron + # wRecChange .+= (eta .* nError .* eRec) + # end + + # compute wRecChange of all neurons wrt to iᵗʰ output neuron + wRecChange .+= (eta .* nError .* eRec) # frequency regulator wRecChange .+= 0.001 .* ((firingTargetFrequency - (firingCounter./timeStep)) ./ timeStep) .* eta .* eRec - # if sum(timeStep) == 785 # epsilonRec_cpu = epsilonRec |> cpu - # println("modelError ", modelError) + # println("modelError $modelError $(size(modelError))", modelError) # println("") - # wchange = (-eta .* nError .* eRec) |> cpu + # println("wOutSum $(size(wOutSum))") + # wchange = (eta .* nError .* eRec) |> cpu # println("wchange 5 1 ", wchange[:,:,5,1]) # println("") - # println("wchange 5 2 ", wchange[:,:,5,2]) - # println("") # println("epsilonRec 5 1 ", epsilonRec_cpu[:,:,5,1]) # println("") - # println("epsilonRec 5 2 ", epsilonRec_cpu[:,:,5,2]) - # println("") # error("DEBUG lifComputeParamsChange!") # end - + # reset epsilonRec epsilonRec .= 0 end @@ -130,24 +150,28 @@ function alifComputeParamsChange!( timeStep::CuArray, arrayProjection4d::CuArray, nError::CuArray, modelError::CuArray, + outputError::CuArray, + inputSize::CuArray, + bk::CuArray, + label, epsilonRecA::CuArray, - beta::CuArray + beta::CuArray, ) - # Bₖⱼ in paper, sum() to get each neuron's total wOut weight, - # use absolute because only magnitude is needed - wOutSum_all = reshape( abs.(sum(wOut, dims=3)), (1,1,:, size(wOut, 4)) ) # (1,1,allNeuron,batch) - - # get only each lif neuron's wOut, leaving out other neuron's wOut - wOutSum = @view(wOutSum_all[1,1, end-size(wRec, 3)+1:end, :]) - wOutSum = reshape(wOutSum, (1, 1, size(wOutSum, 1), size(wOutSum, 2))) # (1,1,n,batch) - - # nError a.k.a. learning signal use dopamine concept, - # this neuron receive summed error signal (modelError) - nError .= (modelError .* wOutSum) .* arrayProjection4d eRec .= phi .* (epsilonRec .- (beta .* epsilonRecA)) # use eq. 25 - wRecChange .+= (-eta .* nError .* eRec) + + # 2D wRec matrix contain input, lif, alif neurons. I need only lif neurons + startIndex = prod(inputSize) +1 + stopIndex = startIndex + size(wRec, 3) -1 + startCol = CartesianIndices(wRec)[startIndex][2] + stopCol = CartesianIndices(wRec)[stopIndex][2] + + _bk = @view(bk[:, startCol:stopCol, 1]) + nError = _bk .* modelError + nError = reshape(nError, (1,1,:,1)) + + wRecChange .+= (eta .* nError .* eRec) # frequency regulator wRecChange .+= 0.001 .* ((firingTargetFrequency - (firingCounter./timeStep)) ./ timeStep) .* @@ -395,8 +419,8 @@ function lifLearn(wRec, zitCumulative_cpu = zitCumulative_cpu[:,:,1] #TODO neuroplasticity, work on CPU side - wRec_cpu, neuronInactivityCounter_cpu, synapseReconnectDelayCounter_cpu, - = neuroplasticity(synapseConnectionNumber, + wRec_cpu, neuronInactivityCounter_cpu, synapseReconnectDelayCounter_cpu = + neuroplasticity(synapseConnectionNumber, zitCumulative_cpu, wRec_cpu, wRecChange_cpu, diff --git a/src/type.jl b/src/type.jl index 813e5b1..507d61e 100644 --- a/src/type.jl +++ b/src/type.jl @@ -28,6 +28,7 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn exInType::Union{AbstractArray, Nothing} = nothing modelError::Union{AbstractArray, Nothing} = nothing # store RSNN error outputError::Union{AbstractArray, Nothing} = nothing # store output neurons error + bk::Union{AbstractArray, Nothing} = nothing # Bⱼₖ # ---------------------------------------------------------------------------- # # LIF Neurons # @@ -190,6 +191,7 @@ function kfn_1(params::Dict; device=cpu) kfn.zit = zeros(row, col, batch) |> device kfn.zitCumulative = (similar(kfn.zit) .= 0) kfn.modelError = zeros(1) |> device + kfn.bk = rand(size(kfn.zit)...) |> device # ---------------------------------------------------------------------------- # # LIF config # @@ -222,7 +224,7 @@ function kfn_1(params::Dict; device=cpu) kfn.lif_eta = (similar(kfn.lif_wRec) .= 0.01) kfn.lif_gammaPd = (similar(kfn.lif_wRec) .= 0.3) kfn.lif_wRecChange = (similar(kfn.lif_wRec) .= 0) - kfn.lif_error = (similar(kfn.lif_wRec) .= 0) + kfn.lif_error = (similar(kfn.lif_wRec) .= 0) kfn.lif_firingCounter = (similar(kfn.lif_wRec) .= 0) kfn.lif_firingTargetFrequency = (similar(kfn.lif_wRec) .= 0.1) @@ -280,7 +282,7 @@ function kfn_1(params::Dict; device=cpu) kfn.alif_eta = (similar(kfn.alif_wRec) .= 0.01) kfn.alif_gammaPd = (similar(kfn.alif_wRec) .= 0.3) kfn.alif_wRecChange = (similar(kfn.alif_wRec) .= 0) - kfn.alif_error = (similar(kfn.alif_wRec) .= 0) + kfn.alif_error = (similar(kfn.alif_wRec) .= 0) kfn.alif_firingCounter = (similar(kfn.alif_wRec) .= 0) kfn.alif_firingTargetFrequency = (similar(kfn.alif_wRec) .= 0.1)