version 0.0.2
This commit is contained in:
911
previousVersion/0.0.2/Manifest.toml
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911
previousVersion/0.0.2/Manifest.toml
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# This file is machine-generated - editing it directly is not advised
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uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
|
||||
version = "0.7.0"
|
||||
|
||||
[[deps.Scratch]]
|
||||
deps = ["Dates"]
|
||||
git-tree-sha1 = "30449ee12237627992a99d5e30ae63e4d78cd24a"
|
||||
uuid = "6c6a2e73-6563-6170-7368-637461726353"
|
||||
version = "1.2.0"
|
||||
|
||||
[[deps.Serialization]]
|
||||
uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
|
||||
|
||||
[[deps.Setfield]]
|
||||
deps = ["ConstructionBase", "Future", "MacroTools", "StaticArraysCore"]
|
||||
git-tree-sha1 = "e2cc6d8c88613c05e1defb55170bf5ff211fbeac"
|
||||
uuid = "efcf1570-3423-57d1-acb7-fd33fddbac46"
|
||||
version = "1.1.1"
|
||||
|
||||
[[deps.ShowCases]]
|
||||
git-tree-sha1 = "7f534ad62ab2bd48591bdeac81994ea8c445e4a5"
|
||||
uuid = "605ecd9f-84a6-4c9e-81e2-4798472b76a3"
|
||||
version = "0.1.0"
|
||||
|
||||
[[deps.SimpleTraits]]
|
||||
deps = ["InteractiveUtils", "MacroTools"]
|
||||
git-tree-sha1 = "5d7e3f4e11935503d3ecaf7186eac40602e7d231"
|
||||
uuid = "699a6c99-e7fa-54fc-8d76-47d257e15c1d"
|
||||
version = "0.9.4"
|
||||
|
||||
[[deps.Sockets]]
|
||||
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
|
||||
|
||||
[[deps.SortingAlgorithms]]
|
||||
deps = ["DataStructures"]
|
||||
git-tree-sha1 = "c60ec5c62180f27efea3ba2908480f8055e17cee"
|
||||
uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
|
||||
version = "1.1.1"
|
||||
|
||||
[[deps.SparseArrays]]
|
||||
deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"]
|
||||
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
|
||||
|
||||
[[deps.SpecialFunctions]]
|
||||
deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
|
||||
git-tree-sha1 = "7beb031cf8145577fbccacd94b8a8f4ce78428d3"
|
||||
uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
|
||||
version = "2.3.0"
|
||||
weakdeps = ["ChainRulesCore"]
|
||||
|
||||
[deps.SpecialFunctions.extensions]
|
||||
SpecialFunctionsChainRulesCoreExt = "ChainRulesCore"
|
||||
|
||||
[[deps.SplittablesBase]]
|
||||
deps = ["Setfield", "Test"]
|
||||
git-tree-sha1 = "e08a62abc517eb79667d0a29dc08a3b589516bb5"
|
||||
uuid = "171d559e-b47b-412a-8079-5efa626c420e"
|
||||
version = "0.1.15"
|
||||
|
||||
[[deps.StaticArrays]]
|
||||
deps = ["LinearAlgebra", "Random", "StaticArraysCore", "Statistics"]
|
||||
git-tree-sha1 = "832afbae2a45b4ae7e831f86965469a24d1d8a83"
|
||||
uuid = "90137ffa-7385-5640-81b9-e52037218182"
|
||||
version = "1.5.26"
|
||||
|
||||
[[deps.StaticArraysCore]]
|
||||
git-tree-sha1 = "6b7ba252635a5eff6a0b0664a41ee140a1c9e72a"
|
||||
uuid = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
|
||||
version = "1.4.0"
|
||||
|
||||
[[deps.Statistics]]
|
||||
deps = ["LinearAlgebra", "SparseArrays"]
|
||||
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
|
||||
version = "1.9.0"
|
||||
|
||||
[[deps.StatsAPI]]
|
||||
deps = ["LinearAlgebra"]
|
||||
git-tree-sha1 = "45a7769a04a3cf80da1c1c7c60caf932e6f4c9f7"
|
||||
uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
|
||||
version = "1.6.0"
|
||||
|
||||
[[deps.StatsBase]]
|
||||
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
|
||||
git-tree-sha1 = "75ebe04c5bed70b91614d684259b661c9e6274a4"
|
||||
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
|
||||
version = "0.34.0"
|
||||
|
||||
[[deps.StatsFuns]]
|
||||
deps = ["HypergeometricFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"]
|
||||
git-tree-sha1 = "f625d686d5a88bcd2b15cd81f18f98186fdc0c9a"
|
||||
uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
|
||||
version = "1.3.0"
|
||||
|
||||
[deps.StatsFuns.extensions]
|
||||
StatsFunsChainRulesCoreExt = "ChainRulesCore"
|
||||
StatsFunsInverseFunctionsExt = "InverseFunctions"
|
||||
|
||||
[deps.StatsFuns.weakdeps]
|
||||
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
|
||||
InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"
|
||||
|
||||
[[deps.StructArrays]]
|
||||
deps = ["Adapt", "DataAPI", "GPUArraysCore", "StaticArraysCore", "Tables"]
|
||||
git-tree-sha1 = "521a0e828e98bb69042fec1809c1b5a680eb7389"
|
||||
uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
|
||||
version = "0.6.15"
|
||||
|
||||
[[deps.StructTypes]]
|
||||
deps = ["Dates", "UUIDs"]
|
||||
git-tree-sha1 = "ca4bccb03acf9faaf4137a9abc1881ed1841aa70"
|
||||
uuid = "856f2bd8-1eba-4b0a-8007-ebc267875bd4"
|
||||
version = "1.10.0"
|
||||
|
||||
[[deps.SuiteSparse]]
|
||||
deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
|
||||
uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
|
||||
|
||||
[[deps.SuiteSparse_jll]]
|
||||
deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"]
|
||||
uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c"
|
||||
version = "5.10.1+6"
|
||||
|
||||
[[deps.TOML]]
|
||||
deps = ["Dates"]
|
||||
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
|
||||
version = "1.0.3"
|
||||
|
||||
[[deps.TableTraits]]
|
||||
deps = ["IteratorInterfaceExtensions"]
|
||||
git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
|
||||
uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
|
||||
version = "1.0.1"
|
||||
|
||||
[[deps.Tables]]
|
||||
deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits", "Test"]
|
||||
git-tree-sha1 = "1544b926975372da01227b382066ab70e574a3ec"
|
||||
uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
|
||||
version = "1.10.1"
|
||||
|
||||
[[deps.Tar]]
|
||||
deps = ["ArgTools", "SHA"]
|
||||
uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"
|
||||
version = "1.10.0"
|
||||
|
||||
[[deps.Test]]
|
||||
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
|
||||
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
|
||||
|
||||
[[deps.TimerOutputs]]
|
||||
deps = ["ExprTools", "Printf"]
|
||||
git-tree-sha1 = "f548a9e9c490030e545f72074a41edfd0e5bcdd7"
|
||||
uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
|
||||
version = "0.5.23"
|
||||
|
||||
[[deps.Transducers]]
|
||||
deps = ["Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"]
|
||||
git-tree-sha1 = "a66fb81baec325cf6ccafa243af573b031e87b00"
|
||||
uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999"
|
||||
version = "0.4.77"
|
||||
|
||||
[deps.Transducers.extensions]
|
||||
TransducersBlockArraysExt = "BlockArrays"
|
||||
TransducersDataFramesExt = "DataFrames"
|
||||
TransducersLazyArraysExt = "LazyArrays"
|
||||
TransducersOnlineStatsBaseExt = "OnlineStatsBase"
|
||||
TransducersReferenceablesExt = "Referenceables"
|
||||
|
||||
[deps.Transducers.weakdeps]
|
||||
BlockArrays = "8e7c35d0-a365-5155-bbbb-fb81a777f24e"
|
||||
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
|
||||
LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02"
|
||||
OnlineStatsBase = "925886fa-5bf2-5e8e-b522-a9147a512338"
|
||||
Referenceables = "42d2dcc6-99eb-4e98-b66c-637b7d73030e"
|
||||
|
||||
[[deps.UUIDs]]
|
||||
deps = ["Random", "SHA"]
|
||||
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
|
||||
|
||||
[[deps.Unicode]]
|
||||
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
|
||||
|
||||
[[deps.UnsafeAtomics]]
|
||||
git-tree-sha1 = "6331ac3440856ea1988316b46045303bef658278"
|
||||
uuid = "013be700-e6cd-48c3-b4a1-df204f14c38f"
|
||||
version = "0.2.1"
|
||||
|
||||
[[deps.UnsafeAtomicsLLVM]]
|
||||
deps = ["LLVM", "UnsafeAtomics"]
|
||||
git-tree-sha1 = "ea37e6066bf194ab78f4e747f5245261f17a7175"
|
||||
uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249"
|
||||
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|
||||
|
||||
[[deps.Zlib_jll]]
|
||||
deps = ["Libdl"]
|
||||
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
|
||||
version = "1.2.13+0"
|
||||
|
||||
[[deps.Zygote]]
|
||||
deps = ["AbstractFFTs", "ChainRules", "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.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"
|
||||
14
previousVersion/0.0.2/Project.toml
Normal file
14
previousVersion/0.0.2/Project.toml
Normal file
@@ -0,0 +1,14 @@
|
||||
name = "IronpenGPU"
|
||||
uuid = "3d5396ea-818e-43fc-a9d3-164248e840cd"
|
||||
authors = ["ton <narawat@gmail.com>"]
|
||||
version = "0.1.0"
|
||||
|
||||
[deps]
|
||||
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
|
||||
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"
|
||||
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
|
||||
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
|
||||
85
previousVersion/0.0.2/src/IronpenGPU.jl
Normal file
85
previousVersion/0.0.2/src/IronpenGPU.jl
Normal file
@@ -0,0 +1,85 @@
|
||||
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 0.0.2
|
||||
Todo:
|
||||
[] use partial error update for computeNeuron
|
||||
[] use integrate_neuron_params synapticConnectionPercent LESS THAN 100%
|
||||
[2] 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: 0.0.1
|
||||
- knowledgeFn in GPU format
|
||||
|
||||
All features
|
||||
|
||||
"""
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module IronpenGPU
|
||||
809
previousVersion/0.0.2/src/forward.jl
Normal file
809
previousVersion/0.0.2/src/forward.jl
Normal file
@@ -0,0 +1,809 @@
|
||||
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
|
||||
|
||||
#TODO time step forward
|
||||
if view(kfn.learningStage, 1)[1] == 1
|
||||
# reset learning params
|
||||
kfn.lif_vt .= 0
|
||||
kfn.lif_wRecChange .= 0
|
||||
kfn.lif_epsilonRec .= 0
|
||||
|
||||
kfn.alif_vt .= 0
|
||||
kfn.alif_epsilonRec .= 0
|
||||
kfn.alif_wRecChange .= 0
|
||||
|
||||
kfn.on_vt .= 0
|
||||
kfn.on_epsilonRec .= 0
|
||||
kfn.on_wOutChange .= 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)))
|
||||
|
||||
# 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
|
||||
|
||||
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,)
|
||||
|
||||
# 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
|
||||
|
||||
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_epsilonRecA,
|
||||
kfn.alif_a,
|
||||
kfn.alif_avth,
|
||||
kfn.alif_beta,
|
||||
kfn.alif_rho,)
|
||||
|
||||
# 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)))
|
||||
|
||||
# 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,)
|
||||
# error("DEBUG -> kfn forward")
|
||||
logit = reshape(kfn.on_zt, (size(input, 1), :))
|
||||
|
||||
return logit,
|
||||
kfn.zit
|
||||
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
|
||||
|
||||
# gpu launcher
|
||||
function lifForward( lif_zit::CuArray,
|
||||
lif_wRec::CuArray,
|
||||
lif_vt::CuArray,
|
||||
lif_vth::CuArray,
|
||||
lif_vRest::CuArray,
|
||||
lif_zt::CuArray,
|
||||
lif_alpha::CuArray,
|
||||
lif_phi::CuArray,
|
||||
lif_epsilonRec::CuArray,
|
||||
lif_refractoryCounter::CuArray,
|
||||
lif_refractoryDuration::CuArray,
|
||||
lif_gammaPd::CuArray,
|
||||
lif_firingCounter::CuArray,
|
||||
lif_recSignal::CuArray,)
|
||||
|
||||
kernel = @cuda launch=false lifForward( lif_zit,
|
||||
lif_wRec,
|
||||
lif_vt,
|
||||
lif_vth,
|
||||
lif_vRest,
|
||||
lif_zt,
|
||||
lif_alpha,
|
||||
lif_phi,
|
||||
lif_epsilonRec,
|
||||
lif_refractoryCounter,
|
||||
lif_refractoryDuration,
|
||||
lif_gammaPd,
|
||||
lif_firingCounter,
|
||||
lif_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(lif_wRec)
|
||||
|
||||
blocks = cld(totalThreads, threads)
|
||||
# println("launching gpu kernel")
|
||||
CUDA.@sync begin
|
||||
kernel( lif_zit,
|
||||
lif_wRec,
|
||||
lif_vt,
|
||||
lif_vth,
|
||||
lif_vRest,
|
||||
lif_zt,
|
||||
lif_alpha,
|
||||
lif_phi,
|
||||
lif_epsilonRec,
|
||||
lif_refractoryCounter,
|
||||
lif_refractoryDuration,
|
||||
lif_gammaPd,
|
||||
lif_firingCounter,
|
||||
lif_recSignal,
|
||||
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,
|
||||
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")
|
||||
|
||||
refractoryCounter[i] -= 1
|
||||
|
||||
if refractoryCounter[i] > 0 # refractory period is active
|
||||
refractoryCounter[i] -= 1
|
||||
zt[i] = 0
|
||||
vt[i] = alpha[i] * vt[i]
|
||||
phi[i] = 0
|
||||
|
||||
# compute epsilonRec
|
||||
epsilonRec[i] = (alpha[i] * epsilonRec[i]) + zit[i]
|
||||
|
||||
else # refractory period is inactive
|
||||
recSignal[i] = zit[i] * wRec[i]
|
||||
vt[i] = (alpha[i] * vt[i]) + sum(@view(recSignal[:,:,i3,i4]))
|
||||
|
||||
# fires if membrane potential exceed threshold
|
||||
if vt[i] > vth[i]
|
||||
zt[i] = 1
|
||||
refractoryCounter[i] = refractoryDuration[i]
|
||||
firingCounter[i] += 1
|
||||
vt[i] = vRest[i]
|
||||
else
|
||||
zt[i] = 0
|
||||
end
|
||||
|
||||
# compute phi, there is a difference from lif formula
|
||||
phi[i] = (gammaPd[i] / vth[i]) * max(0, 1 - ((vt[i] - vth[i]) / vth[i]))
|
||||
|
||||
# compute epsilonRec
|
||||
epsilonRec[i] = (alpha[i] * epsilonRec[i]) + zit[i]
|
||||
end
|
||||
end
|
||||
return nothing
|
||||
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
|
||||
|
||||
# gpu launcher
|
||||
function alifForward( alif_zit::CuArray,
|
||||
alif_wRec::CuArray,
|
||||
alif_vt::CuArray,
|
||||
alif_vth::CuArray,
|
||||
alif_vRest::CuArray,
|
||||
alif_zt::CuArray,
|
||||
alif_alpha::CuArray,
|
||||
alif_phi::CuArray,
|
||||
alif_epsilonRec::CuArray,
|
||||
alif_refractoryCounter::CuArray,
|
||||
alif_refractoryDuration::CuArray,
|
||||
alif_gammaPd::CuArray,
|
||||
alif_firingCounter::CuArray,
|
||||
alif_recSignal::CuArray,
|
||||
alif_epsilonRecA::CuArray,
|
||||
alif_a::CuArray,
|
||||
alif_avth::CuArray,
|
||||
alif_beta::CuArray,
|
||||
alif_rho::CuArray,
|
||||
)
|
||||
|
||||
kernel = @cuda launch=false alifForward( alif_zit,
|
||||
alif_wRec,
|
||||
alif_vt,
|
||||
alif_vth,
|
||||
alif_vRest,
|
||||
alif_zt,
|
||||
alif_alpha,
|
||||
alif_phi,
|
||||
alif_epsilonRec,
|
||||
alif_refractoryCounter,
|
||||
alif_refractoryDuration,
|
||||
alif_gammaPd,
|
||||
alif_firingCounter,
|
||||
alif_recSignal,
|
||||
alif_epsilonRecA,
|
||||
alif_a,
|
||||
alif_avth,
|
||||
alif_beta,
|
||||
alif_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(alif_wRec)
|
||||
|
||||
blocks = cld(totalThreads, threads)
|
||||
# println("launching gpu kernel")
|
||||
CUDA.@sync begin
|
||||
kernel( alif_zit,
|
||||
alif_wRec,
|
||||
alif_vt,
|
||||
alif_vth,
|
||||
alif_vRest,
|
||||
alif_zt,
|
||||
alif_alpha,
|
||||
alif_phi,
|
||||
alif_epsilonRec,
|
||||
alif_refractoryCounter,
|
||||
alif_refractoryDuration,
|
||||
alif_gammaPd,
|
||||
alif_firingCounter,
|
||||
alif_recSignal,
|
||||
alif_epsilonRecA,
|
||||
alif_a,
|
||||
alif_avth,
|
||||
alif_beta,
|
||||
alif_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,
|
||||
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")
|
||||
|
||||
refractoryCounter[i] -= 1
|
||||
|
||||
if refractoryCounter[i] > 0 # refractory period is active
|
||||
refractoryCounter[i] -= 1
|
||||
zt[i] = 0
|
||||
vt[i] = alpha[i] * vt[i]
|
||||
phi[i] = 0
|
||||
a[i] = rho[i] * a[i]
|
||||
|
||||
# compute epsilonRec
|
||||
epsilonRec[i] = (alpha[i] * epsilonRec[i]) + zit[i]
|
||||
|
||||
# compute epsilonRecA
|
||||
epsilonRecA[i] = (phi[i] * epsilonRec[i]) +
|
||||
((rho[i] - (phi[i] * beta[i])) * epsilonRecA[i])
|
||||
|
||||
# compute avth
|
||||
avth[i] = vth[i] + (beta[i] * a[i])
|
||||
|
||||
else # refractory period is inactive
|
||||
recSignal[i] = zit[i] * wRec[i]
|
||||
vt[i] = (alpha[i] * vt[i]) + sum(@view(recSignal[:,:,i3,i4]))
|
||||
|
||||
# compute avth
|
||||
avth[i] = vth[i] + (beta[i] * a[i])
|
||||
|
||||
# fires if membrane potential exceed threshold
|
||||
if vt[i] > avth[i]
|
||||
zt[i] = 1
|
||||
refractoryCounter[i] = refractoryDuration[i]
|
||||
firingCounter[i] += 1
|
||||
vt[i] = vRest[i]
|
||||
a[i] = (rho[i] * a[i]) + 1
|
||||
else
|
||||
zt[i] = 0
|
||||
a[i] = (rho[i] * a[i])
|
||||
end
|
||||
|
||||
# compute phi, there is a difference from alif formula
|
||||
phi[i] = (gammaPd[i] / vth[i]) * max(0, 1 - ((vt[i] - vth[i]) / vth[i]))
|
||||
|
||||
# compute epsilonRec
|
||||
epsilonRec[i] = (alpha[i] * epsilonRec[i]) + zit[i]
|
||||
|
||||
# compute epsilonRecA
|
||||
epsilonRecA[i] = (phi[i] * epsilonRec[i]) +
|
||||
((rho[i] - (phi[i] * beta[i])) * epsilonRecA[i])
|
||||
end
|
||||
end
|
||||
return nothing
|
||||
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
|
||||
|
||||
# gpu launcher
|
||||
function onForward( on_zit::CuArray,
|
||||
on_wOut::CuArray,
|
||||
on_vt::CuArray,
|
||||
on_vth::CuArray,
|
||||
on_vRest::CuArray,
|
||||
on_zt::CuArray,
|
||||
on_alpha::CuArray,
|
||||
on_phi::CuArray,
|
||||
on_epsilonRec::CuArray,
|
||||
on_refractoryCounter::CuArray,
|
||||
on_refractoryDuration::CuArray,
|
||||
on_gammaPd::CuArray,
|
||||
on_firingCounter::CuArray,
|
||||
on_recSignal::CuArray)
|
||||
|
||||
kernel = @cuda launch=false onForward( on_zit,
|
||||
on_wOut,
|
||||
on_vt,
|
||||
on_vth,
|
||||
on_vRest,
|
||||
on_zt,
|
||||
on_alpha,
|
||||
on_phi,
|
||||
on_epsilonRec,
|
||||
on_refractoryCounter,
|
||||
on_refractoryDuration,
|
||||
on_gammaPd,
|
||||
on_firingCounter,
|
||||
on_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(on_wOut)
|
||||
|
||||
blocks = cld(totalThreads, threads)
|
||||
# println("launching gpu kernel")
|
||||
CUDA.@sync begin
|
||||
kernel( on_zit,
|
||||
on_wOut,
|
||||
on_vt,
|
||||
on_vth,
|
||||
on_vRest,
|
||||
on_zt,
|
||||
on_alpha,
|
||||
on_phi,
|
||||
on_epsilonRec,
|
||||
on_refractoryCounter,
|
||||
on_refractoryDuration,
|
||||
on_gammaPd,
|
||||
on_firingCounter,
|
||||
on_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")
|
||||
|
||||
refractoryCounter[i] -= 1
|
||||
|
||||
if refractoryCounter[i] > 0 # refractory period is active
|
||||
refractoryCounter[i] -= 1
|
||||
zt[i] = 0
|
||||
vt[i] = alpha[i] * vt[i]
|
||||
phi[i] = 0
|
||||
|
||||
# compute epsilonRec
|
||||
epsilonRec[i] = (alpha[i] * epsilonRec[i]) + zit[i]
|
||||
|
||||
else # refractory period is inactive
|
||||
recSignal[i] = zit[i] * wOut[i]
|
||||
vt[i] = (alpha[i] * vt[i]) + sum(@view(recSignal[:,:,i3,i4]))
|
||||
|
||||
# fires if membrane potential exceed threshold
|
||||
if vt[i] > vth[i]
|
||||
zt[i] = 1
|
||||
refractoryCounter[i] = refractoryDuration[i]
|
||||
firingCounter[i] += 1
|
||||
vt[i] = vRest[i]
|
||||
else
|
||||
zt[i] = 0
|
||||
end
|
||||
|
||||
# compute phi, there is a difference from on formula
|
||||
phi[i] = (gammaPd[i] / vth[i]) * max(0, 1 - ((vt[i] - vth[i]) / vth[i]))
|
||||
|
||||
# compute epsilonRec
|
||||
epsilonRec[i] = (alpha[i] * epsilonRec[i]) + zit[i]
|
||||
end
|
||||
end
|
||||
return nothing
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module
|
||||
87
previousVersion/0.0.2/src/interface.jl
Normal file
87
previousVersion/0.0.2/src/interface.jl
Normal file
@@ -0,0 +1,87 @@
|
||||
module interface
|
||||
|
||||
|
||||
# export
|
||||
|
||||
# using Flux, CUDA
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module
|
||||
312
previousVersion/0.0.2/src/learn.jl
Normal file
312
previousVersion/0.0.2/src/learn.jl
Normal file
@@ -0,0 +1,312 @@
|
||||
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.lif_phi,
|
||||
kfn.lif_epsilonRec,
|
||||
kfn.lif_eta,
|
||||
kfn.lif_eRec,
|
||||
kfn.lif_wRec,
|
||||
kfn.lif_wRecChange,
|
||||
kfn.on_wOut,
|
||||
kfn.lif_arrayProjection4d,
|
||||
kfn.lif_error,
|
||||
modelError)
|
||||
|
||||
alifComputeParamsChange!(kfn.alif_phi,
|
||||
kfn.alif_epsilonRec,
|
||||
kfn.alif_eta,
|
||||
kfn.alif_eRec,
|
||||
kfn.alif_wRec,
|
||||
kfn.alif_wRecChange,
|
||||
kfn.on_wOut,
|
||||
kfn.alif_arrayProjection4d,
|
||||
kfn.alif_error,
|
||||
modelError,
|
||||
kfn.alif_beta)
|
||||
|
||||
onComputeParamsChange!(kfn.on_phi,
|
||||
kfn.on_epsilonRec,
|
||||
kfn.on_eta,
|
||||
kfn.on_eRec,
|
||||
kfn.on_wOut,
|
||||
kfn.on_wOutChange,
|
||||
outputError)
|
||||
# error("DEBUG -> kfn compute_paramsChange! $(Dates.now())")
|
||||
end
|
||||
|
||||
function lifComputeParamsChange!( phi::CuArray,
|
||||
epsilonRec::CuArray,
|
||||
eta::CuArray,
|
||||
eRec::CuArray,
|
||||
wRec::CuArray,
|
||||
wRecChange::CuArray,
|
||||
wOut::CuArray,
|
||||
arrayProjection4d::CuArray,
|
||||
nError::CuArray,
|
||||
modelError::CuArray)
|
||||
wOutSum = sum(wOut, dims=3) .* arrayProjection4d
|
||||
|
||||
# nError a.k.a. learning signal use dopamine concept,
|
||||
# this neuron receive summed error signal (modelError)
|
||||
nError .= (modelError .* arrayProjection4d) .* wOutSum
|
||||
eRec .= phi .* epsilonRec
|
||||
# GeneralUtils.isNotEqual(wRec, 0) is a subscribe filter use to filter out non-subscribed wRecChange
|
||||
wRecChange .+= ((-1 .* eta) .* nError .* eRec) .* GeneralUtils.isNotEqual.(wRec, 0)
|
||||
# error("DEBUG -> lifComputeParamsChange! $(Dates.now())")
|
||||
end
|
||||
|
||||
function alifComputeParamsChange!( phi::CuArray,
|
||||
epsilonRec::CuArray,
|
||||
eta::CuArray,
|
||||
eRec::CuArray,
|
||||
wRec::CuArray,
|
||||
wRecChange::CuArray,
|
||||
wOut::CuArray,
|
||||
arrayProjection4d::CuArray,
|
||||
nError::CuArray,
|
||||
modelError::CuArray,
|
||||
beta::CuArray)
|
||||
|
||||
wOutSum = sum(wOut, dims=3) .* arrayProjection4d
|
||||
|
||||
# nError a.k.a. learning signal use dopamine concept,
|
||||
# this neuron receive summed error signal (modelError)
|
||||
nError .= (modelError .* arrayProjection4d) .* wOutSum
|
||||
eRec .= (phi .* epsilonRec) .+ (phi .* epsilonRec .* beta)
|
||||
|
||||
# GeneralUtils.isNotEqual(wRec, 0) is a subscribe filter use to filter out non-subscribed wRecChange
|
||||
wRecChange .+= ((-1 .* eta) .* nError .* eRec) .* GeneralUtils.isNotEqual.(wRec, 0)
|
||||
# error("DEBUG -> alifComputeParamsChange! $(Dates.now())")
|
||||
end
|
||||
|
||||
function onComputeParamsChange!(phi::CuArray,
|
||||
epsilonRec::CuArray,
|
||||
eta::CuArray,
|
||||
eRec::CuArray,
|
||||
wOut::CuArray,
|
||||
wOutChange::CuArray,
|
||||
outputError::CuArray # outputError is output neuron's error
|
||||
)
|
||||
|
||||
# nError a.k.a. learning signal use dopamine concept,
|
||||
# this neuron receive summed error signal (modelError)
|
||||
eRec .= (phi .* epsilonRec) .* reshape(outputError, (1, 1, :, size(epsilonRec, 4)))
|
||||
|
||||
# GeneralUtils.isNotEqual(wRec, 0) is a subscribe filter use to filter out non-subscribed wRecChange
|
||||
wOutChange .+= ((-1 .* eta) .* eRec) .* GeneralUtils.isNotEqual.(wOut, 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)
|
||||
error("DEBUG -> lifComputeParamsChange! $(Dates.now())")
|
||||
# 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)
|
||||
# lif learn
|
||||
lifLearn!(kfn.lif_wRec,
|
||||
kfn.lif_wRecChange,
|
||||
kfn.lif_arrayProjection4d)
|
||||
|
||||
# alif learn
|
||||
alifLearn!(kfn.alif_wRec,
|
||||
kfn.alif_wRecChange,
|
||||
kfn.alif_arrayProjection4d)
|
||||
|
||||
# on learn
|
||||
onLearn!(kfn.on_wOut,
|
||||
kfn.on_wOutChange,
|
||||
kfn.on_arrayProjection4d)
|
||||
|
||||
# wOut decay
|
||||
kfn.on_wOut .*= 0.0001
|
||||
|
||||
# wrap up learning session
|
||||
if kfn.learningStage == [3]
|
||||
kfn.learningStage = [0]
|
||||
end
|
||||
# error("DEBUG -> kfn learn! $(Dates.now())")
|
||||
end
|
||||
|
||||
function lifLearn!(wRec,
|
||||
wRecChange,
|
||||
arrayProjection4d)
|
||||
|
||||
# merge learning weight with average learning weight
|
||||
wRec .+= (sum(wRecChange) ./ (size(wRec, 4))) .* arrayProjection4d
|
||||
|
||||
#TODO synaptic strength
|
||||
|
||||
#TODO neuroplasticity
|
||||
|
||||
end
|
||||
|
||||
function alifLearn!(wRec,
|
||||
wRecChange,
|
||||
arrayProjection4d)
|
||||
# merge learning weight
|
||||
wRec .+= (sum(wRecChange) ./ (size(wRec, 4))) .* arrayProjection4d
|
||||
|
||||
#TODO synaptic strength
|
||||
|
||||
#TODO neuroplasticity
|
||||
|
||||
end
|
||||
|
||||
function onLearn!(wOut,
|
||||
wOutChange,
|
||||
arrayProjection4d)
|
||||
# merge learning weight
|
||||
wOut .+= (sum(wOutChange) ./ (size(wOut, 4))) .* arrayProjection4d
|
||||
|
||||
#TODO synaptic strength
|
||||
|
||||
#TODO neuroplasticity
|
||||
|
||||
end
|
||||
|
||||
#TODO voltage regulator
|
||||
|
||||
#TODO frequency regulator
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module
|
||||
77
previousVersion/0.0.2/src/snnUtil.jl
Normal file
77
previousVersion/0.0.2/src/snnUtil.jl
Normal file
@@ -0,0 +1,77 @@
|
||||
module snnUtil
|
||||
|
||||
export refractoryStatus!
|
||||
|
||||
# using
|
||||
|
||||
#------------------------------------------------------------------------------------------------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
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module
|
||||
427
previousVersion/0.0.2/src/type.jl
Normal file
427
previousVersion/0.0.2/src/type.jl
Normal file
@@ -0,0 +1,427 @@
|
||||
module type
|
||||
|
||||
export
|
||||
# struct
|
||||
kfn_1
|
||||
|
||||
# function
|
||||
|
||||
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
|
||||
zit::Union{AbstractArray, Nothing} = nothing # 3D activation matrix
|
||||
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_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
|
||||
|
||||
# pre-allocation array
|
||||
lif_arrayProjection4d::Union{AbstractArray, Nothing} = nothing # use to project 3d array to 4d
|
||||
lif_recSignal::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_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
|
||||
|
||||
# pre-allocation array
|
||||
alif_arrayProjection4d::Union{AbstractArray, Nothing} = nothing # use to project 3d array to 4d
|
||||
alif_recSignal::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_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, col, batch = kfn.params[:inputPort][:signal][:numbers] # z-axis represent signal batch number
|
||||
# row += kfn.params[:inputPort][:noise][:numbers][1]
|
||||
col += kfn.params[:inputPort][:noise][:numbers][2]
|
||||
col += kfn.params[:computeNeuron][:lif][:numbers][2]
|
||||
col += kfn.params[:computeNeuron][:alif][:numbers][2]
|
||||
|
||||
# activation matrix
|
||||
kfn.zit = zeros(row, col, batch) |> device
|
||||
kfn.modelError = zeros(1) |> device
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# LIF config #
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# In 3D LIF matrix, z-axis represent each neuron while each 2D slice represent that neuron's
|
||||
# synaptic subscription to other neurons (via activation matrix)
|
||||
n = kfn.params[:computeNeuron][:lif][:numbers][1] * kfn.params[:computeNeuron][:lif][:numbers][2]
|
||||
|
||||
# subscription
|
||||
w = zeros(row, col, n)
|
||||
synapticConnectionPercent = kfn.params[:computeNeuron][:lif][:params][:synapticConnectionPercent]
|
||||
synapticConnection = Int(floor(row*col * synapticConnectionPercent/100))
|
||||
for slice in eachslice(w, dims=3)
|
||||
pool = shuffle!([1:row*col...])[1:synapticConnection]
|
||||
for i in pool
|
||||
slice[i] = randn()/10 # assign weight to synaptic connection
|
||||
end
|
||||
end
|
||||
# project 3D w into 4D kfn.lif_wRec (row, col, n, batch)
|
||||
kfn.lif_wRec = reshape(w, (row, col, n, 1)) .* ones(row, col, n, batch) |> device
|
||||
kfn.lif_zit = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
kfn.lif_vt = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
kfn.lif_vth = (similar(kfn.lif_wRec) .= 1) |> device
|
||||
kfn.lif_vRest = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
kfn.lif_zt = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_zt4d = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
kfn.lif_refractoryCounter = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
kfn.lif_refractoryDuration = (similar(kfn.lif_wRec) .= 3) |> device
|
||||
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))) |> device
|
||||
kfn.lif_phi = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
kfn.lif_epsilonRec = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
kfn.lif_eRec = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
kfn.lif_eta = (similar(kfn.lif_wRec) .= 0.001) |> device
|
||||
kfn.lif_gammaPd = (similar(kfn.lif_wRec) .= 0.3) |> device
|
||||
kfn.lif_wRecChange = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
kfn.lif_error = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
|
||||
kfn.lif_firingCounter = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
|
||||
kfn.lif_arrayProjection4d = (similar(kfn.lif_wRec) .= 1) |> device
|
||||
kfn.lif_recSignal = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
# kfn.lif_decayed_epsilonRec = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
# kfn.lif_vt_diff_vth = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
# kfn.lif_vt_diff_vth_div_vth = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
# kfn.lif_gammaPd_div_vth = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
# kfn.lif_phiActivation = (similar(kfn.lif_wRec) .= 0) |> device
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# ALIF config #
|
||||
# ---------------------------------------------------------------------------- #
|
||||
n = kfn.params[:computeNeuron][:alif][:numbers][1] * kfn.params[:computeNeuron][:alif][:numbers][2]
|
||||
|
||||
# subscription
|
||||
w = zeros(row, col, n)
|
||||
synapticConnectionPercent = kfn.params[:computeNeuron][:alif][:params][:synapticConnectionPercent]
|
||||
synapticConnection = Int(floor(row*col * synapticConnectionPercent/100))
|
||||
for slice in eachslice(w, dims=3)
|
||||
pool = shuffle!([1:row*col...])[1:synapticConnection]
|
||||
for i in pool
|
||||
slice[i] = randn()/10 # assign weight to synaptic connection
|
||||
end
|
||||
end
|
||||
# project 3D w into 4D kfn.alif_wRec
|
||||
kfn.alif_wRec = reshape(w, (row, col, n, 1)) .* ones(row, col, n, batch) |> device
|
||||
kfn.alif_zit = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
kfn.alif_vt = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
kfn.alif_vth = (similar(kfn.alif_wRec) .= 1) |> device
|
||||
kfn.alif_vRest = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
kfn.alif_zt = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_zt4d = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
kfn.alif_refractoryCounter = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
kfn.alif_refractoryDuration = (similar(kfn.alif_wRec) .= 3) |> device
|
||||
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))) |> device
|
||||
kfn.alif_phi = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
kfn.alif_epsilonRec = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
kfn.alif_eRec = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
kfn.alif_eta = (similar(kfn.alif_wRec) .= 0.001) |> device
|
||||
kfn.alif_gammaPd = (similar(kfn.alif_wRec) .= 0.3) |> device
|
||||
kfn.alif_wRecChange = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
kfn.alif_error = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
|
||||
kfn.alif_firingCounter = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
|
||||
kfn.alif_arrayProjection4d = (similar(kfn.alif_wRec) .= 1) |> device
|
||||
kfn.alif_recSignal = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
# kfn.alif_decayed_epsilonRec = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
# kfn.alif_vt_diff_vth = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
# kfn.alif_vt_diff_vth_div_vth = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
# kfn.alif_gammaPd_div_vth = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
# kfn.alif_phiActivation = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
|
||||
# alif specific variables
|
||||
kfn.alif_epsilonRecA = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
kfn.alif_avth = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
kfn.alif_a = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
kfn.alif_beta = (similar(kfn.alif_wRec) .= 0.07) |> device
|
||||
kfn.alif_tau_a = 100.0
|
||||
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) |> device
|
||||
# kfn.alif_phi_x_beta = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
# kfn.alif_rho_diff_phi_x_beta = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
# kfn.alif_rho_div_phi_x_beta_x_epsilonRecA = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
# kfn.alif_beta_x_a = (similar(kfn.alif_wRec) .= 0) |> device
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# output config #
|
||||
# ---------------------------------------------------------------------------- #
|
||||
n = kfn.params[:outputPort][:numbers][1] * kfn.params[:outputPort][:numbers][2]
|
||||
|
||||
# subscription
|
||||
w = zeros(row, col, n)
|
||||
synapticConnectionPercent = kfn.params[:computeNeuron][:lif][:params][:synapticConnectionPercent]
|
||||
synapticConnection = Int(floor(row*col * synapticConnectionPercent/100))
|
||||
for slice in eachslice(w, dims=3)
|
||||
pool = shuffle!([1:row*col...])[1:synapticConnection]
|
||||
for i in pool
|
||||
slice[i] = randn()/10 # assign weight to synaptic connection
|
||||
end
|
||||
end
|
||||
# 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) |> device
|
||||
kfn.on_vt = (similar(kfn.on_wOut) .= 0) |> device
|
||||
kfn.on_vth = (similar(kfn.on_wOut) .= 1) |> device
|
||||
kfn.on_vRest = (similar(kfn.on_wOut) .= 0) |> device
|
||||
kfn.on_zt = zeros(1, 1, n, batch) |> device
|
||||
kfn.on_zt4d = (similar(kfn.on_wOut) .= 0) |> device
|
||||
kfn.on_refractoryCounter = (similar(kfn.on_wOut) .= 0) |> device
|
||||
kfn.on_refractoryDuration = (similar(kfn.on_wOut) .= 0) |> device
|
||||
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))) |> device
|
||||
kfn.on_phi = (similar(kfn.on_wOut) .= 0) |> device
|
||||
kfn.on_epsilonRec = (similar(kfn.on_wOut) .= 0) |> device
|
||||
kfn.on_eRec = (similar(kfn.on_wOut) .= 0) |> device
|
||||
kfn.on_eta = (similar(kfn.on_wOut) .= 0.001) |> device
|
||||
kfn.on_gammaPd = (similar(kfn.on_wOut) .= 0.3) |> device
|
||||
kfn.on_wOutChange = (similar(kfn.on_wOut) .= 0) |> device
|
||||
kfn.on_error = (similar(kfn.on_wOut) .= 0) |> device
|
||||
|
||||
kfn.on_firingCounter = (similar(kfn.on_wOut) .= 0) |> device
|
||||
|
||||
kfn.on_arrayProjection4d = (similar(kfn.on_wOut) .= 1) |> device
|
||||
kfn.on_recSignal = (similar(kfn.on_wOut) .= 0) |> device
|
||||
|
||||
|
||||
|
||||
kfn.outputError = zeros(n, batch) |> device
|
||||
|
||||
|
||||
|
||||
|
||||
# kfn.on_decayed_epsilonRec = (similar(kfn.on_wOut) .= 0 |> device
|
||||
# kfn.on_vt_diff_vth = (similar(kfn.on_wOut) .= 0 |> device
|
||||
# kfn.on_vt_diff_vth_div_vth = (similar(kfn.on_wOut) .= 0 |> device
|
||||
# kfn.on_gammaPd_div_vth = (similar(kfn.on_wOut) .= 0 |> device
|
||||
# kfn.on_phiActivation = (similar(kfn.on_wOut) .= 0 |> device
|
||||
|
||||
# kfn.on_zit = zeros(row, col, n, batch) |> device
|
||||
# kfn.on_vt = zeros(1, 1, n, batch) |> device
|
||||
# kfn.on_vth = ones(1, 1, n, batch) |> device
|
||||
# kfn.on_vRest = zeros(1, 1, n, batch) |> device
|
||||
# # kfn.on_zt = zeros(1, 1, n, batch) |> device
|
||||
# kfn.on_zt4d = zeros(1, 1, n, batch) |> device
|
||||
# kfn.on_refractoryCounter = zeros(1, 1, n, batch) |> device
|
||||
# kfn.on_refractoryDuration = ones(1, 1, n, batch) .* 0 |> device
|
||||
# kfn.on_delta = 1.0
|
||||
# kfn.on_tau_m = 20.0
|
||||
# kfn.on_alpha = ones(1, 1, n, batch) .* (exp(-kfn.on_delta / kfn.on_tau_m)) |> device
|
||||
# kfn.on_phi = zeros(1, 1, n, batch) |> device
|
||||
# kfn.on_epsilonRec = zeros(row, col, n, batch) |> device
|
||||
# # kfn.on_eRec = zeros(row, col, n, batch)
|
||||
# kfn.on_eta = zeros(1, 1, n, batch) |> device
|
||||
# kfn.on_gammaPd = zeros(1, 1, n, batch) .* 0.3 |> device
|
||||
# kfn.on_wOutChange = zeros(row, col, n, batch) |> device
|
||||
# # kfn.on_b = randn(1, 1, n, batch) |> device
|
||||
# # kfn.on_bChange = randn(1, 1, n, batch) |> device
|
||||
|
||||
# kfn.on_firingCounter = zeros(1, 1, n, batch) |> device
|
||||
# kfn.on_arraySize = [row, col, n, batch] |> device
|
||||
# kfn.on_arrayProjection4d = ones(row, col, n, batch) |> device
|
||||
|
||||
# # subscription
|
||||
# w = zeros(row, col, n)
|
||||
# synapticConnectionPercent = kfn.params[:outputPort][:params][:synapticConnectionPercent]
|
||||
# synapticConnection = Int(floor(row*col * synapticConnectionPercent/100))
|
||||
# for slice in eachslice(w, dims=3)
|
||||
# pool = shuffle!([1:row*col...])[1:synapticConnection]
|
||||
# for i in pool
|
||||
# slice[i] = randn()/10 # assign weight to synaptic connection
|
||||
# end
|
||||
# end
|
||||
# # project 3D w into 4D kfn.on_wOut
|
||||
# kfn.on_wOut = reshape(w, (row, col, n, 1)) .* ones(row, col, n, batch) |> device
|
||||
|
||||
|
||||
|
||||
return kfn
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module
|
||||
Reference in New Issue
Block a user