version 0.01
This commit is contained in:
911
previousVersion/0.0.1/Manifest.toml
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911
previousVersion/0.0.1/Manifest.toml
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# This file is machine-generated - editing it directly is not advised
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julia_version = "1.9.2"
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manifest_format = "2.0"
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project_hash = "1d38b0278f78d536c218e3a421dfd88a68063099"
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|
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[[deps.AbstractFFTs]]
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deps = ["LinearAlgebra"]
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version = "1.3.2"
|
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weakdeps = ["ChainRulesCore"]
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[deps.AbstractFFTs.extensions]
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AbstractFFTsChainRulesCoreExt = "ChainRulesCore"
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[[deps.Adapt]]
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deps = ["LinearAlgebra", "Requires"]
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[deps.Adapt.extensions]
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AdaptStaticArraysExt = "StaticArrays"
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[[deps.ArgCheck]]
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[[deps.Atomix]]
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[[deps.BFloat16s]]
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[[deps.BangBang]]
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[deps.BangBang.extensions]
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BangBangDataFramesExt = "DataFrames"
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BangBangStaticArraysExt = "StaticArrays"
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BangBangStructArraysExt = "StructArrays"
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BangBangTypedTablesExt = "TypedTables"
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[deps.BangBang.weakdeps]
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ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
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[[deps.Base64]]
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[[deps.Baselet]]
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[[deps.CEnum]]
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[[deps.CUDA]]
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[[deps.CUDA_Driver_jll]]
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[[deps.CUDA_Runtime_Discovery]]
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[[deps.CUDA_Runtime_jll]]
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[[deps.CUDNN_jll]]
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[[deps.Calculus]]
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||||
|
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[[deps.ChainRules]]
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deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "Statistics", "StructArrays"]
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[[deps.ChainRulesCore]]
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[[deps.CommonSubexpressions]]
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[[deps.Compat]]
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[deps.Compat.extensions]
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CompatLinearAlgebraExt = "LinearAlgebra"
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[[deps.CompilerSupportLibraries_jll]]
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[[deps.CompositionsBase]]
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||||
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||||
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[deps.CompositionsBase.weakdeps]
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||||
InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"
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[[deps.ConstructionBase]]
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||||
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[deps.ConstructionBase.extensions]
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ConstructionBaseStaticArraysExt = "StaticArrays"
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||||
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||||
[deps.ConstructionBase.weakdeps]
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[[deps.ContextVariablesX]]
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||||
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||||
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||||
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||||
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||||
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|
||||
|
||||
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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"
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LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02"
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OnlineStatsBase = "925886fa-5bf2-5e8e-b522-a9147a512338"
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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"
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||||
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|
||||
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"]
|
||||
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|
||||
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/0.0.1/Project.toml
Normal file
14
previousVersion/0.0.1/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"
|
||||
86
previousVersion/0.0.1/src/IronpenGPU.jl
Normal file
86
previousVersion/0.0.1/src/IronpenGPU.jl
Normal file
@@ -0,0 +1,86 @@
|
||||
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 0.0.1
|
||||
Todo:
|
||||
[*1] knowledgeFn in GPU format
|
||||
[] 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:
|
||||
-
|
||||
|
||||
All features
|
||||
|
||||
"""
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module IronpenGPU
|
||||
391
previousVersion/0.0.1/src/forward.jl
Normal file
391
previousVersion/0.0.1/src/forward.jl
Normal file
@@ -0,0 +1,391 @@
|
||||
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.learningStage = [2]
|
||||
end
|
||||
|
||||
# println(">>> input ", size(input))
|
||||
# println(">>> zit ", size(kfn.zit))
|
||||
# println(">>> lif_zit ", size(kfn.lif_zit))
|
||||
# println(">>> lif_recSignal ", size(kfn.lif_recSignal))
|
||||
# println(">>> lif_wRec ", size(kfn.lif_wRec))
|
||||
# println(">>> lif_refractoryCounter ", size(kfn.lif_refractoryCounter))
|
||||
# println(">>> lif_alpha ", size(kfn.lif_alpha))
|
||||
# println(">>> lif_vt0 ", size(kfn.lif_vt0))
|
||||
# println(">>> lif_vt0 sum ", sum(kfn.lif_vt0))
|
||||
|
||||
# pass input_data into input neuron.
|
||||
GeneralUtils.cartesianAssign!(kfn.zit, input)
|
||||
|
||||
lifForward( kfn.zit,
|
||||
kfn.lif_zit,
|
||||
kfn.lif_wRec,
|
||||
kfn.lif_vt0,
|
||||
kfn.lif_vt1,
|
||||
kfn.lif_vth,
|
||||
kfn.lif_vRest,
|
||||
kfn.lif_zt1,
|
||||
kfn.lif_alpha,
|
||||
kfn.lif_phi,
|
||||
kfn.lif_epsilonRec,
|
||||
kfn.lif_refractoryCounter,
|
||||
kfn.lif_refractoryDuration,
|
||||
kfn.lif_gammaPd,
|
||||
kfn.lif_firingCounter,
|
||||
kfn.lif_arraySize,
|
||||
kfn.lif_arrayProjection3DTo4D)
|
||||
|
||||
alifForward( kfn.zit,
|
||||
kfn.alif_zit,
|
||||
kfn.alif_wRec,
|
||||
kfn.alif_vt0,
|
||||
kfn.alif_vt1,
|
||||
kfn.alif_vth,
|
||||
kfn.alif_avth,
|
||||
kfn.alif_vRest,
|
||||
kfn.alif_zt1,
|
||||
kfn.alif_alpha,
|
||||
kfn.alif_phi,
|
||||
kfn.alif_epsilonRec,
|
||||
kfn.alif_epsilonRecA,
|
||||
kfn.alif_refractoryCounter,
|
||||
kfn.alif_refractoryDuration,
|
||||
kfn.alif_a,
|
||||
kfn.alif_beta,
|
||||
kfn.alif_rho,
|
||||
kfn.alif_gammaPd,
|
||||
kfn.alif_firingCounter)
|
||||
# error("DEBUG -> kfn forward")
|
||||
|
||||
|
||||
|
||||
# update activation matrix by concatenate (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_zt1, (size(input, 1), :, 1, size(input, 3))),
|
||||
reshape(kfn.alif_zt1, (size(input, 1), :, 1, size(input, 3))), dims=2)
|
||||
kfn.zit .= reshape(_zit, (size(input, 1), :, size(input, 3)))
|
||||
|
||||
# read out
|
||||
onForward( kfn.zit,
|
||||
kfn.on_zit,
|
||||
kfn.on_wOut,
|
||||
kfn.on_vt0,
|
||||
kfn.on_vt1,
|
||||
kfn.on_vth,
|
||||
kfn.on_vRest,
|
||||
kfn.on_zt1,
|
||||
kfn.on_alpha,
|
||||
kfn.on_phi,
|
||||
kfn.on_epsilonRec,
|
||||
kfn.on_refractoryCounter,
|
||||
kfn.on_refractoryDuration,
|
||||
kfn.on_gammaPd,
|
||||
kfn.on_firingCounter)
|
||||
|
||||
return reshape(kfn.on_zt1, (size(input, 1), :)),
|
||||
kfn.zit
|
||||
end
|
||||
|
||||
function lifForward(kfn_zit,
|
||||
zit,
|
||||
wRec,
|
||||
vt0,
|
||||
vt1,
|
||||
vth,
|
||||
vRest,
|
||||
zt1,
|
||||
alpha,
|
||||
phi,
|
||||
epsilonRec,
|
||||
refractoryCounter,
|
||||
refractoryDuration,
|
||||
gammaPd,
|
||||
firingCounter,
|
||||
arraySize,
|
||||
arrayProjection3DTo4D)
|
||||
|
||||
# project 3D kfn zit into 4D lif zit
|
||||
zit .= reshape(kfn_zit,
|
||||
(view(arraySize, 1)[1], view(arraySize, 2)[1], 1, view(arraySize, 4)[1])) .*
|
||||
arrayProjection3DTo4D
|
||||
# error("DEBUG -> lif forward") #WORKING
|
||||
for j in 1:size(wRec, 4), i in 1:size(wRec, 3) # compute along neurons axis of every batch
|
||||
if view(refractoryCounter, :, :, i, j)[1] > 0 # refractory period is active
|
||||
view(refractoryCounter, :, :, i, j)[1] -= 1
|
||||
view(zt1, :, :, i, j)[1] = 0
|
||||
view(vt1, :, :, i, j)[1] =
|
||||
view(alpha, :, :, i, j)[1] * view(vt0, :, :, i, j)[1]
|
||||
view(phi, :, :, i, j)[1] = 0.0
|
||||
view(epsilonRec, :, :, i, j) .= view(alpha, :, :, i, j)[1] .*
|
||||
view(epsilonRec, :, :, i, j)
|
||||
else # refractory period is inactive
|
||||
view(vt1, :, :, i, j)[1] =
|
||||
(view(alpha, :, :, i, j)[1] * view(vt0,:, :, i, j)[1]) +
|
||||
sum(view(zit, :, :, i, j) .* view(wRec, :, :, i, j))
|
||||
if view(vt1, :, :, i, j)[1] > view(vth, :, :, i, j)[1]
|
||||
view(zt1, :, :, i, j)[1] = 1
|
||||
view(refractoryCounter, :, :, i, j)[1] =
|
||||
view(refractoryDuration, :, :, i, j)[1]
|
||||
view(firingCounter, :, :, i, j)[1] += 1
|
||||
view(vt1, :, :, i, j)[1] = view(vRest, :, :, i, j)[1]
|
||||
else
|
||||
view(zt1, :, :, i, j)[1] = 0
|
||||
end
|
||||
# there is a difference from alif formula
|
||||
view(phi, :, :, i, j)[1] =
|
||||
(view(gammaPd, :, :, i, j)[1] / view(vth, :, :, i, j)[1]) *
|
||||
max(0, 1 - ((view(vt1, :, :, i, j)[1] - view(vth, :, :, i, j)[1]) /
|
||||
view(vth, :, :, i, j)[1]))
|
||||
view(epsilonRec, :, :, i, j) .=
|
||||
(view(alpha, :, :, i, j)[1] .* view(epsilonRec, :, :, i, j)) +
|
||||
view(zit, :, :, i, j)
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function alifForward(kfn_zit,
|
||||
zit,
|
||||
wRec,
|
||||
vt0,
|
||||
vt1,
|
||||
vth,
|
||||
avth,
|
||||
vRest,
|
||||
zt1,
|
||||
alpha,
|
||||
phi,
|
||||
epsilonRec,
|
||||
epsilonRecA,
|
||||
refractoryCounter,
|
||||
refractoryDuration,
|
||||
a,
|
||||
beta,
|
||||
rho,
|
||||
gammaPd,
|
||||
firingCounter)
|
||||
d1, d2, d3, d4 = size(wRec)
|
||||
zit .= reshape(kfn_zit, (d1, d2, 1, d4)) .* ones(size(wRec)...) # project zit into zit
|
||||
|
||||
for j in 1:d4, i in 1:d3 # compute along neurons axis of every batch
|
||||
if view(refractoryCounter, :, :, i, j)[1] > 0 # refractory period is active
|
||||
view(refractoryCounter, :, :, i, j)[1] -= 1
|
||||
view(zt1, :, :, i, j)[1] = 0
|
||||
view(vt1, :, :, i, j)[1] = view(alpha, :, :, i, j)[1] *
|
||||
view(vt0, :, :, i, j)[1]
|
||||
view(phi, :, :, i, j)[1] = 0.0
|
||||
view(epsilonRec, :, :, i, j) .= view(alpha, :, :, i, j)[1] .*
|
||||
view(epsilonRec, :, :, i, j)
|
||||
view(a, :, :, i, j)[1] =
|
||||
(view(rho, :, :, i, j)[1] * view(a, :, :, i, j)[1]) + 0
|
||||
else # refractory period is inactive
|
||||
view(vt1, :, :, i, j)[1] =
|
||||
(view(alpha, :, :, i, j)[1] * view(vt0,:, :, i, j)[1]) +
|
||||
sum(view(zit, :, :, i, j) .* view(wRec, :, :, i, j))
|
||||
view(avth, :, :, i, j)[1] = view(vth, :, :, i, j)[1] +
|
||||
(view(beta, :, :, i, j)[1] * view(a, :, :, i, j)[1])
|
||||
if view(vt1, :, :, i, j)[1] > view(avth, :, :, i, j)[1]
|
||||
view(zt1, :, :, i, j)[1] = 1
|
||||
view(refractoryCounter, :, :, i, j)[1] =
|
||||
view(refractoryDuration, :, :, i, j)[1]
|
||||
view(firingCounter, :, :, i, j)[1] += 1
|
||||
view(vt1, :, :, i, j)[1] = view(vRest, :, :, i, j)[1]
|
||||
view(a, :, :, i, j)[1] = (view(rho, :, :, i, j)[1] *
|
||||
view(a, :, :, i, j)[1]) + 1
|
||||
else
|
||||
view(zt1, :, :, i, j)[1] = 0
|
||||
view(a, :, :, i, j)[1] =
|
||||
(view(rho, :, :, i, j)[1] * view(a, :, :, i, j)[1]) + 0
|
||||
end
|
||||
|
||||
# there is a difference from alif formula
|
||||
view(phi, :, :, i, j)[1] =
|
||||
(view(gammaPd, :, :, i, j)[1] / view(vth, :, :, i, j)[1]) *
|
||||
max(0, 1 - ((view(vt1, :, :, i, j)[1] - view(avth, :, :, i, j)[1]) /
|
||||
view(vth, :, :, i, j)[1]))
|
||||
view(epsilonRec, :, :, i, j) .=
|
||||
(view(alpha, :, :, i, j) .* view(epsilonRec, :, :, i, j)) +
|
||||
view(zit, :, :, i, j)
|
||||
view(epsilonRecA, :, :, i, j) .=
|
||||
(view(phi, :, :, i, j)[1] .* view(epsilonRec, :, :, i, j)) +
|
||||
((view(rho, :, :, i, j)[1] -
|
||||
(view(phi, :, :, i, j)[1] * view(beta, :, :, i, j)[1])) .*
|
||||
view(epsilonRecA, :, :, i, j))
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function onForward(kfn_zit,
|
||||
zit,
|
||||
wOut,
|
||||
vt0,
|
||||
vt1,
|
||||
vth,
|
||||
vRest,
|
||||
zt1,
|
||||
alpha,
|
||||
phi,
|
||||
epsilonRec,
|
||||
refractoryCounter,
|
||||
refractoryDuration,
|
||||
gammaPd,
|
||||
firingCounter)
|
||||
d1, d2, d3, d4 = size(wOut)
|
||||
zit .= reshape(kfn_zit, (d1, d2, 1, d4)) .* ones(size(wOut)...) # project zit into zit
|
||||
|
||||
for j in 1:d4, i in 1:d3 # compute along neurons axis of every batch
|
||||
if view(refractoryCounter, :, :, i, j)[1] > 0 # neuron is inactive (in refractory period)
|
||||
view(refractoryCounter, :, :, i, j)[1] -= 1
|
||||
view(zt1, :, :, i, j)[1] = 0
|
||||
view(vt1, :, :, i, j)[1] =
|
||||
view(alpha, :, :, i, j)[1] * view(vt0, :, :, i, j)[1]
|
||||
view(phi, :, :, i, j)[1] = 0.0
|
||||
view(epsilonRec, :, :, i, j) .= view(alpha, :, :, i, j)[1] .*
|
||||
view(epsilonRec, :, :, i, j)
|
||||
else # neuron is active
|
||||
view(vt1, :, :, i, j)[1] =
|
||||
(view(alpha, :, :, i, j)[1] * view(vt0,:, :, i, j)[1]) +
|
||||
sum(view(zit, :, :, i, j) .* view(wOut, :, :, i, j))
|
||||
if view(vt1, :, :, i, j)[1] > view(vth, :, :, i, j)[1]
|
||||
view(zt1, :, :, i, j)[1] = 1
|
||||
view(refractoryCounter, :, :, i, j)[1] =
|
||||
view(refractoryDuration, :, :, i, j)[1]
|
||||
view(firingCounter, :, :, i, j)[1] += 1
|
||||
view(vt1, :, :, i, j)[1] = view(vRest, :, :, i, j)[1]
|
||||
else
|
||||
view(zt1, :, :, i, j)[1] = 0
|
||||
end
|
||||
# there is a difference from alif formula
|
||||
view(phi, :, :, i, j)[1] =
|
||||
(view(gammaPd, :, :, i, j)[1] / view(vth, :, :, i, j)[1]) *
|
||||
max(0, 1 - ((view(vt1, :, :, i, j)[1] - view(vth, :, :, i, j)[1]) /
|
||||
view(vth, :, :, i, j)[1]))
|
||||
view(epsilonRec, :, :, i, j) .=
|
||||
(view(alpha, :, :, i, j)[1] .* view(epsilonRec, :, :, i, j)) +
|
||||
view(zit, :, :, i, j)
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
# function onForward(kfn_zit,
|
||||
# zit,
|
||||
# wOut,
|
||||
# vt0,
|
||||
# vt1,
|
||||
# vth,
|
||||
# vRest,
|
||||
# zt1,
|
||||
# alpha,
|
||||
# phi,
|
||||
# epsilonRec,
|
||||
# refractoryCounter,
|
||||
# refractoryDuration,
|
||||
# gammaPd,
|
||||
# firingCounter)
|
||||
# d1, d2, d3, d4 = size(wOut)
|
||||
# zit .= reshape(kfn_zit, (d1, d2, 1, d4)) .* ones(size(wOut)...) # project zit into zit
|
||||
|
||||
# for j in 1:d4, i in 1:d3 # compute along neurons axis of every batch
|
||||
# if view(refractoryCounter, :, :, i, j)[1] > 0 # neuron is inactive (in refractory period)
|
||||
# view(refractoryCounter, :, :, i, j)[1] -= 1
|
||||
# view(zt1, :, :, i, j)[1] = 0
|
||||
# view(vt1, :, :, i, j)[1] =
|
||||
# view(alpha, :, :, i, j)[1] * view(vt0, :, :, i, j)[1]
|
||||
# view(phi, :, :, i, j)[1] = 0.0
|
||||
# view(epsilonRec, :, :, i, j) .= view(alpha, :, :, i, j)[1] .*
|
||||
# view(epsilonRec, :, :, i, j)
|
||||
# else # neuron is active
|
||||
# view(vt1, :, :, i, j)[1] =
|
||||
# (view(alpha, :, :, i, j)[1] * view(vt0,:, :, i, j)[1]) +
|
||||
# sum(view(zit, :, :, i, j) .* view(wOut, :, :, i, j))
|
||||
# if view(vt1, :, :, i, j)[1] > view(vth, :, :, i, j)[1]
|
||||
# view(zt1, :, :, i, j)[1] = 1
|
||||
# view(refractoryCounter, :, :, i, j)[1] =
|
||||
# view(refractoryDuration, :, :, i, j)[1]
|
||||
# view(firingCounter, :, :, i, j)[1] += 1
|
||||
# view(vt1, :, :, i, j)[1] = view(vRest, :, :, i, j)[1]
|
||||
# else
|
||||
# view(zt1, :, :, i, j)[1] = 0
|
||||
# end
|
||||
# # there is a difference from alif formula
|
||||
# view(phi, :, :, i, j)[1] =
|
||||
# (view(gammaPd, :, :, i, j)[1] / view(vth, :, :, i, j)[1]) *
|
||||
# max(0, 1 - ((view(vt1, :, :, i, j)[1] - view(vth, :, :, i, j)[1]) /
|
||||
# view(vth, :, :, i, j)[1]))
|
||||
# view(epsilonRec, :, :, i, j) .=
|
||||
# (view(alpha, :, :, i, j)[1] .* view(epsilonRec, :, :, i, j)) +
|
||||
# view(zit, :, :, i, j)
|
||||
# end
|
||||
# end
|
||||
# end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module
|
||||
87
previousVersion/0.0.1/src/interface.jl
Normal file
87
previousVersion/0.0.1/src/interface.jl
Normal file
@@ -0,0 +1,87 @@
|
||||
module interface
|
||||
|
||||
|
||||
# export
|
||||
|
||||
# using Flux, CUDA
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module
|
||||
237
previousVersion/0.0.1/src/learn.jl
Normal file
237
previousVersion/0.0.1/src/learn.jl
Normal file
@@ -0,0 +1,237 @@
|
||||
module learn
|
||||
|
||||
export learn!, compute_paramsChange!
|
||||
|
||||
using Statistics, Random, LinearAlgebra, JSON3, Flux, 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_wRec,
|
||||
kfn.lif_wRecChange,
|
||||
kfn.on_wOut,
|
||||
modelError)
|
||||
|
||||
alifComputeParamsChange!(kfn.alif_phi,
|
||||
kfn.alif_epsilonRec,
|
||||
kfn.alif_epsilonRecA,
|
||||
kfn.alif_eta,
|
||||
kfn.alif_wRec,
|
||||
kfn.alif_wRecChange,
|
||||
kfn.alif_beta,
|
||||
kfn.on_wOut,
|
||||
modelError)
|
||||
|
||||
onComputeParamsChange!(kfn.on_phi,
|
||||
kfn.on_epsilonRec,
|
||||
kfn.on_eta,
|
||||
kfn.on_wOutChange,
|
||||
outputError)
|
||||
|
||||
|
||||
error("debug end -> kfn compute_paramsChange! $(Dates.now())")
|
||||
end
|
||||
|
||||
function lifComputeParamsChange!( phi,
|
||||
epsilonRec,
|
||||
eta,
|
||||
wRec,
|
||||
wRecChange,
|
||||
wOut,
|
||||
modelError)
|
||||
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,
|
||||
epsilonRec,
|
||||
epsilonRecA,
|
||||
eta,
|
||||
wRec,
|
||||
wRecChange,
|
||||
beta,
|
||||
wOut,
|
||||
modelError)
|
||||
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,
|
||||
epsilonRec,
|
||||
eta,
|
||||
wOutChange,
|
||||
outputError)
|
||||
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 onComputeParamsChange!(wOut,
|
||||
# epsilonRec,
|
||||
# eta,
|
||||
# wOutChange,
|
||||
# bChange,
|
||||
# outputError)
|
||||
# d1, d2, d3, d4 = size(epsilonRec)
|
||||
# println(">>> epsilon ", size(epsilonRec))
|
||||
# println(">>> outputError ", size(outputError))
|
||||
|
||||
|
||||
# # Bₖⱼ in paper, sum() to get each neuron's total wOut weight
|
||||
|
||||
# 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] * view(outputError, :, j)[i]) .*
|
||||
# view(epsilonRec, :, :, i, j)
|
||||
# end
|
||||
# #TODO add b
|
||||
# error(">>> DEBUG -> onComputeParamsChange!")
|
||||
# end
|
||||
|
||||
|
||||
function learn!(kfn::kfn_1)
|
||||
#WORKING lif learn
|
||||
lifLearn!(kfn.lif_wRec,
|
||||
kfn.lif_wRecChange)
|
||||
|
||||
|
||||
#TODO alif learn
|
||||
|
||||
|
||||
#TODO on learn
|
||||
|
||||
#TODO wOut decay
|
||||
|
||||
# wrap up learning session
|
||||
if kfn.learningStage == [3]
|
||||
kfn.learningStage = [0]
|
||||
end
|
||||
end
|
||||
|
||||
function lifLearn!(wRec,
|
||||
wRecChange)
|
||||
# merge learning weight
|
||||
wRec .+= wRecChange
|
||||
|
||||
#TODO synaptic strength
|
||||
|
||||
#TODO neuroplasticity
|
||||
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module
|
||||
77
previousVersion/0.0.1/src/snnUtil.jl
Normal file
77
previousVersion/0.0.1/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
|
||||
344
previousVersion/0.0.1/src/type.jl
Normal file
344
previousVersion/0.0.1/src/type.jl
Normal file
@@ -0,0 +1,344 @@
|
||||
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
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# 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_vt0::Union{AbstractArray, Nothing} = nothing
|
||||
lif_vt1::Union{AbstractArray, Nothing} = nothing
|
||||
lif_vth::Union{AbstractArray, Nothing} = nothing
|
||||
lif_vRest::Union{AbstractArray, Nothing} = nothing
|
||||
lif_zt0::Union{AbstractArray, Nothing} = nothing
|
||||
lif_zt1::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_firingCounter::Union{AbstractArray, Nothing} = nothing
|
||||
lif_arraySize::Union{AbstractArray, Nothing} = nothing
|
||||
|
||||
# pre-allocation array
|
||||
lif_arrayProjection3DTo4D::Union{AbstractArray, Nothing} = nothing # use to project 3d array to 4d
|
||||
lif_recSignal::Union{AbstractArray, Nothing} = nothing
|
||||
lif_decayed_Vt0::Union{AbstractArray, Nothing} = nothing
|
||||
lif_decayed_EpsilonRec::Union{AbstractArray, Nothing} = nothing
|
||||
lif_vt1_diff_vth::Union{AbstractArray, Nothing} = nothing
|
||||
lif_vt1_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_vt0::Union{AbstractArray, Nothing} = nothing
|
||||
alif_vt1::Union{AbstractArray, Nothing} = nothing
|
||||
alif_vth::Union{AbstractArray, Nothing} = nothing
|
||||
alif_avth::Union{AbstractArray, Nothing} = nothing
|
||||
alif_vRest::Union{AbstractArray, Nothing} = nothing
|
||||
alif_zt0::Union{AbstractArray, Nothing} = nothing
|
||||
alif_zt1::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_epsilonRecA::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_firingCounter::Union{AbstractArray, Nothing} = nothing
|
||||
alif_arraySize::Union{AbstractArray, Nothing} = nothing
|
||||
alif_arrayProjection3DTo4D::Union{AbstractArray, Nothing} = nothing # use to project 3d array to 4d
|
||||
|
||||
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
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# Output Neurons #
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# output neuron is based on LIF
|
||||
on_zit::Union{AbstractArray, Nothing} = nothing
|
||||
|
||||
on_wOut::Union{AbstractArray, Nothing} = nothing # same as lif_wRec
|
||||
on_vt0::Union{AbstractArray, Nothing} = nothing
|
||||
on_vt1::Union{AbstractArray, Nothing} = nothing
|
||||
on_vth::Union{AbstractArray, Nothing} = nothing
|
||||
on_vRest::Union{AbstractArray, Nothing} = nothing
|
||||
on_zt0::Union{AbstractArray, Nothing} = nothing
|
||||
on_zt1::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_b::Union{AbstractArray, Nothing} = nothing
|
||||
on_bChange::Union{AbstractArray, Nothing} = nothing
|
||||
|
||||
on_firingCounter::Union{AbstractArray, Nothing} = nothing
|
||||
on_arraySize::Union{AbstractArray, Nothing} = nothing
|
||||
on_arrayProjection3DTo4D::Union{AbstractArray, Nothing} = nothing # use to project 3d array to 4d
|
||||
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
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# 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
|
||||
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_vt0 = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_vt1 = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_vth = ones(1, 1, n, batch) |> device
|
||||
kfn.lif_vRest = zeros(1, 1, n, batch) |> device
|
||||
# kfn.lif_zt0 = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_zt1 = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_refractoryCounter = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_refractoryDuration = ones(1, 1, n, batch) .* 3 |> device
|
||||
kfn.lif_delta = 1.0
|
||||
kfn.lif_tau_m = 20.0
|
||||
kfn.lif_alpha = ones(1, 1, n, batch) .* (exp(-kfn.lif_delta / kfn.lif_tau_m)) |> device
|
||||
kfn.lif_phi = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_epsilonRec = zeros(row, col, n, batch) |> device
|
||||
# kfn.lif_eRec = zeros(row, col, n, batch)
|
||||
kfn.lif_eta = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_gammaPd = zeros(1, 1, n, batch) .* 0.3 |> device
|
||||
kfn.lif_wRecChange = zeros(row, col, n, batch) |> device
|
||||
|
||||
kfn.lif_firingCounter = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_arraySize = [row, col, n, batch] |> device
|
||||
kfn.lif_arrayProjection3DTo4D = ones(row, col, n, batch) |> device
|
||||
|
||||
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# ALIF config #
|
||||
# ---------------------------------------------------------------------------- #
|
||||
n = kfn.params[:computeNeuron][:alif][:numbers][1] * kfn.params[:computeNeuron][:alif][:numbers][2]
|
||||
kfn.alif_zit = zeros(row, col, n, batch) |> device
|
||||
kfn.alif_vt0 = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_vt1 = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_vth = ones(1, 1, n, batch) |> device
|
||||
kfn.alif_avth = ones(1, 1, n, batch) |> device
|
||||
kfn.alif_vRest = zeros(1, 1, n, batch) |> device
|
||||
# kfn.alif_zt0 = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_zt1 = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_refractoryCounter = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_refractoryDuration = ones(1, 1, n, batch) .* 3 |> device
|
||||
kfn.alif_delta = 1.0
|
||||
kfn.alif_tau_m = 20.0
|
||||
kfn.alif_alpha = ones(1, 1, n, batch) .* (exp(-kfn.alif_delta / kfn.alif_tau_m)) |> device
|
||||
kfn.alif_phi = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_epsilonRec = zeros(row, col, n, batch) |> device
|
||||
kfn.alif_epsilonRecA = zeros(row, col, n, batch) |> device
|
||||
# kfn.alif_eRec = zeros(row, col, n, batch)
|
||||
kfn.alif_eta = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_gammaPd = zeros(1, 1, n, batch) .* 0.3 |> device
|
||||
kfn.alif_wRecChange = zeros(row, col, n, batch) |> device
|
||||
|
||||
kfn.alif_a = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_beta = zeros(1, 1, n, batch) .* 0.15 |> device
|
||||
kfn.alif_tau_a = 100.0
|
||||
kfn.alif_rho = zeros(1, 1, n, batch) .* (exp(-kfn.alif_delta / kfn.alif_tau_a)) |> device
|
||||
|
||||
kfn.alif_firingCounter = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_arraySize = [row, col, n, batch] |> device
|
||||
kfn.alif_arrayProjection3DTo4D = ones(row, col, n, batch) |> device
|
||||
|
||||
# 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
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# output config #
|
||||
# ---------------------------------------------------------------------------- #
|
||||
n = kfn.params[:outputPort][:numbers][1] * kfn.params[:outputPort][:numbers][2]
|
||||
kfn.on_zit = zeros(row, col, n, batch) |> device
|
||||
kfn.on_vt0 = zeros(1, 1, n, batch) |> device
|
||||
kfn.on_vt1 = 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_zt0 = zeros(1, 1, n, batch) |> device
|
||||
kfn.on_zt1 = 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_arrayProjection3DTo4D = 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
|
||||
@@ -2,6 +2,7 @@ module forward
|
||||
|
||||
# export
|
||||
|
||||
using Flux, CUDA
|
||||
using GeneralUtils
|
||||
using ..type, ..snnUtil
|
||||
|
||||
@@ -11,15 +12,15 @@ using ..type, ..snnUtil
|
||||
input (row, col, batch)
|
||||
"""
|
||||
function (kfn::kfn_1)(input::AbstractArray)
|
||||
|
||||
kfn.timeStep .+= 1
|
||||
|
||||
|
||||
#TODO time step forward
|
||||
if kfn.learningStage == [1]
|
||||
if view(kfn.learningStage, 1)[1] == 1
|
||||
# reset learning params
|
||||
kfn.learningStage = [2]
|
||||
# kfn.learningStage = [2]
|
||||
end
|
||||
|
||||
d1, d2, d3 = size(input)
|
||||
|
||||
# println(">>> input ", size(input))
|
||||
# println(">>> zit ", size(kfn.zit))
|
||||
# println(">>> lif_zit ", size(kfn.lif_zit))
|
||||
@@ -29,10 +30,10 @@ function (kfn::kfn_1)(input::AbstractArray)
|
||||
# println(">>> lif_alpha ", size(kfn.lif_alpha))
|
||||
# println(">>> lif_vt0 ", size(kfn.lif_vt0))
|
||||
# println(">>> lif_vt0 sum ", sum(kfn.lif_vt0))
|
||||
|
||||
|
||||
# pass input_data into input neuron.
|
||||
GeneralUtils.cartesianAssign!(kfn.zit, input)
|
||||
|
||||
|
||||
lifForward( kfn.zit,
|
||||
kfn.lif_zit,
|
||||
kfn.lif_wRec,
|
||||
@@ -47,7 +48,9 @@ function (kfn::kfn_1)(input::AbstractArray)
|
||||
kfn.lif_refractoryCounter,
|
||||
kfn.lif_refractoryDuration,
|
||||
kfn.lif_gammaPd,
|
||||
kfn.lif_firingCounter)
|
||||
kfn.lif_firingCounter,
|
||||
kfn.lif_arraySize,
|
||||
kfn.lif_arrayProjection3DTo4D)
|
||||
|
||||
alifForward( kfn.zit,
|
||||
kfn.alif_zit,
|
||||
@@ -69,13 +72,16 @@ function (kfn::kfn_1)(input::AbstractArray)
|
||||
kfn.alif_rho,
|
||||
kfn.alif_gammaPd,
|
||||
kfn.alif_firingCounter)
|
||||
# error("DEBUG -> kfn forward")
|
||||
|
||||
|
||||
|
||||
# update activation matrix by concatenate (input, lif_zt1, alif_zt1) to form activation matrix
|
||||
_zit = cat(reshape(input, (d1, d2, 1, d3)),
|
||||
reshape(kfn.lif_zt1, (d1, :, 1, d3)),
|
||||
reshape(kfn.alif_zt1, (d1, :, 1, d3)), dims=2)
|
||||
kfn.zit .= reshape(_zit, (d1, :, d3))
|
||||
|
||||
_zit = cat(reshape(input, (size(input, 1), size(input, 2), 1, size(input, 3))),
|
||||
reshape(kfn.lif_zt1, (size(input, 1), :, 1, size(input, 3))),
|
||||
reshape(kfn.alif_zt1, (size(input, 1), :, 1, size(input, 3))), dims=2)
|
||||
kfn.zit .= reshape(_zit, (size(input, 1), :, size(input, 3)))
|
||||
|
||||
# read out
|
||||
onForward( kfn.zit,
|
||||
kfn.on_zit,
|
||||
@@ -93,7 +99,7 @@ function (kfn::kfn_1)(input::AbstractArray)
|
||||
kfn.on_gammaPd,
|
||||
kfn.on_firingCounter)
|
||||
|
||||
return reshape(kfn.on_zt1, (d1, :)),
|
||||
return reshape(kfn.on_zt1, (size(input, 1), :)),
|
||||
kfn.zit
|
||||
end
|
||||
|
||||
@@ -111,11 +117,16 @@ function lifForward(kfn_zit,
|
||||
refractoryCounter,
|
||||
refractoryDuration,
|
||||
gammaPd,
|
||||
firingCounter)
|
||||
d1, d2, d3, d4 = size(wRec)
|
||||
zit .= reshape(kfn_zit, (d1, d2, 1, d4)) .* ones(size(wRec)...) # project zit into zit
|
||||
firingCounter,
|
||||
arraySize,
|
||||
arrayProjection3DTo4D)
|
||||
|
||||
for j in 1:d4, i in 1:d3 # compute along neurons axis of every batch
|
||||
# project 3D kfn zit into 4D lif zit
|
||||
zit .= reshape(kfn_zit,
|
||||
(view(arraySize, 1)[1], view(arraySize, 2)[1], 1, view(arraySize, 4)[1])) .*
|
||||
arrayProjection3DTo4D
|
||||
# error("DEBUG -> lif forward") #WORKING
|
||||
for j in 1:size(wRec, 4), i in 1:size(wRec, 3) # compute along neurons axis of every batch
|
||||
if view(refractoryCounter, :, :, i, j)[1] > 0 # refractory period is active
|
||||
view(refractoryCounter, :, :, i, j)[1] -= 1
|
||||
view(zt1, :, :, i, j)[1] = 0
|
||||
|
||||
193
src/type.jl
193
src/type.jl
@@ -17,18 +17,19 @@ abstract type knowledgeFn <: Ironpen end
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
Base.@kwdef mutable struct kfn_1 <: knowledgeFn
|
||||
params::Dict = Dict() # store params of knowledgeFn itself for later use
|
||||
params::Union{Dict, Nothing} = nothing # store params of knowledgeFn itself for later use
|
||||
|
||||
timeStep::AbstractArray = [0]
|
||||
learningStage::AbstractArray = [0] # 0 inference, 1 start, 2 during, 3 end learning
|
||||
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
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# LIF Neurons #
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# a projection of kfn.zit into lif dimension for broadcasting later)
|
||||
lif_zit::Union{AbstractArray, Nothing} = nothing
|
||||
lif_zit::Union{AbstractArray, Nothing} = nothing
|
||||
|
||||
# main variables according to papers
|
||||
lif_wRec::Union{AbstractArray, Nothing} = nothing
|
||||
lif_vt0::Union{AbstractArray, Nothing} = nothing
|
||||
lif_vt1::Union{AbstractArray, Nothing} = nothing
|
||||
@@ -39,16 +40,27 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
|
||||
lif_refractoryCounter::Union{AbstractArray, Nothing} = nothing
|
||||
lif_refractoryDuration::Union{AbstractArray, Nothing} = nothing
|
||||
lif_alpha::Union{AbstractArray, Nothing} = nothing
|
||||
lif_delta::AbstractFloat = 1.0
|
||||
lif_tau_m::AbstractFloat = 20.0
|
||||
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_eRec::Union{AbstractArray, Nothing} = nothing
|
||||
lif_eta::Union{AbstractArray, Nothing} = nothing
|
||||
lif_gammaPd::Union{AbstractArray, Nothing} = nothing
|
||||
lif_wRecChange::Union{AbstractArray, Nothing} = nothing
|
||||
|
||||
lif_firingCounter::Union{AbstractArray, Nothing} = nothing
|
||||
lif_arraySize::Union{AbstractArray, Nothing} = nothing
|
||||
|
||||
# pre-allocation array
|
||||
lif_arrayProjection3DTo4D::Union{AbstractArray, Nothing} = nothing # use to project 3d array to 4d
|
||||
lif_recSignal::Union{AbstractArray, Nothing} = nothing
|
||||
lif_decayed_Vt0::Union{AbstractArray, Nothing} = nothing
|
||||
lif_decayed_EpsilonRec::Union{AbstractArray, Nothing} = nothing
|
||||
lif_vt1_diff_vth::Union{AbstractArray, Nothing} = nothing
|
||||
lif_vt1_diff_vth_div_vth::Union{AbstractArray, Nothing} = nothing
|
||||
lif_gammaPd_div_vth::Union{AbstractArray, Nothing} = nothing
|
||||
lif_phiActivation::Union{AbstractArray, Nothing} = nothing
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# ALIF Neurons #
|
||||
@@ -66,22 +78,24 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
|
||||
alif_refractoryCounter::Union{AbstractArray, Nothing} = nothing
|
||||
alif_refractoryDuration::Union{AbstractArray, Nothing} = nothing
|
||||
alif_alpha::Union{AbstractArray, Nothing} = nothing
|
||||
alif_delta::AbstractFloat = 1.0
|
||||
alif_tau_m::AbstractFloat = 20.0
|
||||
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_epsilonRecA::Union{AbstractArray, Nothing} = nothing
|
||||
# alif_eRec::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_firingCounter::Union{AbstractArray, Nothing} = nothing
|
||||
alif_arraySize::Union{AbstractArray, Nothing} = nothing
|
||||
alif_arrayProjection3DTo4D::Union{AbstractArray, Nothing} = nothing # use to project 3d array to 4d
|
||||
|
||||
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::AbstractFloat = 100.0 # τ_a, adaption time constant in millisecond
|
||||
alif_tau_a::Union{AbstractFloat, Nothing} = nothing # τ_a, adaption time constant in millisecond
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# Output Neurons #
|
||||
@@ -99,8 +113,8 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
|
||||
on_refractoryCounter::Union{AbstractArray, Nothing} = nothing
|
||||
on_refractoryDuration::Union{AbstractArray, Nothing} = nothing
|
||||
on_alpha::Union{AbstractArray, Nothing} = nothing
|
||||
on_delta::AbstractFloat = 1.0
|
||||
on_tau_m::AbstractFloat = 20.0
|
||||
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
|
||||
@@ -112,13 +126,17 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
|
||||
on_bChange::Union{AbstractArray, Nothing} = nothing
|
||||
|
||||
on_firingCounter::Union{AbstractArray, Nothing} = nothing
|
||||
on_arraySize::Union{AbstractArray, Nothing} = nothing
|
||||
on_arrayProjection3DTo4D::Union{AbstractArray, Nothing} = nothing # use to project 3d array to 4d
|
||||
end
|
||||
|
||||
# outer constructor
|
||||
function kfn_1(params::Dict)
|
||||
function kfn_1(params::Dict; device=cpu)
|
||||
kfn = kfn_1()
|
||||
kfn.params = params
|
||||
|
||||
kfn.timeStep = [0] |> device
|
||||
kfn.learningStage = [0] |> device
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# initialize activation matrix #
|
||||
# ---------------------------------------------------------------------------- #
|
||||
@@ -130,7 +148,7 @@ function kfn_1(params::Dict)
|
||||
col += kfn.params[:computeNeuron][:alif][:numbers][2]
|
||||
|
||||
# activation matrix
|
||||
kfn.zit = zeros(row, col, batch)
|
||||
kfn.zit = zeros(row, col, batch) |> device
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# LIF config #
|
||||
@@ -138,22 +156,6 @@ function kfn_1(params::Dict)
|
||||
# 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]
|
||||
kfn.lif_zit = zeros(row, col, n, batch)
|
||||
kfn.lif_vt0 = zeros(1, 1, n, batch)
|
||||
kfn.lif_vt1 = zeros(1, 1, n, batch)
|
||||
kfn.lif_vth = ones(1, 1, n, batch)
|
||||
kfn.lif_vRest = zeros(1, 1, n, batch)
|
||||
kfn.lif_zt0 = zeros(1, 1, n, batch)
|
||||
kfn.lif_zt1 = zeros(1, 1, n, batch)
|
||||
kfn.lif_refractoryCounter = zeros(1, 1, n, batch)
|
||||
kfn.lif_refractoryDuration = ones(1, 1, n, batch) .* 3
|
||||
kfn.lif_alpha = ones(1, 1, n, batch) .* (exp(-kfn.lif_delta / kfn.lif_tau_m))
|
||||
kfn.lif_phi = zeros(1, 1, n, batch)
|
||||
kfn.lif_epsilonRec = zeros(row, col, n, batch)
|
||||
# kfn.lif_eRec = zeros(row, col, n, batch)
|
||||
kfn.lif_eta = zeros(1, 1, n, batch)
|
||||
kfn.lif_gammaPd = zeros(1, 1, n, batch) .* 0.3
|
||||
kfn.lif_wRecChange = zeros(row, col, n, batch)
|
||||
|
||||
# subscription
|
||||
w = zeros(row, col, n)
|
||||
@@ -166,35 +168,67 @@ function kfn_1(params::Dict)
|
||||
end
|
||||
end
|
||||
# project 3D w into 4D kfn.lif_wRec
|
||||
kfn.lif_wRec = reshape(w, (row, col, n, 1)) .* ones(row, col, n, batch)
|
||||
kfn.lif_firingCounter = zeros(1, 1, 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_vt0 = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_vt1 = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_vth = ones(1, 1, n, batch) |> device
|
||||
kfn.lif_vRest = zeros(1, 1, n, batch) |> device
|
||||
# kfn.lif_zt0 = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_zt1 = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_refractoryCounter = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_refractoryDuration = ones(1, 1, n, batch) .* 3 |> device
|
||||
kfn.lif_delta = 1.0
|
||||
kfn.lif_tau_m = 20.0
|
||||
kfn.lif_alpha = ones(1, 1, n, batch) .* (exp(-kfn.lif_delta / kfn.lif_tau_m)) |> device
|
||||
kfn.lif_phi = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_epsilonRec = zeros(row, col, n, batch) |> device
|
||||
# kfn.lif_eRec = zeros(row, col, n, batch)
|
||||
kfn.lif_eta = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_gammaPd = zeros(1, 1, n, batch) .* 0.3 |> device
|
||||
kfn.lif_wRecChange = zeros(row, col, n, batch) |> device
|
||||
|
||||
kfn.lif_firingCounter = zeros(1, 1, n, batch) |> device
|
||||
kfn.lif_arraySize = [row, col, n, batch] |> device
|
||||
kfn.lif_arrayProjection3DTo4D = ones(row, col, n, batch) |> device
|
||||
|
||||
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# ALIF config #
|
||||
# ---------------------------------------------------------------------------- #
|
||||
n = kfn.params[:computeNeuron][:alif][:numbers][1] * kfn.params[:computeNeuron][:alif][:numbers][2]
|
||||
kfn.alif_zit = zeros(row, col, n, batch)
|
||||
kfn.alif_vt0 = zeros(1, 1, n, batch)
|
||||
kfn.alif_vt1 = zeros(1, 1, n, batch)
|
||||
kfn.alif_vth = ones(1, 1, n, batch)
|
||||
kfn.alif_avth = ones(1, 1, n, batch)
|
||||
kfn.alif_vRest = zeros(1, 1, n, batch)
|
||||
kfn.alif_zt0 = zeros(1, 1, n, batch)
|
||||
kfn.alif_zt1 = zeros(1, 1, n, batch)
|
||||
kfn.alif_refractoryCounter = zeros(1, 1, n, batch)
|
||||
kfn.alif_refractoryDuration = ones(1, 1, n, batch) .* 3
|
||||
kfn.alif_alpha = ones(1, 1, n, batch) .* (exp(-kfn.alif_delta / kfn.alif_tau_m))
|
||||
kfn.alif_phi = zeros(1, 1, n, batch)
|
||||
kfn.alif_epsilonRec = zeros(row, col, n, batch)
|
||||
kfn.alif_epsilonRecA = zeros(row, col, n, batch)
|
||||
kfn.alif_zit = zeros(row, col, n, batch) |> device
|
||||
kfn.alif_vt0 = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_vt1 = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_vth = ones(1, 1, n, batch) |> device
|
||||
kfn.alif_avth = ones(1, 1, n, batch) |> device
|
||||
kfn.alif_vRest = zeros(1, 1, n, batch) |> device
|
||||
# kfn.alif_zt0 = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_zt1 = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_refractoryCounter = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_refractoryDuration = ones(1, 1, n, batch) .* 3 |> device
|
||||
kfn.alif_delta = 1.0
|
||||
kfn.alif_tau_m = 20.0
|
||||
kfn.alif_alpha = ones(1, 1, n, batch) .* (exp(-kfn.alif_delta / kfn.alif_tau_m)) |> device
|
||||
kfn.alif_phi = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_epsilonRec = zeros(row, col, n, batch) |> device
|
||||
kfn.alif_epsilonRecA = zeros(row, col, n, batch) |> device
|
||||
# kfn.alif_eRec = zeros(row, col, n, batch)
|
||||
kfn.alif_eta = zeros(1, 1, n, batch)
|
||||
kfn.alif_gammaPd = zeros(1, 1, n, batch) .* 0.3
|
||||
kfn.alif_wRecChange = zeros(row, col, n, batch)
|
||||
kfn.alif_eta = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_gammaPd = zeros(1, 1, n, batch) .* 0.3 |> device
|
||||
kfn.alif_wRecChange = zeros(row, col, n, batch) |> device
|
||||
|
||||
kfn.alif_a = zeros(1, 1, n, batch)
|
||||
kfn.alif_beta = zeros(1, 1, n, batch) .* 0.15
|
||||
kfn.alif_rho = zeros(1, 1, n, batch) .* (exp(-kfn.alif_delta / kfn.alif_tau_a))
|
||||
kfn.alif_a = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_beta = zeros(1, 1, n, batch) .* 0.15 |> device
|
||||
kfn.alif_tau_a = 100.0
|
||||
kfn.alif_rho = zeros(1, 1, n, batch) .* (exp(-kfn.alif_delta / kfn.alif_tau_a)) |> device
|
||||
|
||||
kfn.alif_firingCounter = zeros(1, 1, n, batch) |> device
|
||||
kfn.alif_arraySize = [row, col, n, batch] |> device
|
||||
kfn.alif_arrayProjection3DTo4D = ones(row, col, n, batch) |> device
|
||||
|
||||
# subscription
|
||||
w = zeros(row, col, n)
|
||||
@@ -207,31 +241,37 @@ function kfn_1(params::Dict)
|
||||
end
|
||||
end
|
||||
# project 3D w into 4D kfn.alif_wRec
|
||||
kfn.alif_wRec = reshape(w, (row, col, n, 1)) .* ones(row, col, n, batch)
|
||||
kfn.alif_firingCounter = zeros(1, 1, n, batch)
|
||||
kfn.alif_wRec = reshape(w, (row, col, n, 1)) .* ones(row, col, n, batch) |> device
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# output config #
|
||||
# ---------------------------------------------------------------------------- #
|
||||
n = kfn.params[:outputPort][:numbers][1] * kfn.params[:outputPort][:numbers][2]
|
||||
kfn.on_zit = zeros(row, col, n, batch)
|
||||
kfn.on_vt0 = zeros(1, 1, n, batch)
|
||||
kfn.on_vt1 = zeros(1, 1, n, batch)
|
||||
kfn.on_vth = ones(1, 1, n, batch)
|
||||
kfn.on_vRest = zeros(1, 1, n, batch)
|
||||
kfn.on_zt0 = zeros(1, 1, n, batch)
|
||||
kfn.on_zt1 = zeros(1, 1, n, batch)
|
||||
kfn.on_refractoryCounter = zeros(1, 1, n, batch)
|
||||
kfn.on_refractoryDuration = ones(1, 1, n, batch) .* 0
|
||||
kfn.on_alpha = ones(1, 1, n, batch) .* (exp(-kfn.on_delta / kfn.on_tau_m))
|
||||
kfn.on_phi = zeros(1, 1, n, batch)
|
||||
kfn.on_epsilonRec = zeros(row, col, n, batch)
|
||||
kfn.on_zit = zeros(row, col, n, batch) |> device
|
||||
kfn.on_vt0 = zeros(1, 1, n, batch) |> device
|
||||
kfn.on_vt1 = 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_zt0 = zeros(1, 1, n, batch) |> device
|
||||
kfn.on_zt1 = 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)
|
||||
kfn.on_gammaPd = zeros(1, 1, n, batch) .* 0.3
|
||||
kfn.on_wOutChange = zeros(row, col, n, batch)
|
||||
kfn.on_b = randn(1, 1, n, batch)
|
||||
kfn.on_bChange = randn(1, 1, 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_arrayProjection3DTo4D = ones(row, col, n, batch) |> device
|
||||
|
||||
# subscription
|
||||
w = zeros(row, col, n)
|
||||
@@ -244,11 +284,8 @@ function kfn_1(params::Dict)
|
||||
end
|
||||
end
|
||||
# project 3D w into 4D kfn.on_wOut
|
||||
kfn.on_wOut = reshape(w, (row, col, n, 1)) .* ones(row, col, n, batch)
|
||||
kfn.on_firingCounter = zeros(1, 1, n, batch)
|
||||
|
||||
|
||||
|
||||
kfn.on_wOut = reshape(w, (row, col, n, 1)) .* ones(row, col, n, batch) |> device
|
||||
|
||||
|
||||
|
||||
return kfn
|
||||
|
||||
Reference in New Issue
Block a user