0.0.2
This commit is contained in:
931
oldVersion/0.0.2/Manifest.toml
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931
oldVersion/0.0.2/Manifest.toml
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# This file is machine-generated - editing it directly is not advised
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julia_version = "1.9.0"
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manifest_format = "2.0"
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project_hash = "b9e7ae4b78dc59a5adb629a04e856c4fedc6fb60"
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[[deps.AbstractFFTs]]
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deps = ["LinearAlgebra"]
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version = "1.3.1"
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weakdeps = ["ChainRulesCore"]
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[deps.AbstractFFTs.extensions]
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AbstractFFTsChainRulesCoreExt = "ChainRulesCore"
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|
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[[deps.Accessors]]
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deps = ["Compat", "CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "LinearAlgebra", "MacroTools", "Requires", "Test"]
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[deps.Accessors.extensions]
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AccessorsStaticArraysExt = "StaticArrays"
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AccessorsStructArraysExt = "StructArrays"
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[deps.Accessors.weakdeps]
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[[deps.Adapt]]
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[deps.Adapt.extensions]
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[[deps.BFloat16s]]
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[[deps.BangBang]]
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[deps.BangBang.extensions]
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BangBangChainRulesCoreExt = "ChainRulesCore"
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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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[[deps.CEnum]]
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[[deps.CUDA]]
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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.Compat.extensions]
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[[deps.CompilerSupportLibraries_jll]]
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||||
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||||
[deps.CompositionsBase.extensions]
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||||
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||||
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uuid = "e6cf234a-135c-5ec9-84dd-332b85af5143"
|
||||
version = "1.5.3"
|
||||
|
||||
[[deps.RealDot]]
|
||||
deps = ["LinearAlgebra"]
|
||||
git-tree-sha1 = "9f0a1b71baaf7650f4fa8a1d168c7fb6ee41f0c9"
|
||||
uuid = "c1ae055f-0cd5-4b69-90a6-9a35b1a98df9"
|
||||
version = "0.1.0"
|
||||
|
||||
[[deps.Reexport]]
|
||||
git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b"
|
||||
uuid = "189a3867-3050-52da-a836-e630ba90ab69"
|
||||
version = "1.2.2"
|
||||
|
||||
[[deps.Requires]]
|
||||
deps = ["UUIDs"]
|
||||
git-tree-sha1 = "838a3a4188e2ded87a4f9f184b4b0d78a1e91cb7"
|
||||
uuid = "ae029012-a4dd-5104-9daa-d747884805df"
|
||||
version = "1.3.0"
|
||||
|
||||
[[deps.Rmath]]
|
||||
deps = ["Random", "Rmath_jll"]
|
||||
git-tree-sha1 = "f65dcb5fa46aee0cf9ed6274ccbd597adc49aa7b"
|
||||
uuid = "79098fc4-a85e-5d69-aa6a-4863f24498fa"
|
||||
version = "0.7.1"
|
||||
|
||||
[[deps.Rmath_jll]]
|
||||
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
|
||||
git-tree-sha1 = "6ed52fdd3382cf21947b15e8870ac0ddbff736da"
|
||||
uuid = "f50d1b31-88e8-58de-be2c-1cc44531875f"
|
||||
version = "0.4.0+0"
|
||||
|
||||
[[deps.SHA]]
|
||||
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.SnoopPrecompile]]
|
||||
deps = ["Preferences"]
|
||||
git-tree-sha1 = "e760a70afdcd461cf01a575947738d359234665c"
|
||||
uuid = "66db9d55-30c0-4569-8b51-7e840670fc0c"
|
||||
version = "1.0.3"
|
||||
|
||||
[[deps.Sockets]]
|
||||
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
|
||||
|
||||
[[deps.SortingAlgorithms]]
|
||||
deps = ["DataStructures"]
|
||||
git-tree-sha1 = "a4ada03f999bd01b3a25dcaa30b2d929fe537e00"
|
||||
uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
|
||||
version = "1.1.0"
|
||||
|
||||
[[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 = "ef28127915f4229c971eb43f3fc075dd3fe91880"
|
||||
uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
|
||||
version = "2.2.0"
|
||||
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 = "8982b3607a212b070a5e46eea83eb62b4744ae12"
|
||||
uuid = "90137ffa-7385-5640-81b9-e52037218182"
|
||||
version = "1.5.25"
|
||||
|
||||
[[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"
|
||||
weakdeps = ["ChainRulesCore", "InverseFunctions"]
|
||||
|
||||
[deps.StatsFuns.extensions]
|
||||
StatsFunsChainRulesCoreExt = "ChainRulesCore"
|
||||
StatsFunsInverseFunctionsExt = "InverseFunctions"
|
||||
|
||||
[[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 = "25358a5f2384c490e98abd565ed321ffae2cbb37"
|
||||
uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999"
|
||||
version = "0.4.76"
|
||||
|
||||
[[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"
|
||||
version = "0.1.2"
|
||||
|
||||
[[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", "Random", "Requires", "SnoopPrecompile", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"]
|
||||
git-tree-sha1 = "987ae5554ca90e837594a0f30325eeb5e7303d1e"
|
||||
uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
|
||||
version = "0.6.60"
|
||||
|
||||
[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 = "ec954b59f6b0324543f2e3ed8118309ac60cb75b"
|
||||
uuid = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
|
||||
version = "1.0.3"
|
||||
|
||||
[[deps.libblastrampoline_jll]]
|
||||
deps = ["Artifacts", "Libdl"]
|
||||
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
|
||||
version = "5.7.0+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
oldVersion/0.0.2/Project.toml
Normal file
14
oldVersion/0.0.2/Project.toml
Normal file
@@ -0,0 +1,14 @@
|
||||
name = "Ironpen"
|
||||
uuid = "29a645ab-0d6f-4ef8-acfd-1b192480382c"
|
||||
authors = ["tonaerospace <tonaerospace.etc@gmail.com>"]
|
||||
version = "0.1.0"
|
||||
|
||||
[deps]
|
||||
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
|
||||
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"
|
||||
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
|
||||
1
oldVersion/0.0.2/src/.vscode/settings.json
vendored
Normal file
1
oldVersion/0.0.2/src/.vscode/settings.json
vendored
Normal file
@@ -0,0 +1 @@
|
||||
{}
|
||||
153
oldVersion/0.0.2/src/DB_services.jl
Normal file
153
oldVersion/0.0.2/src/DB_services.jl
Normal file
@@ -0,0 +1,153 @@
|
||||
module DB_services
|
||||
|
||||
""" version 0.2
|
||||
"""
|
||||
|
||||
using DataStructures: count
|
||||
export send_to_DB, data_prep_for_DB
|
||||
|
||||
using DataStructures
|
||||
using JSON3
|
||||
using Redis
|
||||
using Random
|
||||
using UUIDs
|
||||
|
||||
include("Utils.jl")
|
||||
using .Utils
|
||||
|
||||
"""
|
||||
Dummy iron_pen_ai for raw_data_db_service testing
|
||||
"""
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
|
||||
""" Prepare model data for sending to raw_data_db_service by flattening all hierarchy
|
||||
data structure inside model_data into 1-dept JSON3.
|
||||
This function output is flattened JSON3 data
|
||||
*** all parameter name that is going to Cassandra must not contain a capital letter ***
|
||||
"""
|
||||
function data_prep_for_DB(model_name::String, experiment_number::Int, episode_number::Int,
|
||||
time_stamp::Int, model_data::OrderedDict)::Array{OrderedDict, 1}
|
||||
|
||||
payload_template = OrderedDict{Any, Any}(
|
||||
:model_name => model_name,
|
||||
:knowledgeFn_name => "none",
|
||||
:experiment_number => experiment_number,
|
||||
:episode_number => episode_number,
|
||||
)
|
||||
payloads = []
|
||||
for (k, v) in model_data[:m][:knowledgeFn] # loop over each knowledgeFn
|
||||
payload = deepcopy(payload_template)
|
||||
payload[:knowledgeFn_name] = v[:knowledgefn_name]
|
||||
payload[:neurons_list] = []
|
||||
for (k1, v1) in v
|
||||
if k1 == :neurons_array || k1 == :output_neurons_array
|
||||
for (k2, v2) in v1 # loop over each neuron
|
||||
if k2 != :type # add the following additonal data into neuron's ODict data (already have its parameters in there)
|
||||
neuron = OrderedDict(v2) # v2 is still in JSON3 format but
|
||||
# to be able to add new value to
|
||||
# it, it needs to be in
|
||||
# OrderedDict format
|
||||
|
||||
# # add corresponding knowledgeFn to neuron OrderedDict
|
||||
# neuron[:knowledgefn_name] = v[:knowledgefn_name]
|
||||
|
||||
# add corresponding experiment_number to neuron OrderedDict
|
||||
neuron[:experiment_number] = experiment_number
|
||||
|
||||
# add corresponding episode_number to neuron OrderedDict
|
||||
neuron[:episode_number] = episode_number
|
||||
|
||||
# # add corresponding tick_number to neuron OrderedDict
|
||||
# neuron[:tick_number] = tick_number
|
||||
|
||||
""" add neuron name of itself to neuron OrderedDict
|
||||
since neurons in neurons_array and output_neurons_array has the
|
||||
same name (because its name derived from its position in the
|
||||
array it lives in). In order to store them in the same
|
||||
OrderedDict, I need to change their name so I prefix their name
|
||||
with their array name
|
||||
"""
|
||||
neuron[:neuron_name] = Symbol(string(k1) * "_" * string(k2))
|
||||
|
||||
neuron[:model_error] = model_data[:m][:model_error]
|
||||
|
||||
neuron[:knowledgefn_error] = model_data[:m][:knowledgeFn][k][:knowledgeFn_error]
|
||||
|
||||
neuron[:model_name] = model_name
|
||||
|
||||
# use as identifier durin debug
|
||||
# neuron[:random] = Random.rand(1:100)
|
||||
|
||||
push!(payload[:neurons_list], neuron)
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
push!(payloads, payload)
|
||||
end
|
||||
return payloads
|
||||
end
|
||||
|
||||
function send_to_DB(model_name::String, experiment_number::Int, episode_number::Int,
|
||||
tick_number::Int, model_json_string::String, redis_server_ip::String,
|
||||
pub_channel::String, sub_channel::String)
|
||||
model_ordereddict = OrderedDict(JSON3.read(model_json_string))
|
||||
payloads = data_prep_for_DB(model_name, experiment_number, episode_number, tick_number,
|
||||
model_ordereddict)
|
||||
|
||||
for payload in payloads
|
||||
# ask raw data service whether it is ready
|
||||
# println("checking raw_data_db_service")
|
||||
ask = Dict(:sender => "ironpen_ai",
|
||||
:topic => "whois", # [uuid1(), "whois"] to get name of the receiver
|
||||
:topic_id => uuid1(),
|
||||
:responding_to => nothing, # receiver fills in the message uuid it is responding to
|
||||
:communication_channel => sub_channel, # a channel that sender wants receiver to send message to or "none" to get message at receiver's default respond channel
|
||||
:instruction => nothing,
|
||||
:payload => nothing,
|
||||
:isreturn => true)
|
||||
incoming_message = Utils.service_query(redis_server_ip, pub_channel, sub_channel, ask)
|
||||
# println("raw_data_db_service ok")
|
||||
if UUID(incoming_message[:responding_to]) == ask[:topic_id]
|
||||
message = Dict(:sender => "ironpen_ai",
|
||||
:topic => "process", # [uuid1(), "whois"] to get name of the receiver
|
||||
:topic_id => uuid1(),
|
||||
:responding_to => nothing, # receiver fills in the message uuid it is responding to
|
||||
:communication_channel => sub_channel, # a channel that sender wants receiver to send message to or "none" to get message at receiver's default respond channel
|
||||
:instruction => "insert",
|
||||
:payload => payload,
|
||||
:isreturn => false)
|
||||
|
||||
result = Utils.service_query(redis_server_ip, pub_channel, sub_channel, message)
|
||||
# println("published")
|
||||
else
|
||||
error("raw_data_db_service not respond")
|
||||
end
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
114
oldVersion/0.0.2/src/Ironpen.jl
Normal file
114
oldVersion/0.0.2/src/Ironpen.jl
Normal file
@@ -0,0 +1,114 @@
|
||||
module Ironpen
|
||||
|
||||
export kfn_1, synapticConnStrength!
|
||||
|
||||
|
||||
""" 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("types.jl")
|
||||
using .types # bring model into this module namespace (this module is a parent module)
|
||||
|
||||
include("snn_utils.jl")
|
||||
using .snn_utils
|
||||
|
||||
# include("Save_and_load.jl")
|
||||
# using .Save_and_load
|
||||
|
||||
# include("DB_services.jl")
|
||||
# using .DB_services
|
||||
|
||||
include("forward.jl")
|
||||
using .forward
|
||||
|
||||
include("learn.jl")
|
||||
using .learn
|
||||
|
||||
# include("readout.jl")
|
||||
# using .readout
|
||||
|
||||
# include("interface.jl")
|
||||
# using .interface
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" version 0.0.2
|
||||
Todo:
|
||||
[*2] implement connection strength based on right or wrong answer
|
||||
[*1] how to manage how much constrength increase and decrease
|
||||
[4] implement dormant connection
|
||||
[3] Δweight * connection strength
|
||||
[] using RL to control learning signal
|
||||
[] consider using Dates.now() instead of timestamp because time_stamp may overflow
|
||||
[5] training should include adjusting α, neuron membrane potential decay factor
|
||||
which defined by neuron.tau_m formula in type.jl
|
||||
|
||||
[DONE] each knowledgeFn should have its own noise generater
|
||||
[DONE] where to put pseudo derivative (n.phi)
|
||||
[DONE] add excitatory, inhabitory to neuron
|
||||
[DONE] implement "start learning", reset learning and "learning", "end_learning and
|
||||
"inference"
|
||||
[DONE] output neuron connect to random multiple compute neurons and overall have
|
||||
the same structure as lif
|
||||
[DONE] time-based learning method based on new error formula
|
||||
(use output vt compared to vth instead of late time)
|
||||
if output neuron not activate when it should, use output neuron's
|
||||
(vth - vt)*100/vth as error
|
||||
if output neuron activates when it should NOT, use output neuron's
|
||||
(vt*100)/vth as error
|
||||
[DONE] use LinearAlgebra.normalize!(vector, 1) to adjust weight after weight merge
|
||||
[DONE] reset_epsilonRec after ΔwRecChange is calculated
|
||||
[DONE] synaptic connection strength concept. use sigmoid, turn connection offline
|
||||
[DONE] wRec should not normalized whole. it should be local 5 conn normalized.
|
||||
[DONE] neuroplasticity() i.e. change connection
|
||||
[DONE] add multi threads
|
||||
[DONE] during 0 training if 1-9 output neuron fires, adjust weight only those neurons
|
||||
[DONE] add maximum weight cap of each connection
|
||||
[DONE] weaker connection should be harder to increase strength. It requires a lot of
|
||||
repeat activation to get it stronger. While strong connction requires a lot of
|
||||
inactivation to get it weaker. The concept is strong connection will lock
|
||||
correct neural pathway through repeated use of the right connection i.e. keep training
|
||||
on the correct answer -> strengthen the right neural pathway (connections) ->
|
||||
this correct neural pathway resist to change.
|
||||
Not used connection should dissapear (forgetting).
|
||||
|
||||
Change from version: v06_36a
|
||||
-
|
||||
|
||||
All features
|
||||
- multidispatch + for loop as main compute method
|
||||
- hard connection constrain yes
|
||||
- normalize output yes
|
||||
- allow -w_rec yes
|
||||
- voltage drop when neuron fires voltage drop equals to vth
|
||||
- v_t decay during refractory
|
||||
duration exponantial decay
|
||||
- input data population encoding, each pixel data =>
|
||||
population encoding, ralative between pixel data
|
||||
- compute neuron weight init rand()
|
||||
- output neuron weight init randn()
|
||||
"""
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module end
|
||||
200
oldVersion/0.0.2/src/WPembeddings.jl
Normal file
200
oldVersion/0.0.2/src/WPembeddings.jl
Normal file
@@ -0,0 +1,200 @@
|
||||
"
|
||||
version 0.4
|
||||
Word and Positional embedding module
|
||||
"
|
||||
module WPembeddings
|
||||
|
||||
using Embeddings
|
||||
using JSON3
|
||||
using Redis
|
||||
|
||||
include("Utils.jl")
|
||||
|
||||
export get_word_embedding, get_positional_embedding, wp_embedding
|
||||
|
||||
|
||||
#----------------------------------------------------------------------------------------------
|
||||
# user setting for word embedding
|
||||
GloVe_embedding_filepath = "C:\\myWork\\my_projects\\AI\\NLP\\my_NLP\\glove.840B.300d.txt"
|
||||
max_GloVe_vocab_size = 0 # size 10000+ or "all"
|
||||
#----------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# load GloVe word embedding. URL of the embedding file: https://nlp.stanford.edu/projects/glove/
|
||||
if max_GloVe_vocab_size == 0
|
||||
# don't load vocab
|
||||
elseif max_GloVe_vocab_size != "all"
|
||||
@time const embtable = Embeddings.load_embeddings(GloVe{:en}, GloVe_embedding_filepath,
|
||||
max_vocab_size=max_GloVe_vocab_size) # size 10000 or something
|
||||
const get_word_index = Dict(word=>ii for (ii,word) in enumerate(embtable.vocab))
|
||||
else
|
||||
@time const embtable = Embeddings.load_embeddings(GloVe{:en}, GloVe_embedding_filepath)
|
||||
const get_word_index = Dict(word=>ii for (ii,word) in enumerate(embtable.vocab))
|
||||
end
|
||||
|
||||
|
||||
# if max_GloVe_vocab_size != "all"
|
||||
# @time const embtable = Embeddings.load_embeddings(GloVe{:en}, GloVe_embedding_filepath,
|
||||
# max_vocab_size=max_GloVe_vocab_size) # size 10000 or something
|
||||
# const get_word_index = Dict(word=>ii for (ii,word) in enumerate(embtable.vocab))
|
||||
# elseif max_GloVe_vocab_size == 0
|
||||
# else
|
||||
# @time const embtable = Embeddings.load_embeddings(GloVe{:en}, GloVe_embedding_filepath)
|
||||
# const get_word_index = Dict(word=>ii for (ii,word) in enumerate(embtable.vocab))
|
||||
# end
|
||||
|
||||
|
||||
"""
|
||||
get_word_embedding(word::String)
|
||||
|
||||
Get embedding vector of a word. Its dimention is depend on GloVe file used
|
||||
|
||||
# Example
|
||||
|
||||
we_matrix = get_word_embedding("blue")
|
||||
"""
|
||||
function get_word_embedding(word::String)
|
||||
index = get_word_index[word]
|
||||
embedding = embtable.embeddings[:,index]
|
||||
return embedding
|
||||
end
|
||||
|
||||
|
||||
"""
|
||||
get_positional_embedding(total_word_position::Integer, word_embedding_dimension::Integer=300)
|
||||
|
||||
return positional embedding matrix of size [word_embedding_dimension * total_word_position]
|
||||
|
||||
# Example
|
||||
|
||||
pe_matrix = get_positional_embedding(length(content), 300)
|
||||
"""
|
||||
function get_positional_embedding(total_word_position::Integer, word_embedding_dimension::Integer=300)
|
||||
d = word_embedding_dimension
|
||||
p = total_word_position
|
||||
pe = [x = i%2 == 0 ? cos(j/(10^(2i/d))) : sin(j/(10^(2i/d))) for i = 1:d, j = 1:p]
|
||||
return pe
|
||||
|
||||
end
|
||||
|
||||
|
||||
"""
|
||||
wp_embedding(tokenized_word::Array{String}, positional_embedding::Bool=false)
|
||||
|
||||
Word embedding with positional embedding.
|
||||
tokenized_word = sentense's tokenized word (not sentense in English definition but BERT definition.
|
||||
1-BERT sentense can be 20+ English's sentense)
|
||||
|
||||
# Example
|
||||
|
||||
|
||||
"""
|
||||
function wp_embedding(tokenized_word::Array{String}, positional_embedding::Bool=false)
|
||||
we_matrix = 0
|
||||
for (i, v) in enumerate(tokenized_word)
|
||||
if i == 1
|
||||
we_matrix = get_word_embedding(v)
|
||||
else
|
||||
we_matrix = hcat(we_matrix, get_word_embedding(v))
|
||||
end
|
||||
end
|
||||
|
||||
if positional_embedding
|
||||
pe_matrix = get_positional_embedding(length(tokenized_word), 300) # positional embedding
|
||||
wp_matrix = we_matrix + pe_matrix
|
||||
|
||||
return wp_matrix
|
||||
else
|
||||
return we_matrix
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
"""
|
||||
wp_query(tokenized_word::Array{String}, positional_embedding::Bool=false)
|
||||
|
||||
convert tokenized_word into JSON3 String to be sent to GloVe docker server
|
||||
"""
|
||||
function wp_query_send(tokenized_word::Array{String}, positional_embedding::Bool=false)
|
||||
d = Dict("tokenized_word"=> tokenized_word, "positional_embedding"=>positional_embedding)
|
||||
json3_str = JSON3.write(d)
|
||||
return json3_str
|
||||
end
|
||||
|
||||
|
||||
"""
|
||||
wp_query(tokenized_word::Array{String}, positional_embedding::Bool=false)
|
||||
|
||||
Using inside word_embedding_server to receive word embedding job
|
||||
convert JSON3 String into tokenized_word and positional_embedding
|
||||
"""
|
||||
function wp_query_receive(json3_str::String)
|
||||
d = JSON3.read(json3_str)
|
||||
tokenized_word = Array(d.tokenized_word)
|
||||
positional_embedding = d.positional_embedding
|
||||
|
||||
return tokenized_word, positional_embedding
|
||||
end
|
||||
|
||||
|
||||
"""
|
||||
Send tokenized_word to word_embedding_server and return word embedding
|
||||
|
||||
# Example
|
||||
|
||||
WPembeddings.query_wp_server(tokenized_word)
|
||||
"""
|
||||
function query_wp_server(query;
|
||||
host="0.0.0.0",
|
||||
port=6379,
|
||||
publish_channel="word_embedding_server/input",
|
||||
positional_encoding=true)
|
||||
|
||||
# channel used to receive JSON String from word_embedding_server
|
||||
wp_channel = Channel(10)
|
||||
function wp_receive(x)
|
||||
array = Utils.JSON3_str_to_Array(x)
|
||||
put!(wp_channel, array)
|
||||
end
|
||||
|
||||
# establish connection to word_embedding_server using default port
|
||||
conn = Redis.RedisConnection(host=host, port=port)
|
||||
sub = Redis.open_subscription(conn)
|
||||
Redis.subscribe(sub, "word_embedding_server/output", wp_receive)
|
||||
# Redis.subscribe(sub, "word_embedding_server/output", WPembeddings.wp_receive)
|
||||
|
||||
# set positional_encoding = true to enable positional encoding
|
||||
query = WPembeddings.wp_query_send(query, positional_encoding)
|
||||
# Ask word_embedding_server for word embedding
|
||||
Redis.publish(conn, publish_channel, query);
|
||||
wait(wp_channel) # wait for word_embedding_server to response
|
||||
embedded_word = take!(wp_channel)
|
||||
|
||||
disconnect(conn)
|
||||
return embedded_word
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end
|
||||
292
oldVersion/0.0.2/src/forward.jl
Normal file
292
oldVersion/0.0.2/src/forward.jl
Normal file
@@ -0,0 +1,292 @@
|
||||
module forward
|
||||
|
||||
using Statistics, Random, LinearAlgebra, JSON3
|
||||
using GeneralUtils
|
||||
using ..types, ..snn_utils
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" Model forward()
|
||||
"""
|
||||
function (m::model)(input_data::AbstractVector)
|
||||
m.timeStep += 1
|
||||
|
||||
# process all corresponding KFN
|
||||
# raw_model_respond, outputNeuron_v_t1, firedNeurons_t1 = m.knowledgeFn[:I](m, input_data)
|
||||
|
||||
# the 2nd return (KFN error) should not be used as model error but I use it because there is
|
||||
# only one KFN in a model right now
|
||||
return m.knowledgeFn[:I](m, input_data)
|
||||
end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" knowledgeFn forward()
|
||||
"""
|
||||
|
||||
function (kfn::kfn_1)(m::model, input_data::AbstractVector)
|
||||
kfn.timeStep = m.timeStep
|
||||
|
||||
kfn.learningStage = m.learningStage
|
||||
|
||||
if kfn.learningStage == "start_learning"
|
||||
# reset params here instead of at the end_learning so that neuron's parameter data
|
||||
# don't gets wiped and can be logged for visualization later
|
||||
for n in kfn.neuronsArray
|
||||
# epsilonRec need to be reset because it counting how many each synaptic fires and
|
||||
# use this info to calculate how much synaptic weight should be adjust
|
||||
resetLearningParams!(n)
|
||||
end
|
||||
|
||||
for n in kfn.outputNeuronsArray
|
||||
# epsilonRec need to be reset because it counting how many each synaptic fires and
|
||||
# use this info to calculate how much synaptic weight should be adjust
|
||||
resetLearningParams!(n)
|
||||
end
|
||||
|
||||
# clear variables
|
||||
kfn.firedNeurons = Int64[]
|
||||
kfn.firedNeurons_t0 = Bool[]
|
||||
kfn.firedNeurons_t1 = Bool[]
|
||||
|
||||
kfn.learningStage = "learning"
|
||||
m.learningStage = kfn.learningStage
|
||||
end
|
||||
|
||||
# generate noise
|
||||
noise = [GeneralUtils.randomChoiceWithProb([true, false],[0.5,0.5])
|
||||
for i in 1:length(input_data)]
|
||||
# noise = [rand(rng, Distributions.Binomial(1, 0.5)) for i in 1:10] # another option
|
||||
|
||||
input_data = [noise; input_data] # noise must start from neuron id 1
|
||||
|
||||
for n in kfn.neuronsArray
|
||||
timestep_forward!(n)
|
||||
end
|
||||
for n in kfn.outputNeuronsArray
|
||||
timestep_forward!(n)
|
||||
end
|
||||
|
||||
# pass input_data into input neuron.
|
||||
# number of data point equals to number of input neuron starting from id 1
|
||||
for (i, data) in enumerate(input_data)
|
||||
kfn.neuronsArray[i].z_t1 = data
|
||||
end
|
||||
|
||||
kfn.firedNeurons_t0 = [n.z_t for n in kfn.neuronsArray] #TODO check if it is used?
|
||||
|
||||
# Threads.@threads for n in kfn.neuronsArray
|
||||
for n in kfn.neuronsArray
|
||||
n(kfn)
|
||||
end
|
||||
|
||||
kfn.firedNeurons_t1 = [n.z_t1 for n in kfn.neuronsArray]
|
||||
append!(kfn.firedNeurons, findall(kfn.firedNeurons_t1)) # store id of neuron that fires
|
||||
kfn.firedNeurons |> unique! # use for random new neuron connection
|
||||
|
||||
# Threads.@threads for n in kfn.outputNeuronsArray
|
||||
for n in kfn.outputNeuronsArray
|
||||
n(kfn)
|
||||
end
|
||||
|
||||
out = [n.z_t1 for n in kfn.outputNeuronsArray]
|
||||
outputNeuron_v_t1 = [n.v_t1 for n in kfn.outputNeuronsArray]
|
||||
|
||||
return out::Array{Bool}, outputNeuron_v_t1::Array{Float64}, sum(kfn.firedNeurons_t1),
|
||||
kfn.exSignalSum, kfn.inSignalsum
|
||||
end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" passthroughNeuron forward()
|
||||
"""
|
||||
function (n::passthroughNeuron)(kfn::knowledgeFn)
|
||||
n.timeStep = kfn.timeStep
|
||||
end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" lifNeuron forward()
|
||||
"""
|
||||
function (n::lifNeuron)(kfn::knowledgeFn)
|
||||
n.timeStep = kfn.timeStep
|
||||
|
||||
# pulling other neuron's firing status at time t
|
||||
n.z_i_t = getindex(kfn.firedNeurons_t0, n.subscriptionList)
|
||||
n.z_i_t_commulative += n.z_i_t
|
||||
|
||||
if n.refractoryCounter != 0
|
||||
n.refractoryCounter -= 1
|
||||
|
||||
# neuron is in refractory state, skip all calculation
|
||||
n.z_t1 = false # used by timestep_forward() in kfn. Set to zero because neuron spike
|
||||
# last only 1 timestep follow by a period of refractory.
|
||||
n.recSignal = n.recSignal * 0.0
|
||||
|
||||
# decay of v_t1
|
||||
n.v_t1 = n.alpha * n.v_t
|
||||
else
|
||||
n.recSignal = sum(n.wRec .* n.z_i_t) # signal from other neuron that this neuron subscribed
|
||||
n.alpha_v_t = n.alpha * n.v_t
|
||||
n.v_t1 = n.alpha_v_t + n.recSignal
|
||||
n.v_t1 = no_negative!(n.v_t1)
|
||||
|
||||
if n.v_t1 > n.v_th
|
||||
n.z_t1 = true
|
||||
n.refractoryCounter = n.refractoryDuration
|
||||
n.firingCounter += 1
|
||||
n.v_t1 = n.vRest
|
||||
else
|
||||
n.z_t1 = false
|
||||
end
|
||||
|
||||
# there is a difference from alif formula
|
||||
n.phi = (n.gammaPd / n.v_th) * max(0, 1 - (n.v_t1 - n.v_th) / n.v_th)
|
||||
n.decayedEpsilonRec = n.alpha * n.epsilonRec
|
||||
n.epsilonRec = n.decayedEpsilonRec + n.z_i_t
|
||||
end
|
||||
end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" alifNeuron forward()
|
||||
"""
|
||||
function (n::alifNeuron)(kfn::knowledgeFn)
|
||||
n.timeStep = kfn.timeStep
|
||||
|
||||
n.z_i_t = getindex(kfn.firedNeurons_t0, n.subscriptionList)
|
||||
n.z_i_t_commulative += n.z_i_t
|
||||
|
||||
if n.refractoryCounter != 0
|
||||
n.refractoryCounter -= 1
|
||||
|
||||
# neuron is in refractory state, skip all calculation
|
||||
n.z_t1 = false # used by timestep_forward() in kfn. Set to zero because neuron spike last only 1 timestep follow by a period of refractory.
|
||||
n.a = (n.rho * n.a) + ((1 - n.rho) * n.z_t)
|
||||
n.recSignal = n.recSignal * 0.0
|
||||
|
||||
# decay of v_t1
|
||||
n.v_t1 = n.alpha * n.v_t
|
||||
n.phi = 0
|
||||
else
|
||||
n.a = (n.rho * n.a) + ((1 - n.rho) * n.z_t)
|
||||
n.av_th = n.v_th + (n.beta * n.a)
|
||||
n.recSignal = sum(n.wRec .* n.z_i_t) # signal from other neuron that this neuron subscribed
|
||||
n.alpha_v_t = n.alpha * n.v_t
|
||||
n.v_t1 = n.alpha_v_t + n.recSignal
|
||||
n.v_t1 = no_negative!(n.v_t1)
|
||||
if n.v_t1 > n.av_th
|
||||
n.z_t1 = true
|
||||
n.refractoryCounter = n.refractoryDuration
|
||||
n.firingCounter += 1
|
||||
n.v_t1 = n.vRest
|
||||
else
|
||||
n.z_t1 = false
|
||||
end
|
||||
|
||||
# there is a difference from lif formula
|
||||
n.phi = (n.gammaPd / n.v_th) * max(0, 1 - (n.v_t1 - n.av_th) / n.v_th)
|
||||
n.decayedEpsilonRec = n.alpha * n.epsilonRec
|
||||
n.epsilonRec = n.decayedEpsilonRec + n.z_i_t
|
||||
n.epsilonRecA = (n.phi * n.epsilonRec) +
|
||||
((n.rho - (n.phi * n.beta)) * n.epsilonRecA)
|
||||
end
|
||||
end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" linearNeuron forward()
|
||||
In this implementation, each output neuron is fully connected to every lif and alif neuron.
|
||||
"""
|
||||
function (n::linearNeuron)(kfn::T) where T<:knowledgeFn
|
||||
n.timeStep = kfn.timeStep
|
||||
|
||||
# pulling other neuron's firing status at time t
|
||||
n.z_i_t = getindex(kfn.firedNeurons_t1, n.subscriptionList)
|
||||
n.z_i_t_commulative += n.z_i_t
|
||||
|
||||
if n.refractoryCounter != 0
|
||||
n.refractoryCounter -= 1
|
||||
|
||||
# neuron is in refractory state, skip all calculation
|
||||
n.z_t1 = false # used by timestep_forward() in kfn. Set to zero because neuron spike
|
||||
# last only 1 timestep follow by a period of refractory.
|
||||
n.recSignal = n.recSignal * 0.0
|
||||
|
||||
# decay of v_t1
|
||||
n.v_t1 = n.alpha * n.v_t
|
||||
n.vError = n.v_t1 # store voltage that will be used to calculate error later
|
||||
else
|
||||
recSignal = n.wRec .* n.z_i_t
|
||||
if n.id == 1 #FIXME debugging output neuron dead
|
||||
for i in recSignal
|
||||
if i > 0
|
||||
kfn.exSignalSum += i
|
||||
elseif i < 0
|
||||
kfn.inSignalsum += i
|
||||
else
|
||||
end
|
||||
end
|
||||
end
|
||||
n.recSignal = sum(recSignal) # signal from other neuron that this neuron subscribed
|
||||
n.alpha_v_t = n.alpha * n.v_t
|
||||
n.v_t1 = n.alpha_v_t + n.recSignal
|
||||
n.v_t1 = no_negative!(n.v_t1)
|
||||
n.vError = n.v_t1 # store voltage that will be used to calculate error later
|
||||
if n.v_t1 > n.v_th
|
||||
n.z_t1 = true
|
||||
n.refractoryCounter = n.refractoryDuration
|
||||
n.firingCounter += 1
|
||||
n.v_t1 = n.vRest
|
||||
else
|
||||
n.z_t1 = false
|
||||
end
|
||||
|
||||
# there is a difference from alif formula
|
||||
n.phi = (n.gammaPd / n.v_th) * max(0, 1 - (n.v_t1 - n.v_th) / n.v_th)
|
||||
n.decayedEpsilonRec = n.alpha * n.epsilonRec
|
||||
n.epsilonRec = n.decayedEpsilonRec + n.z_i_t
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # end module
|
||||
79
oldVersion/0.0.2/src/interface.jl
Normal file
79
oldVersion/0.0.2/src/interface.jl
Normal file
@@ -0,0 +1,79 @@
|
||||
module interface
|
||||
|
||||
|
||||
# export
|
||||
|
||||
# using
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end
|
||||
181
oldVersion/0.0.2/src/learn.jl
Normal file
181
oldVersion/0.0.2/src/learn.jl
Normal file
@@ -0,0 +1,181 @@
|
||||
module learn
|
||||
|
||||
using Statistics, Random, LinearAlgebra, JSON3
|
||||
using GeneralUtils
|
||||
using ..types, ..snn_utils
|
||||
|
||||
export learn!
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
function learn!(m::model, modelRespond::AbstractVector, correctAnswer::Union{AbstractVector, Nothing})
|
||||
if correctAnswer === nothing
|
||||
correctAnswer_I = BitArray(zeros(length(modelRespond)))
|
||||
else
|
||||
correctAnswer_I = Bool.(correctAnswer) # correct answer for kfn I
|
||||
end
|
||||
|
||||
learn!(m.knowledgeFn[:I], correctAnswer_I)
|
||||
end
|
||||
|
||||
""" knowledgeFn learn()
|
||||
"""
|
||||
function learn!(kfn::kfn_1, correctAnswer::BitVector)
|
||||
# compute kfn error for each neuron
|
||||
# outs = [n.z_t1 for n in kfn.outputNeuronsArray]
|
||||
# for (i, out) in enumerate(outs)
|
||||
# if out != correctAnswer[i] # need to adjust weight
|
||||
# kfnError = ( (kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError) *
|
||||
# 100 / kfn.outputNeuronsArray[i].v_th )
|
||||
|
||||
# Threads.@threads for n in kfn.neuronsArray
|
||||
# # for n in kfn.neuronsArray
|
||||
# learn!(n, kfnError)
|
||||
# end
|
||||
|
||||
# learn!(kfn.outputNeuronsArray[i], kfnError)
|
||||
# end
|
||||
# end
|
||||
|
||||
# compute kfn error for each neuron
|
||||
outs = [n.z_t1 for n in kfn.outputNeuronsArray]
|
||||
for (i, out) in enumerate(outs)
|
||||
if out == correctAnswer # output correct
|
||||
kfnError = 0.0
|
||||
Threads.@threads for n in kfn.neuronsArray # multithread is not atomic and causing error
|
||||
# for n in kfn.neuronsArray
|
||||
compute_wRecChange!(n, kfnError)
|
||||
learn!(n, kfn.firedNeurons, kfn.nExInType, true)
|
||||
end
|
||||
compute_wRecChange!(kfn.outputNeuronsArray[i], kfnError)
|
||||
learn!(kfn.outputNeuronsArray[i], kfn.firedNeurons, kfn.nExInType,
|
||||
kfn.kfnParams[:totalInputPort], true)
|
||||
else
|
||||
kfnError = ( (kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError) *
|
||||
100.0 / kfn.outputNeuronsArray[i].v_th )^2
|
||||
Threads.@threads for n in kfn.neuronsArray # multithread is not atomic and causing error
|
||||
# for n in kfn.neuronsArray
|
||||
compute_wRecChange!(n, kfnError)
|
||||
learn!(n, kfn.firedNeurons, kfn.nExInType, false)
|
||||
end
|
||||
compute_wRecChange!(kfn.outputNeuronsArray[i], kfnError)
|
||||
learn!(kfn.outputNeuronsArray[i], kfn.firedNeurons, kfn.nExInType,
|
||||
kfn.kfnParams[:totalInputPort], false)
|
||||
end
|
||||
end
|
||||
|
||||
# wrap up learning session
|
||||
if kfn.learningStage == "end_learning"
|
||||
kfn.learningStage = "inference"
|
||||
end
|
||||
end
|
||||
|
||||
function compute_wRecChange!(n::passthroughNeuron, error::Float64)
|
||||
# skip
|
||||
end
|
||||
|
||||
function compute_wRecChange!(n::lifNeuron, error::Float64)
|
||||
n.eRec = n.phi * n.epsilonRec
|
||||
ΔwRecChange = n.eta * error * n.eRec
|
||||
n.wRecChange .+= ΔwRecChange
|
||||
reset_epsilonRec!(n)
|
||||
end
|
||||
|
||||
function compute_wRecChange!(n::alifNeuron, error::Float64)
|
||||
n.eRec_v = n.phi * n.epsilonRec
|
||||
n.eRec_a = -n.phi * n.beta * n.epsilonRecA
|
||||
n.eRec = n.eRec_v + n.eRec_a
|
||||
ΔwRecChange = n.eta * error * n.eRec
|
||||
n.wRecChange .+= ΔwRecChange
|
||||
reset_epsilonRec!(n)
|
||||
reset_epsilonRecA!(n)
|
||||
end
|
||||
|
||||
function compute_wRecChange!(n::linearNeuron, error::Float64)
|
||||
n.eRec = n.phi * n.epsilonRec
|
||||
ΔwRecChange = n.eta * error * n.eRec
|
||||
n.wRecChange .+= ΔwRecChange
|
||||
reset_epsilonRec!(n)
|
||||
end
|
||||
|
||||
function learn!(n::T, firedNeurons, nExInType, correctAnswer) where T<:inputNeuron
|
||||
# skip
|
||||
end
|
||||
|
||||
function learn!(n::T, firedNeurons, nExInType, correctAnswer) where T<:computeNeuron
|
||||
wSign_0 = sign.(n.wRec) # original sign
|
||||
#TESTING strong connection gets less weight change, weak connection gets more weight change
|
||||
n.wRecChange .*= (connStrengthAdjust.(n.synapticStrength))
|
||||
n.wRec += n.wRecChange # merge wRecChange into wRec
|
||||
reset_wRecChange!(n)
|
||||
wSign_1 = sign.(n.wRec) # check for fliped sign, 1 indicates non-fliped sign
|
||||
nonFlipedSign = isequal.(wSign_0, wSign_1) # 1 not fliped, 0 fliped
|
||||
# normalize wRec peak to prevent input signal overwhelming neuron
|
||||
normalizePeak!(n.wRec, n.wRecChange, 2)
|
||||
# set weight that fliped sign to 0 for random new connection
|
||||
n.wRec .*= nonFlipedSign
|
||||
capMaxWeight!(n.wRec) # cap maximum weight
|
||||
|
||||
synapticConnStrength!(n, correctAnswer)
|
||||
neuroplasticity!(n, firedNeurons, nExInType)
|
||||
end
|
||||
|
||||
function learn!(n::T, firedNeurons, nExInType, totalInputPort, correctAnswer) where T<:outputNeuron
|
||||
wSign_0 = sign.(n.wRec) # original sign
|
||||
#TESTING strong connection gets less weight change, weak connection gets more weight change
|
||||
n.wRecChange .*= (connStrengthAdjust.(n.synapticStrength))
|
||||
n.wRec += n.wRecChange
|
||||
reset_wRecChange!(n)
|
||||
wSign_1 = sign.(n.wRec) # check for fliped sign, 1 indicates non-fliped sign
|
||||
nonFlipedSign = isequal.(wSign_0, wSign_1) # 1 not fliped, 0 fliped
|
||||
# normalize wRec peak to prevent input signal overwhelming neuron
|
||||
normalizePeak!(n.wRec, n.wRecChange, 2)
|
||||
# set weight that fliped sign to 0 for random new connection
|
||||
n.wRec .*= nonFlipedSign
|
||||
capMaxWeight!(n.wRec) # cap maximum weight
|
||||
|
||||
synapticConnStrength!(n, correctAnswer)
|
||||
neuroplasticity!(n,firedNeurons, nExInType, totalInputPort)
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module end
|
||||
83
oldVersion/0.0.2/src/readout.jl
Normal file
83
oldVersion/0.0.2/src/readout.jl
Normal file
@@ -0,0 +1,83 @@
|
||||
module readout
|
||||
|
||||
using Flux.Optimise: apply!
|
||||
|
||||
using Statistics, Flux, Random, LinearAlgebra
|
||||
using GeneralUtils
|
||||
using ..types, ..readout, ..learn, ..forward
|
||||
|
||||
export readout!
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
function readout!(kfn::knowledgeFn; correctAnswer=nothing) # correctAnswer=nothing use for inference
|
||||
# clear output to start reading
|
||||
# kfn.on_out_t0 *= 0.0 #FIXME should I clear it before RSNN readout?
|
||||
respondCount = zeros(length(kfn.on_out_t0))
|
||||
|
||||
# prepare signal used to read RSNN
|
||||
readoutSignal = zeros(length(kfn.passthrough_zt0))
|
||||
readoutSignal[1] = 1
|
||||
readoutSignal[end] = 1
|
||||
|
||||
lastKfnTimeStamp = kfn.timeStamp[1]
|
||||
for t in 1:kfn.on_tauOut[1]
|
||||
# println("t $t")
|
||||
tick = lastKfnTimeStamp + t
|
||||
if t == kfn.on_tauOut[1]
|
||||
println("")
|
||||
end
|
||||
if kfn.learningStage[1] == 0 # RSNN is in inference mode, do not change marker
|
||||
# skip
|
||||
else # RSNN is in learning mode, assign marker for commiting wChange at the end of readout window.
|
||||
marker = t == kfn.on_tauOut[1] ? 4 : kfn.learningStage[1]
|
||||
end
|
||||
|
||||
# RSNN forward ----------
|
||||
singleTimeReadout, on_out_t0, softmaxRespond = kfn(readoutSignal, tick, marker,
|
||||
correctAnswer=correctAnswer)
|
||||
_, _, respondPosition = Utils.findMax(softmaxRespond)
|
||||
respondCount += respondPosition
|
||||
|
||||
if correctAnswer !== nothing
|
||||
kfn.kfnError = [Flux.logitcrossentropy(on_out_t0, correctAnswer)]
|
||||
learn!(kfn)
|
||||
end
|
||||
end
|
||||
|
||||
_, readout, _ = Utils.findMax(respondCount/kfn.on_tauOut[1])
|
||||
|
||||
return readout, kfn.on_out_t0
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
500
oldVersion/0.0.2/src/snn_utils.jl
Normal file
500
oldVersion/0.0.2/src/snn_utils.jl
Normal file
@@ -0,0 +1,500 @@
|
||||
module snn_utils
|
||||
|
||||
export calculate_α, calculate_ρ, calculate_k, timestep_forward!, init_neuron, no_negative!,
|
||||
precision, calculate_w_change!, store_knowledgefn_error!, interneurons_adjustment!,
|
||||
reset_z_t!, resetLearningParams!, reset_learning_history_params!, reset_epsilonRec!,
|
||||
reset_epsilonRecA!, synapticConnStrength!, normalizePeak!, reset_wRecChange!,
|
||||
firing_rate_error!, firing_rate_regulator!, update_Bn!, cal_firing_reg!,
|
||||
neuroplasticity!, shakeup!, reset_learning_no_wchange!, adjust_internal_learning_rate!,
|
||||
gradient_withloss, capMaxWeight!, connStrengthAdjust
|
||||
|
||||
using Statistics, Random, LinearAlgebra, Distributions, Zygote, Flux
|
||||
using GeneralUtils
|
||||
using ..types
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
function timestep_forward!(x::passthroughNeuron)
|
||||
x.z_t = x.z_t1
|
||||
end
|
||||
|
||||
function timestep_forward!(x::Union{computeNeuron, outputNeuron})
|
||||
x.z_t = x.z_t1
|
||||
x.v_t = x.v_t1
|
||||
end
|
||||
|
||||
no_negative!(x) = x < 0.0 ? 0.0 : x
|
||||
precision(x::Array{<:Array}) = ( std(mean.(x)) / mean(mean.(x)) ) * 100
|
||||
|
||||
# reset functions for LIF/ALIF neuron
|
||||
reset_last_firing_time!(n::computeNeuron) = n.lastFiringTime = 0.0
|
||||
reset_refractory_state_active!(n::computeNeuron) = n.refractory_state_active = false
|
||||
reset_v_t!(n::neuron) = n.v_t = n.vRest
|
||||
reset_z_t!(n::computeNeuron) = n.z_t = false
|
||||
reset_epsilonRec!(n::computeNeuron) = n.epsilonRec = n.epsilonRec * 0.0
|
||||
reset_epsilonRec!(n::outputNeuron) = n.epsilonRec = n.epsilonRec * 0.0
|
||||
reset_epsilonRecA!(n::alifNeuron) = n.epsilonRecA = n.epsilonRecA * 0.0
|
||||
reset_epsilon_in!(n::computeNeuron) = n.epsilon_in = isnothing(n.epsilon_in) ? nothing : n.epsilon_in * 0.0
|
||||
reset_error!(n::Union{computeNeuron, outputNeuron}) = n.error = nothing
|
||||
reset_w_in_change!(n::computeNeuron) = n.w_in_change = isnothing(n.w_in_change) ? nothing : n.w_in_change * 0.0
|
||||
reset_wRecChange!(n::Union{computeNeuron, outputNeuron}) = n.wRecChange = n.wRecChange * 0.0
|
||||
reset_a!(n::alifNeuron) = n.a = n.a * 0.0
|
||||
reset_reg_voltage_a!(n::computeNeuron) = n.reg_voltage_a = n.reg_voltage_a * 0.0
|
||||
reset_reg_voltage_b!(n::computeNeuron) = n.reg_voltage_b = n.reg_voltage_b * 0.0
|
||||
reset_reg_voltage_error!(n::computeNeuron) = n.reg_voltage_error = n.reg_voltage_error * 0.0
|
||||
reset_firing_counter!(n::Union{computeNeuron, outputNeuron}) = n.firingCounter = n.firingCounter * 0.0
|
||||
reset_firing_diff!(n::Union{computeNeuron, outputNeuron}) = n.firingDiff = n.firingDiff * 0.0
|
||||
reset_refractoryCounter!(n::Union{computeNeuron, outputNeuron}) = n.refractoryCounter = n.refractoryCounter * 0.0
|
||||
reset_z_i_t_commulative!(n::Union{computeNeuron, outputNeuron}) = n.z_i_t_commulative = n.z_i_t_commulative * 0.0
|
||||
|
||||
# reset function for output neuron
|
||||
reset_epsilon_j!(n::linearNeuron) = n.epsilon_j = n.epsilon_j * 0.0
|
||||
reset_out_t!(n::linearNeuron) = n.out_t = n.out_t * 0.0
|
||||
reset_w_out_change!(n::linearNeuron) = n.w_out_change = n.w_out_change * 0.0
|
||||
reset_b_change!(n::linearNeuron) = n.b_change = n.b_change * 0.0
|
||||
|
||||
|
||||
""" Reset a part of learning-related params that used to collect learning history during learning
|
||||
session
|
||||
"""
|
||||
# function reset_learning_no_wchange!(n::lifNeuron)
|
||||
# reset_epsilonRec!(n)
|
||||
# # reset_v_t!(n)
|
||||
# # reset_z_t!(n)
|
||||
# # reset_reg_voltage_a!(n)
|
||||
# # reset_reg_voltage_b!(n)
|
||||
# # reset_reg_voltage_error!(n)
|
||||
# reset_firing_counter!(n)
|
||||
# reset_firing_diff!(n)
|
||||
# reset_previous_error!(n)
|
||||
# reset_error!(n)
|
||||
|
||||
# # # reset refractory state at the end of episode. Otherwise once neuron goes into refractory state,
|
||||
# # # it will stay in refractory state forever
|
||||
# # reset_refractory_state_active!(n)
|
||||
# end
|
||||
# function reset_learning_no_wchange!(n::Union{alifNeuron, elif_neuron})
|
||||
# reset_epsilonRec!(n)
|
||||
# reset_epsilonRecA!(n)
|
||||
# reset_v_t!(n)
|
||||
# reset_z_t!(n)
|
||||
# # reset_a!(n)
|
||||
# reset_reg_voltage_a!(n)
|
||||
# reset_reg_voltage_b!(n)
|
||||
# reset_reg_voltage_error!(n)
|
||||
# reset_firing_counter!(n)
|
||||
# reset_firing_diff!(n)
|
||||
# reset_previous_error!(n)
|
||||
# reset_error!(n)
|
||||
|
||||
# # reset refractory state at the end of episode. Otherwise once neuron goes into refractory state,
|
||||
# # it will stay in refractory state forever
|
||||
# reset_refractory_state_active!(n)
|
||||
# end
|
||||
# function reset_learning_no_wchange!(n::linearNeuron)
|
||||
# reset_epsilon_j!(n)
|
||||
# reset_out_t!(n)
|
||||
# reset_error!(n)
|
||||
# end
|
||||
|
||||
""" Reset all learning-related params at the END of learning session
|
||||
"""
|
||||
function resetLearningParams!(n::lifNeuron)
|
||||
reset_epsilonRec!(n)
|
||||
reset_wRecChange!(n)
|
||||
# reset_v_t!(n)
|
||||
# reset_z_t!(n)
|
||||
reset_firing_counter!(n)
|
||||
reset_firing_diff!(n)
|
||||
|
||||
# reset refractory state at the start/end of episode. Otherwise once neuron goes into
|
||||
# refractory state, it will stay in refractory state forever
|
||||
# reset_refractoryCounter!(n)
|
||||
reset_z_i_t_commulative!(n)
|
||||
end
|
||||
function resetLearningParams!(n::alifNeuron)
|
||||
reset_epsilonRec!(n)
|
||||
reset_epsilonRecA!(n)
|
||||
reset_wRecChange!(n)
|
||||
# reset_v_t!(n)
|
||||
# reset_z_t!(n)
|
||||
# reset_a!(n)
|
||||
reset_firing_counter!(n)
|
||||
reset_firing_diff!(n)
|
||||
|
||||
# reset refractory state at the start/end of episode. Otherwise once neuron goes into
|
||||
# refractory state, it will stay in refractory state forever
|
||||
# reset_refractoryCounter!(n)
|
||||
reset_z_i_t_commulative!(n)
|
||||
end
|
||||
|
||||
# function reset_learning_no_wchange!(n::passthroughNeuron)
|
||||
# end
|
||||
|
||||
function resetLearningParams!(n::passthroughNeuron)
|
||||
# skip
|
||||
end
|
||||
|
||||
function resetLearningParams!(n::linearNeuron)
|
||||
reset_epsilonRec!(n)
|
||||
reset_wRecChange!(n)
|
||||
# reset_v_t!(n)
|
||||
reset_firing_counter!(n)
|
||||
|
||||
# reset refractory state at the start/end of episode. Otherwise once neuron goes into
|
||||
# refractory state, it will stay in refractory state forever
|
||||
# reset_refractoryCounter!(n)
|
||||
reset_z_i_t_commulative!(n)
|
||||
end
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
function store_knowledgefn_error!(kfn::knowledgeFn)
|
||||
# condition to adjust nueron in KFN plane in addition to weight adjustment inside each neuron
|
||||
if kfn.learningStage == "start_learning"
|
||||
if kfn.recent_knowledgeFn_error === nothing && kfn.knowledgeFn_error === nothing
|
||||
kfn.recent_knowledgeFn_error = [[]]
|
||||
elseif kfn.recent_knowledgeFn_error === nothing
|
||||
kfn.recent_knowledgeFn_error = [[kfn.knowledgeFn_error]]
|
||||
elseif kfn.recent_knowledgeFn_error !== nothing && kfn.knowledgeFn_error === nothing
|
||||
push!(kfn.recent_knowledgeFn_error, [])
|
||||
else
|
||||
push!(kfn.recent_knowledgeFn_error, [kfn.knowledgeFn_error])
|
||||
end
|
||||
elseif kfn.learningStage == "during_learning"
|
||||
if kfn.knowledgeFn_error === nothing
|
||||
#skip
|
||||
else
|
||||
push!(kfn.recent_knowledgeFn_error[end], kfn.knowledgeFn_error)
|
||||
end
|
||||
elseif kfn.learningStage == "end_learning"
|
||||
if kfn.recent_knowledgeFn_error === nothing
|
||||
#skip
|
||||
else
|
||||
push!(kfn.recent_knowledgeFn_error[end], kfn.knowledgeFn_error)
|
||||
end
|
||||
else
|
||||
error("case does not defined yet")
|
||||
end
|
||||
|
||||
if length(kfn.recent_knowledgeFn_error) > 3
|
||||
deleteat!(kfn.recent_knowledgeFn_error, 1)
|
||||
end
|
||||
end
|
||||
|
||||
function update_Bn!(kfn::knowledgeFn)
|
||||
Δw = nothing
|
||||
for n in kfn.outputNeuronsArray
|
||||
Δw = Δw === nothing ? n.w_out_change : Δw + n.w_out_change
|
||||
n.w_out = n.w_out - (n.Bn_wout_decay * n.w_out) # w_out decay
|
||||
end
|
||||
# Δw = Δw / kfn.kfnParams[:linear_neuron_number] # average
|
||||
|
||||
input_neuron_number = kfn.kfnParams[:input_neuron_number] # skip input neuron
|
||||
for i = 1:kfn.kfnParams[:compute_neuron_number]
|
||||
n = kfn.neuronsArray[input_neuron_number+i]
|
||||
n.Bn = n.Bn + Δw[i]
|
||||
n.Bn = n.Bn - (n.Bn_wout_decay * n.Bn) # w_out decay
|
||||
end
|
||||
end
|
||||
|
||||
""" Regulates membrane potential to stay under v_th, output is weight change
|
||||
"""
|
||||
function cal_v_reg!(n::lifNeuron)
|
||||
# retified linear function
|
||||
component_a1 = n.v_t1 - n.v_th < 0 ? 0 : (n.v_t1 - n.v_th)^2
|
||||
component_a2 = -n.v_t1 - n.v_th < 0 ? 0 : (-n.v_t1 - n.v_th)^2
|
||||
n.reg_voltage_a = n.reg_voltage_a + component_a1 + component_a2
|
||||
|
||||
component_b = n.v_t1 - n.v_th < 0 ? 0 : n.v_t1 - n.v_th
|
||||
#FIXME: not sure the following line is correct
|
||||
n.reg_voltage_b = n.reg_voltage_b + (component_b * n.epsilonRec)
|
||||
end
|
||||
|
||||
function cal_v_reg!(n::alifNeuron)
|
||||
# retified linear function
|
||||
component_a1 = n.v_t1 - n.av_th < 0 ? 0 : (n.v_t1 - n.av_th)^2
|
||||
component_a2 = -n.v_t1 - n.av_th < 0 ? 0 : (-n.v_t1 - n.av_th)^2
|
||||
n.reg_voltage_a = n.reg_voltage_a + component_a1 + component_a2
|
||||
|
||||
component_b = n.v_t1 - n.av_th < 0 ? 0 : n.v_t1 - n.av_th
|
||||
#FIXME: not sure the following line is correct
|
||||
n.reg_voltage_b = n.reg_voltage_b + (component_b * (n.epsilonRec - n.epsilonRecA))
|
||||
end
|
||||
|
||||
function voltage_error!(n::computeNeuron)
|
||||
n.reg_voltage_error = 0.5 * n.reg_voltage_a
|
||||
return n.reg_voltage_error
|
||||
end
|
||||
|
||||
function voltage_regulator!(n::computeNeuron) # running average
|
||||
Δw = n.optimiser.eta * n.c_reg_v * n.reg_voltage_b
|
||||
return Δw
|
||||
end
|
||||
|
||||
function firingRateError(kfn::knowledgeFn)
|
||||
start_id = kfn.kfnParams[:input_neuron_number] + 1
|
||||
return 0.5 * sum([(n.firingDiff)^2 for n in kfn.neuronsArray[start_id:end]])
|
||||
end
|
||||
|
||||
function firing_rate_regulator!(n::computeNeuron)
|
||||
# n.firingRate NOT running average (average over learning batch)
|
||||
Δw = n.optimiser.eta * n.c_reg *
|
||||
(n.firingRate - n.firingRateTarget) * n.eRec
|
||||
Δw = n.firingRate > n.firingRateTarget ? Δw : Δw * 0.0
|
||||
return Δw
|
||||
end
|
||||
|
||||
firing_rate!(n::computeNeuron) = n.firingRate = (n.firingCounter / n.timeStep) * 1000
|
||||
firing_diff!(n::computeNeuron) = n.firingDiff = n.firingRate - n.firingRateTarget
|
||||
|
||||
function adjust_internal_learning_rate!(n::computeNeuron)
|
||||
n.internal_learning_rate = n.error_diff[end] < 0.0 ? n.internal_learning_rate * 0.99 :
|
||||
n.internal_learning_rate * 1.005
|
||||
end
|
||||
|
||||
function connStrengthAdjust(currentStrength::Float64)
|
||||
Δstrength = (1.0 - sigmoid(currentStrength))
|
||||
return Δstrength::Float64
|
||||
end
|
||||
|
||||
""" Compute synaptic connection strength. bias will shift currentStrength to fit into
|
||||
sigmoid operating range which centred at 0 and range is -37 to 37.
|
||||
|
||||
# Example
|
||||
synaptic strength range is 0 to 10
|
||||
one may use bias = -5 to transform synaptic strength into range -5 to 5
|
||||
the return value is shifted back to original scale.
|
||||
|
||||
# Concept
|
||||
weaker connection should be harder to increase strength. It requires a lot of
|
||||
repeat activation to get it stronger. While strong connction requires a lot of
|
||||
inactivation to get it weaker. The concept is strong connection will lock
|
||||
correct neural pathway through repeated use of the right connection i.e. keep training
|
||||
on the correct answer -> strengthen the right neural pathway (connections) ->
|
||||
this correct neural pathway resist to change.
|
||||
Not used connection should dissapear (forgetting).
|
||||
"""
|
||||
function synapticConnStrength(currentStrength::Float64, updown::String)
|
||||
Δstrength = connStrengthAdjust(currentStrength)
|
||||
|
||||
if updown == "up"
|
||||
if currentStrength > 4 # strong connection
|
||||
updatedStrength = currentStrength + (Δstrength * 0.2)
|
||||
else
|
||||
updatedStrength = currentStrength + (Δstrength * 0.1)
|
||||
end
|
||||
elseif updown == "down"
|
||||
if currentStrength > 4
|
||||
updatedStrength = currentStrength - (Δstrength * 0.1)
|
||||
else
|
||||
updatedStrength = currentStrength - (Δstrength * 1.0)
|
||||
end
|
||||
else
|
||||
error("undefined condition line $(@__LINE__)")
|
||||
end
|
||||
return updatedStrength::Float64
|
||||
end
|
||||
# function synapticConnStrength(currentStrength::Float64, updown::String)
|
||||
# Δstrength = connStrengthAdjust(currentStrength)
|
||||
|
||||
# if updown == "up"
|
||||
# updatedStrength = currentStrength + Δstrength
|
||||
# else
|
||||
# updatedStrength = currentStrength - Δstrength
|
||||
# end
|
||||
# return updatedStrength::Float64
|
||||
# end
|
||||
|
||||
""" Compute all synaptic connection strength of a neuron. Also mark n.wRec to 0 if wRec goes
|
||||
below lowerlimit.
|
||||
"""
|
||||
# function synapticConnStrength!(n::Union{computeNeuron, outputNeuron})
|
||||
# for (i, connStrength) in enumerate(n.synapticStrength)
|
||||
# # check whether connStrength increase or decrease based on usage from n.epsilonRec
|
||||
# """ use n.z_i_t_commulative instead of the best choice, epsilonRec, here because ΔwRecChange
|
||||
# calculation in learn!() will reset epsilonRec to zeroes vector in case where
|
||||
# output neuron fires and trigger learn!() just before this synapticConnStrength
|
||||
# calculation.
|
||||
# Since n.z_i_t_commulative indicates whether a synaptic connection were used or not, it is
|
||||
# ok to use. n.z_i_t_commulative also span across a training sample without resetting.
|
||||
# """
|
||||
# updown = n.z_i_t_commulative[i] == 0 ? "down" : "up" #
|
||||
# updatedConnStrength = synapticConnStrength(connStrength, updown)
|
||||
# updatedConnStrength = GeneralUtils.limitvalue(updatedConnStrength,
|
||||
# n.synapticStrengthLimit.lowerlimit, n.synapticStrengthLimit.upperlimit)
|
||||
# # at lowerlimit, mark wRec at this position to 0. for new random synaptic conn
|
||||
# if updatedConnStrength == n.synapticStrengthLimit.lowerlimit[1]
|
||||
# n.wRec[i] = 0.0
|
||||
# end
|
||||
# n.synapticStrength[i] = updatedConnStrength
|
||||
# end
|
||||
# end
|
||||
|
||||
function synapticConnStrength!(n::Union{computeNeuron, outputNeuron}, correctAnswer::Bool)
|
||||
if correctAnswer == true
|
||||
for (i, connStrength) in enumerate(n.synapticStrength)
|
||||
# check whether connStrength increase or decrease based on usage from n.epsilonRec
|
||||
""" use n.z_i_t_commulative instead of the best choice, epsilonRec, here because ΔwRecChange
|
||||
calculation in learn!() will reset epsilonRec to zeroes vector in case where
|
||||
output neuron fires and trigger learn!() just before this synapticConnStrength
|
||||
calculation.
|
||||
Since n.z_i_t_commulative indicates whether a synaptic connection were used or not, it is
|
||||
ok to use. n.z_i_t_commulative also span across a training sample without resetting.
|
||||
"""
|
||||
updown = n.z_i_t_commulative[i] == 0 ? "down" : "up"
|
||||
updatedConnStrength = synapticConnStrength(connStrength, updown)
|
||||
updatedConnStrength = GeneralUtils.limitvalue(updatedConnStrength,
|
||||
n.synapticStrengthLimit.lowerlimit, n.synapticStrengthLimit.upperlimit)
|
||||
# at lowerlimit, mark wRec at this position to 0. for new random synaptic conn
|
||||
if updatedConnStrength == n.synapticStrengthLimit.lowerlimit[1]
|
||||
n.wRec[i] = 0.0
|
||||
end
|
||||
n.synapticStrength[i] = updatedConnStrength
|
||||
end
|
||||
else
|
||||
for (i, connStrength) in enumerate(n.synapticStrength)
|
||||
updatedConnStrength = synapticConnStrength(connStrength, "down")
|
||||
updatedConnStrength = GeneralUtils.limitvalue(updatedConnStrength,
|
||||
n.synapticStrengthLimit.lowerlimit, n.synapticStrengthLimit.upperlimit)
|
||||
# at lowerlimit, mark wRec at this position to 0. for new random synaptic conn
|
||||
if updatedConnStrength == n.synapticStrengthLimit.lowerlimit[1]
|
||||
n.wRec[i] = 0.0
|
||||
end
|
||||
n.synapticStrength[i] = updatedConnStrength
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function synapticConnStrength!(n::inputNeuron) end
|
||||
|
||||
""" normalize a part of a vector centering at a vector's maximum value along with nearby value
|
||||
within its radius. radius must be odd number.
|
||||
v1 will be normalized based on v2's peak
|
||||
"""
|
||||
function normalizePeak!(v1::Vector, v2::Vector, radius::Integer=2)
|
||||
peak = findall(isequal.(abs.(v2), maximum(abs.(v2))))[1]
|
||||
upindex = peak - radius
|
||||
upindex = upindex < 1 ? 1 : upindex
|
||||
downindex = peak + radius
|
||||
downindex = downindex > length(v1) ? length(v1) : downindex
|
||||
subvector = view(v1, upindex:downindex)
|
||||
normalize!(subvector, 1)
|
||||
end
|
||||
|
||||
""" rewire of neuron synaptic connection that has 0 weight. Without connection's excitatory and
|
||||
inhabitory ratio constraint.
|
||||
"""
|
||||
function neuroplasticity!(n::computeNeuron, firedNeurons::Vector,
|
||||
nExInTypeList::Vector)
|
||||
# if there is 0-weight then replace it with new connection
|
||||
zeroWeightConnIndex = findall(iszero.(n.wRec)) # connection that has 0 weight
|
||||
|
||||
# new synaptic connection must sample fron neuron that fires
|
||||
nFiredPool = filter(x -> x ∉ [n.id], firedNeurons) # exclude this neuron id from the id list
|
||||
filter!(x -> x ∉ n.subscriptionList, nFiredPool) # exclude this neuron's subscriptionList from the list
|
||||
|
||||
nNonFiredPool = setdiff!([1:length(nExInTypeList)...], nFiredPool)
|
||||
filter!(x -> x ∉ [n.id], nNonFiredPool) # exclude this neuron id from the id list
|
||||
filter!(x -> x ∉ n.subscriptionList, nNonFiredPool) # exclude this neuron's subscriptionList from the list
|
||||
|
||||
w = rand(0.01:0.01:0.2, length(zeroWeightConnIndex))
|
||||
synapticStrength = rand(-5:0.01:-4, length(zeroWeightConnIndex))
|
||||
|
||||
shuffle!(nFiredPool)
|
||||
shuffle!(nNonFiredPool)
|
||||
|
||||
# add new synaptic connection to neuron
|
||||
for (i, connIndex) in enumerate(zeroWeightConnIndex)
|
||||
if length(nFiredPool) != 0
|
||||
newConn = popfirst!(nFiredPool)
|
||||
else
|
||||
newConn = popfirst!(nNonFiredPool)
|
||||
end
|
||||
|
||||
""" conn that is being replaced has to go into nNonFiredPool so nNonFiredPool isn't empty
|
||||
"""
|
||||
push!(nNonFiredPool, n.subscriptionList[connIndex])
|
||||
n.subscriptionList[connIndex] = newConn
|
||||
n.wRec[connIndex] = w[i] * nExInTypeList[newConn]
|
||||
n.synapticStrength[connIndex] = synapticStrength[i]
|
||||
end
|
||||
end
|
||||
|
||||
function neuroplasticity!(n::outputNeuron, firedNeurons::Vector,
|
||||
nExInTypeList::Vector, totalInputNeuron::Integer)
|
||||
# if there is 0-weight then replace it with new connection
|
||||
zeroWeightConnIndex = findall(iszero.(n.wRec)) # connection that has 0 weight
|
||||
|
||||
# new synaptic connection must sample fron neuron that fires
|
||||
nFiredPool = filter(x -> x ∉ [n.id], firedNeurons) # exclude this neuron id from the id list
|
||||
filter!(x -> x ∉ n.subscriptionList, nFiredPool) # exclude this neuron's subscriptionList from the list
|
||||
filter!(x -> x ∉ [1:totalInputNeuron...], nFiredPool) # exclude input neuron
|
||||
|
||||
nNonFiredPool = setdiff!([1:length(nExInTypeList)...], nFiredPool)
|
||||
filter!(x -> x ∉ [n.id], nNonFiredPool) # exclude this neuron id from the id list
|
||||
filter!(x -> x ∉ n.subscriptionList, nNonFiredPool) # exclude this neuron's subscriptionList from the list
|
||||
filter!(x -> x ∉ [1:totalInputNeuron...], nNonFiredPool) # exclude input neuron
|
||||
|
||||
w = rand(0.01:0.01:0.2, length(zeroWeightConnIndex))
|
||||
synapticStrength = rand(-5:0.01:-4, length(zeroWeightConnIndex))
|
||||
|
||||
shuffle!(nFiredPool)
|
||||
shuffle!(nNonFiredPool)
|
||||
|
||||
# add new synaptic connection to neuron
|
||||
for (i, connIndex) in enumerate(zeroWeightConnIndex)
|
||||
newConn::Int64 = 0
|
||||
if length(nFiredPool) != 0
|
||||
newConn = popfirst!(nFiredPool)
|
||||
elseif length(nNonFiredPool) != 0
|
||||
newConn = popfirst!(nNonFiredPool)
|
||||
else
|
||||
# skip
|
||||
end
|
||||
|
||||
if newConn != 0
|
||||
""" conn that is being replaced has to go into nNonFiredPool so nNonFiredPool isn't empty
|
||||
"""
|
||||
push!(nNonFiredPool, n.subscriptionList[connIndex])
|
||||
n.subscriptionList[connIndex] = newConn
|
||||
n.wRec[connIndex] = w[i] * nExInTypeList[newConn]
|
||||
n.synapticStrength[connIndex] = synapticStrength[i]
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
""" Cap maximum weight of each neuron connection
|
||||
"""
|
||||
function capMaxWeight!(v::Vector{Float64}, max=1.0)
|
||||
originalSign = sign.(v)
|
||||
v = originalSign .* GeneralUtils.replaceMoreThan.(abs.(v), max)
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # end module
|
||||
780
oldVersion/0.0.2/src/types.jl
Normal file
780
oldVersion/0.0.2/src/types.jl
Normal file
@@ -0,0 +1,780 @@
|
||||
module types
|
||||
|
||||
export
|
||||
# struct
|
||||
IronpenStruct, model, knowledgeFn, lifNeuron, alifNeuron, linearNeuron,
|
||||
kfn_1, inputNeuron, computeNeuron, neuron, outputNeuron, passthroughNeuron,
|
||||
|
||||
# function
|
||||
instantiate_custom_types, init_neuron, populate_neuron,
|
||||
add_neuron!
|
||||
|
||||
using Random, LinearAlgebra
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
abstract type Ironpen end
|
||||
abstract type knowledgeFn <: Ironpen end
|
||||
abstract type neuron <: Ironpen end
|
||||
abstract type inputNeuron <: neuron end
|
||||
abstract type outputNeuron <: neuron end
|
||||
abstract type computeNeuron <: neuron end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" Model struct
|
||||
"""
|
||||
Base.@kwdef mutable struct model <: Ironpen
|
||||
knowledgeFn::Union{Dict,Nothing} = nothing
|
||||
modelParams::Union{Dict,Nothing} = nothing
|
||||
error::Float64 = 0.0
|
||||
outputError::Array{Float64} = Float64[]
|
||||
|
||||
""" "inference" = no learning params will be collected.
|
||||
"learning" = neuron will accumulate epsilon_j, compute Δw_rec_change each time
|
||||
correct answer is available then merge Δw_rec_change into wRecChange then
|
||||
reset epsilon_j.
|
||||
"reflect" = neuron will merge wRecChange into wRec then reset wRecChange. """
|
||||
learningStage::String = "inference"
|
||||
timeStep::Number = 0.0
|
||||
end
|
||||
""" Model outer constructor
|
||||
|
||||
# Example
|
||||
I_kfnparams = Dict(
|
||||
:type => "lifNeuron",
|
||||
:v_t1 => 0.0, # neuron membrane potential at time = t+1
|
||||
:v_th => 2.0, # neuron firing threshold (this value is treated as maximum bound if I use auto generate)
|
||||
:z_t => false, # neuron firing status at time = t
|
||||
:z_t1 => false, # neuron firing status at time = t+1
|
||||
:gammaPd => 0.3, # discount factor. The value is from the paper
|
||||
:phi => 0.0, # psuedo derivative
|
||||
:refractoryDuration => 2.0, # neuron refractory period in tick
|
||||
:delta => 1.0,
|
||||
:tau_m => 20.0, # membrane time constant in millisecond. The value is from the paper
|
||||
:eta => 0.01, # learning rate
|
||||
|
||||
I_kfn = Ironpen_ai_gpu.knowledgeFn(I_kfnparams, lif_neuron_params, alif_neuron_params,
|
||||
linear_neuron_params)
|
||||
|
||||
modelParams_1 = Dict(:knowledgeFn => Dict(:I => I_kfn,
|
||||
:run => run_kfn),
|
||||
:learningStage => "doing_inference",)
|
||||
|
||||
model_1 = Ironpen_ai_gpu.model(modelParams_1)
|
||||
"""
|
||||
function model(params::Dict)
|
||||
m = model()
|
||||
m.modelParams = params
|
||||
|
||||
fields = fieldnames(typeof(m))
|
||||
for i in fields
|
||||
if i in keys(params)
|
||||
m.:($i) = params[i] # assign params to n struct fields
|
||||
end
|
||||
end
|
||||
|
||||
return m
|
||||
end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" knowledgeFn struct
|
||||
"""
|
||||
Base.@kwdef mutable struct kfn_1 <: knowledgeFn
|
||||
knowledgeFnName::String = "not defined"
|
||||
kfnParams::Union{Dict,Nothing} = nothing # store params of knowledgeFn itself for later use
|
||||
timeStep::Number = 0.0
|
||||
|
||||
# Bn contain error coefficient for both neurons and output neurons in one place
|
||||
Bn::Vector{Float64} = Float64[] # error projection coefficient from kfn output's error to each neurons's error
|
||||
neuronsArray::Array{neuron} = neuron[] # put neurons here
|
||||
|
||||
""" put output neuron here. I seperate output neuron because
|
||||
1. its calculation is difference than other neuron types
|
||||
2. other neuron type will not induced to connnect to output neuron
|
||||
3. output neuron does not induced to connect to its own type """
|
||||
outputNeuronsArray::Array{outputNeuron} = outputNeuron[]
|
||||
|
||||
""" "inference" = no learning params will be collected.
|
||||
"learning" = neuron will accumulate epsilon_j, compute Δw_rec_change each time
|
||||
correct answer is available then merge Δw_rec_change into wRecChange then
|
||||
reset epsilon_j.
|
||||
"reflect" = neuron will merge wRecChange into wRec then reset wRecChange. """
|
||||
learningStage::String = "inference"
|
||||
|
||||
error::Float64 = 0.0
|
||||
|
||||
firedNeurons::Array{Int64} = Int64[] # store unique id of firing neurons to be used when random neuron connection
|
||||
firedNeurons_t0::Union{Vector{Bool},Nothing} = nothing # store firing state of all neurons at t0
|
||||
firedNeurons_t1::Union{Vector{Bool},Nothing} = nothing # store firing state of all neurons at t1
|
||||
|
||||
avgNeuronsFiringRate::Union{Float64,Nothing} = 0.0 # for displaying average firing rate over all neurons
|
||||
avgNeurons_v_t1::Union{Float64,Nothing} = 0.0 # for displaying average v_t1 over all neurons
|
||||
nExcitatory::Array{Int64} =Int64[] # list of excitatory neuron id
|
||||
nInhabitory::Array{Int64} = Int64[] # list of inhabitory neuron id
|
||||
nExInType::Array{Int64} = Int64[] # list all neuron EX or IN
|
||||
excitatoryPercent::Int64 = 60 # percentage of excitatory neuron, inhabitory percent will be 100-ExcitatoryPercent
|
||||
|
||||
exSignalSum = 0
|
||||
inSignalsum = 0
|
||||
end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" Knowledge function outer constructor >>> auto generate <<<
|
||||
|
||||
# Example
|
||||
|
||||
lif_neuron_params = Dict(
|
||||
:type => "lifNeuron",
|
||||
:v_th => 1.2, # neuron firing threshold (this value is treated as maximum bound if I use auto generate)
|
||||
:z_t => false, # neuron firing status at time = t
|
||||
:gammaPd => 0.3, # discount factor. The value is from the paper
|
||||
:refractoryDuration => 2.0, # neuron refractory period in tick
|
||||
:delta => 1.0,
|
||||
:tau_m => 5.0, # membrane time constant in millisecond. It should equals to time use for 1 sequence
|
||||
)
|
||||
|
||||
alif_neuron_params = Dict(
|
||||
:type => "alifNeuron",
|
||||
:v_th => 1.2, # neuron firing threshold (this value is treated as maximum bound if I use auto generate)
|
||||
:z_t => false, # neuron firing status at time = t
|
||||
:gammaPd => 0.3, # discount factor. The value is from the paper
|
||||
:refractoryDuration => 2.0, # neuron refractory period in millisecond
|
||||
:delta => 1.0,
|
||||
:tau_m => 5.0, # membrane time constant in millisecond. It should equals to time use for 1 sequence
|
||||
|
||||
# adaptation time constant in millisecond. It should equals to total time SNN takes to
|
||||
# perform a task i.e. equals to episode length
|
||||
:tau_a => 10.0,
|
||||
:beta => 0.15, # constant.
|
||||
:a => 0.0,
|
||||
)
|
||||
|
||||
linear_neuron_params = Dict(
|
||||
:type => "linearNeuron",
|
||||
:k => 0.9, # output leakink coefficient
|
||||
:tau_out => 5.0, # output time constant in millisecond. It should equals to time use for 1 sequence
|
||||
:out => 0.0, # neuron's output value store here
|
||||
)
|
||||
|
||||
I_kfnparams = Dict(
|
||||
:knowledgeFnName => "I",
|
||||
:lif_neuron_number => 200,
|
||||
:alif_neuron_number => 100, # from Allen Institute, ALIF is 40% of LIF
|
||||
:linear_neuron_number => 5, # output neuron, this is also the output length
|
||||
:Bn => "random", # error projection coefficient from kfn output's error to each neurons's error
|
||||
:learning_rate => 0.01,
|
||||
:neuron_connection_pattern => "100%", # number of each neuron subscribe to other neuron in knowledgeFn.neuronsArray
|
||||
:output_neuron_connection_pattern => "100%", # "60%" of kfn.neuronsArray or number
|
||||
:maximum_input_data_length => 5, # in case of GloVe word encoding, it is 300
|
||||
:neuron_w_in_generation_pattern => "random", # number or "random"
|
||||
:neuron_w_rec_generation_pattern => "random",
|
||||
:neuron_v_t_default => 0.5,
|
||||
:neuron_voltage_drop_percentage => "100%",
|
||||
:neuronFiringRateTarget => 50.0,
|
||||
:neuron_learning_rate => 0.01,
|
||||
:neuron_c_reg => 0.0001,
|
||||
:neuron_c_reg_v => 0.0001,
|
||||
:neuron_optimiser => "ADAM",
|
||||
:meta_params => Dict(:is_first_cycle => true,
|
||||
:launch_time => 0.0,))
|
||||
|
||||
kfn1 = knowledgeFn(kfnParams, lif_neuron_params, alif_neuron_params, linear_neuron_params)
|
||||
"""
|
||||
function kfn_1(kfnParams::Dict)
|
||||
|
||||
kfn = kfn_1()
|
||||
kfn.kfnParams = kfnParams
|
||||
kfn.knowledgeFnName = kfn.kfnParams[:knowledgeFnName]
|
||||
|
||||
if kfn.kfnParams[:computeNeuronNumber] < kfn.kfnParams[:totalInputPort]
|
||||
throw(error("number of compute neuron must be greater than input neuron"))
|
||||
end
|
||||
|
||||
# # Bn
|
||||
# if kfn.kfnParams[:Bn] == "random"
|
||||
# kfn.Bn = [Random.rand(0:0.001:1) for i in 1:kfn.kfnParams[:computeNeuronNumber]]
|
||||
# else # in case I want to specify manually
|
||||
# kfn.Bn = [kfn.kfnParams[:Bn] for i in 1:kfn.kfnParams[:computeNeuronNumber]]
|
||||
# end
|
||||
|
||||
# assign neurons ID by their position in kfn.neurons array because I think it is
|
||||
# straight forward way
|
||||
|
||||
# add input port, it must be added before any other neuron types
|
||||
for (k, v) in kfn.kfnParams[:inputPort]
|
||||
current_type = kfn.kfnParams[:inputPort][k]
|
||||
for i = 1:current_type[:numbers]
|
||||
n_id = length(kfn.neuronsArray) + 1
|
||||
neuron = init_neuron(n_id, current_type[:params], kfn.kfnParams)
|
||||
push!(kfn.neuronsArray, neuron)
|
||||
end
|
||||
end
|
||||
|
||||
# add compute neurons
|
||||
for (k, v) in kfn.kfnParams[:computeNeuron]
|
||||
current_type = kfn.kfnParams[:computeNeuron][k]
|
||||
for i = 1:current_type[:numbers]
|
||||
n_id = length(kfn.neuronsArray) + 1
|
||||
neuron = init_neuron(n_id, current_type[:params], kfn.kfnParams)
|
||||
push!(kfn.neuronsArray, neuron)
|
||||
end
|
||||
end
|
||||
|
||||
for i = 1:kfn.kfnParams[:outputPort][:numbers]
|
||||
neuron = init_neuron(i, kfn.kfnParams[:outputPort][:params],
|
||||
kfn.kfnParams)
|
||||
push!(kfn.outputNeuronsArray, neuron)
|
||||
end
|
||||
|
||||
for n in kfn.neuronsArray
|
||||
if typeof(n) <: computeNeuron
|
||||
n.firingRateTarget = kfn.kfnParams[:neuronFiringRateTarget]
|
||||
end
|
||||
end
|
||||
|
||||
# excitatory neuron to inhabitory neuron = 60:40 % of computeNeuron
|
||||
ex_number = Int(floor((kfn.excitatoryPercent/100.0) * kfn.kfnParams[:computeNeuronNumber]))
|
||||
ex_n = [1 for i in 1:ex_number]
|
||||
in_number = kfn.kfnParams[:computeNeuronNumber] - ex_number
|
||||
in_n = [-1 for i in 1:in_number]
|
||||
ex_in = shuffle!([ex_n; in_n])
|
||||
|
||||
# input neurons are always excitatory, compute_neurons are random between excitatory
|
||||
# and inhabitory
|
||||
for n in kfn.neuronsArray
|
||||
try n.ExInType = pop!(ex_in) catch end
|
||||
end
|
||||
|
||||
# add ExInType into each computeNeuron subExInType
|
||||
for n in kfn.neuronsArray
|
||||
try # input neuron doest have n.subscriptionList
|
||||
for (i, sub_id) in enumerate(n.subscriptionList)
|
||||
n_ExInType = kfn.neuronsArray[sub_id].ExInType
|
||||
n.wRec[i] *= n_ExInType
|
||||
# add id exin type to kfn
|
||||
if n_ExInType < 0
|
||||
push!(kfn.nInhabitory, sub_id)
|
||||
else
|
||||
push!(kfn.nExcitatory, sub_id)
|
||||
end
|
||||
end
|
||||
catch
|
||||
end
|
||||
end
|
||||
|
||||
# add ExInType into each output neuron subExInType
|
||||
for n in kfn.outputNeuronsArray
|
||||
try # input neuron doest have n.subscriptionList
|
||||
for (i, sub_id) in enumerate(n.subscriptionList)
|
||||
n_ExInType = kfn.neuronsArray[sub_id].ExInType
|
||||
n.wRec[i] *= n_ExInType
|
||||
end
|
||||
catch
|
||||
end
|
||||
end
|
||||
|
||||
for n in kfn.neuronsArray
|
||||
push!(kfn.nExInType, n.ExInType)
|
||||
end
|
||||
|
||||
return kfn
|
||||
end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" passthroughNeuron struct
|
||||
"""
|
||||
Base.@kwdef mutable struct passthroughNeuron <: inputNeuron
|
||||
id::Int64 = 0 # ID of this neuron which is it position in knowledgeFn array
|
||||
type::String = "passthroughNeuron"
|
||||
knowledgeFnName::String = "not defined" # knowledgeFn that this neuron belongs to
|
||||
z_t::Bool = false
|
||||
z_t1::Bool = false
|
||||
timeStep::Int64 = 0 # current time
|
||||
ExInType::Int64 = 1 # 1 excitatory, -1 inhabitory. input neuron is always excitatory
|
||||
end
|
||||
|
||||
function passthroughNeuron(params::Dict)
|
||||
n = passthroughNeuron()
|
||||
field_names = fieldnames(typeof(n))
|
||||
for i in field_names
|
||||
if i in keys(params)
|
||||
if i == :optimiser
|
||||
opt_type = string(split(params[i], ".")[end])
|
||||
n.:($i) = load_optimiser(opt_type)
|
||||
else
|
||||
n.:($i) = params[i] # assign params to n struct fields
|
||||
end
|
||||
end
|
||||
end
|
||||
return n
|
||||
end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" lifNeuron struct
|
||||
"""
|
||||
Base.@kwdef mutable struct lifNeuron <: computeNeuron
|
||||
id::Int64 = 0 # this neuron ID i.e. position of this neuron in knowledgeFn
|
||||
type::String = "lifNeuron"
|
||||
ExInType::Int64 = 1 # 1 excitatory, -1 inhabitory
|
||||
knowledgeFnName::String = "not defined" # knowledgeFn that this neuron belongs to
|
||||
subscriptionList::Array{Int64} = Int64[] # list of other neuron that this neuron synapse subscribed to
|
||||
timeStep::Int64 = 0 # current time
|
||||
wRec::Array{Float64} = Float64[] # synaptic weight (for receiving signal from other neuron)
|
||||
v_t::Float64 = 0.0 # vᵗ, postsynaptic neuron membrane potential of previous timestep
|
||||
v_t1::Float64 = rand() # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep
|
||||
v_th::Float64 = 1.0 # vᵗʰ, neuron firing threshold
|
||||
vRest::Float64 = 0.0 # resting potential after neuron fired
|
||||
z_t::Bool = false # zᵗ, neuron postsynaptic firing of previous timestep
|
||||
# zᵗ⁺¹, neuron firing status at time = t+1. I need this because the way I calculate all
|
||||
# neurons forward function at each timestep-by-timestep is to do every neuron
|
||||
# forward calculation. Each neuron requires access to other neuron's firing status
|
||||
# during v_t1 calculation hence I need a variable to hold z_t1 so that I'm not replacing z_t
|
||||
z_t1::Bool = false # neuron postsynaptic firing at current timestep (after neuron's calculation)
|
||||
z_i_t::Array{Bool} = Bool[] # neuron presynaptic firing at current timestep (which is other neuron postsynaptic firing of previous timestep)
|
||||
z_i_t_commulative::Array{Int64} = Int64[] # used to compute connection strength
|
||||
synapticStrength::Array{Float64} = Float64[]
|
||||
synapticStrengthLimit::NamedTuple = (lowerlimit=(-5=>-5), upperlimit=(5=>5))
|
||||
|
||||
gammaPd::Float64 = 0.3 # γ_pd, discount factor, value from paper
|
||||
alpha::Float64 = 0.0 # α, neuron membrane potential decay factor
|
||||
phi::Float64 = 0.0 # ϕ, psuedo derivative
|
||||
epsilonRec::Array{Float64} = Float64[] # ϵ_rec, eligibility vector for neuron spike
|
||||
decayedEpsilonRec::Array{Float64} = Float64[] # α * epsilonRec
|
||||
eRec::Array{Float64} = Float64[] # eligibility trace for neuron spike
|
||||
delta::Float64 = 1.0 # δ, discreate timestep size in millisecond
|
||||
refractoryDuration::Int64 = 3 # neuron's refratory period in millisecond
|
||||
refractoryCounter::Int64 = 0
|
||||
tau_m::Float64 = 0.0 # τ_m, membrane time constant in millisecond
|
||||
eta::Float64 = 0.01 # η, learning rate
|
||||
wRecChange::Array{Float64} = Float64[] # Δw_rec, cumulated wRec change
|
||||
recSignal::Float64 = 0.0 # incoming recurrent signal
|
||||
alpha_v_t::Float64 = 0.0 # alpha * v_t
|
||||
error::Float64 = 0.0 # local neuron error
|
||||
# optimiser::Union{Any,Nothing} = load_optimiser("AdaBelief") # Flux optimizer
|
||||
|
||||
firingCounter::Int64 = 0 # store how many times neuron fires
|
||||
firingRateTarget::Float64 = 20.0 # neuron's target firing rate in Hz
|
||||
firingDiff::Float64 = 0.0 # e-prop supplement paper equation 5
|
||||
firingRateError::Float64 = 0.0 # local neuron error w.r.t. firing regularization
|
||||
firingRate::Float64 = 0.0 # running average of firing rate in Hz
|
||||
|
||||
""" "inference" = no learning params will be collected.
|
||||
"learning" = neuron will accumulate epsilon_j, compute Δw_rec_change each time
|
||||
correct answer is available then merge Δw_rec_change into wRecChange then
|
||||
reset epsilon_j.
|
||||
"reflect" = neuron will merge wRecChange into wRec then reset wRecChange. """
|
||||
learningStage::String = "inference"
|
||||
end
|
||||
|
||||
""" lif neuron outer constructor
|
||||
|
||||
# Example
|
||||
|
||||
lif_neuron_params = Dict(
|
||||
:type => "lifNeuron",
|
||||
:v_th => 1.2, # neuron firing threshold (this value is treated as maximum bound if I use auto generate)
|
||||
:z_t => false, # neuron firing status at time = t
|
||||
:gammaPd => 0.3, # discount factor. The value is from the paper
|
||||
:refractoryDuration => 2.0, # neuron refractory period in tick
|
||||
:delta => 1.0,
|
||||
:tau_m => 5.0, # membrane time constant in millisecond. It should equals to time use for 1 sequence
|
||||
)
|
||||
|
||||
neuron1 = lifNeuron(lif_neuron_params)
|
||||
"""
|
||||
function lifNeuron(params::Dict)
|
||||
n = lifNeuron()
|
||||
field_names = fieldnames(typeof(n))
|
||||
for i in field_names
|
||||
if i in keys(params)
|
||||
if i == :optimiser
|
||||
opt_type = string(split(params[i], ".")[end])
|
||||
n.:($i) = load_optimiser(opt_type)
|
||||
else
|
||||
n.:($i) = params[i] # assign params to n struct fields
|
||||
end
|
||||
end
|
||||
end
|
||||
return n
|
||||
end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
""" alifNeuron struct
|
||||
"""
|
||||
Base.@kwdef mutable struct alifNeuron <: computeNeuron
|
||||
id::Int64 = 0 # this neuron ID i.e. position of this neuron in knowledgeFn
|
||||
type::String = "alifNeuron"
|
||||
ExInType::Int64 = -1 # 1 excitatory, -1 inhabitory
|
||||
knowledgeFnName::String = "not defined" # knowledgeFn that this neuron belongs to
|
||||
subscriptionList::Array{Int64} = Int64[] # list of other neuron that this neuron synapse subscribed to
|
||||
timeStep::Int64 = 0 # current time
|
||||
wRec::Array{Float64} = Float64[] # synaptic weight (for receiving signal from other neuron)
|
||||
v_t::Float64 = 0.0 # vᵗ, postsynaptic neuron membrane potential of previous timestep
|
||||
v_t1::Float64 = rand() # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep
|
||||
v_th::Float64 = 1.0 # vᵗʰ, neuron firing threshold
|
||||
vRest::Float64 = 0.0 # resting potential after neuron fired
|
||||
z_t::Bool = false # zᵗ, neuron postsynaptic firing of previous timestep
|
||||
# zᵗ⁺¹, neuron firing status at time = t+1. I need this because the way I calculate all
|
||||
# neurons forward function at each timestep-by-timestep is to do every neuron
|
||||
# forward calculation. Each neuron requires access to other neuron's firing status
|
||||
# during v_t1 calculation hence I need a variable to hold z_t1 so that I'm not replacing z_t
|
||||
z_t1::Bool = false # neuron postsynaptic firing at current timestep (after neuron's calculation)
|
||||
z_i_t::Array{Bool} = Bool[] # neuron presynaptic firing at current timestep (which is other neuron postsynaptic firing of previous timestep)
|
||||
z_i_t_commulative::Array{Int64} = Int64[] # used to compute connection strength
|
||||
synapticStrength::Array{Float64} = Float64[]
|
||||
synapticStrengthLimit::NamedTuple = (lowerlimit=(-5=>0), upperlimit=(5=>5))
|
||||
|
||||
alpha::Float64 = 0.0 # α, neuron membrane potential decay factor
|
||||
delta::Float64 = 1.0 # δ, discreate timestep size in millisecond
|
||||
epsilonRec::Array{Float64} = Float64[] # ϵ_rec(v), eligibility vector for neuron i spike
|
||||
epsilonRecA::Array{Float64} = Float64[] # ϵ_rec(a)
|
||||
decayedEpsilonRec::Array{Float64} = Float64[] # α * epsilonRec
|
||||
eRec_v::Array{Float64} = Float64[] # a component of neuron's eligibility trace resulted from v_t
|
||||
eRec_a::Array{Float64} = Float64[] # a component of neuron's eligibility trace resulted from av_th
|
||||
eRec::Array{Float64} = Float64[] # neuron's eligibility trace
|
||||
eta::Float64 = 0.01 # eta, learning rate
|
||||
gammaPd::Float64 = 0.3 # γ_pd, discount factor, value from paper
|
||||
phi::Float64 = 0.0 # ϕ, psuedo derivative
|
||||
refractoryDuration::Int64 = 3 # neuron's refractory period in millisecond
|
||||
refractoryCounter::Int64 = 0
|
||||
tau_m::Float64 = 0.0 # τ_m, membrane time constant in millisecond
|
||||
wRecChange::Array{Float64} = Float64[] # Δw_rec, cumulated wRec change
|
||||
recSignal::Float64 = 0.0 # incoming recurrent signal
|
||||
alpha_v_t::Float64 = 0.0 # alpha * v_t
|
||||
error::Float64 = 0.0 # local neuron error
|
||||
# optimiser::Union{Any,Nothing} = load_optimiser("AdaBelief") # Flux optimizer
|
||||
|
||||
firingCounter::Int64 = 0 # store how many times neuron fires
|
||||
firingRateTarget::Float64 = 20.0 # neuron's target firing rate in Hz
|
||||
firingDiff::Float64 = 0.0 # e-prop supplement paper equation 5
|
||||
firingRateError::Float64 = 0.0 # local neuron error w.r.t. firing regularization
|
||||
firingRate::Float64 = 0.0 # running average of firing rate, Hz
|
||||
|
||||
tau_a::Float64 = 0.0 # τ_a, adaption time constant in millisecond
|
||||
beta::Float64 = 0.15 # β, constant, value from paper
|
||||
rho::Float64 = 0.0 # ρ, threshold adaptation decay factor
|
||||
a::Float64 = 0.0 # threshold adaptation
|
||||
av_th::Float64 = 0.0 # adjusted neuron firing threshold
|
||||
|
||||
""" "inference" = no learning params will be collected.
|
||||
"learning" = neuron will accumulate epsilon_j, compute Δw_rec_change each time
|
||||
correct answer is available then merge Δw_rec_change into wRecChange then
|
||||
reset epsilon_j.
|
||||
"reflect" = neuron will merge wRecChange into wRec then reset wRecChange. """
|
||||
learningStage::String = "inference"
|
||||
end
|
||||
""" alif neuron outer constructor
|
||||
|
||||
# Example
|
||||
|
||||
alif_neuron_params = Dict(
|
||||
:type => "alifNeuron",
|
||||
:v_th => 1.2, # neuron firing threshold (this value is treated as maximum bound if I
|
||||
use auto generate)
|
||||
:z_t => false, # neuron firing status at time = t
|
||||
:gammaPd => 0.3, # discount factor. The value is from the paper
|
||||
:refractoryDuration => 2.0, # neuron refractory period in millisecond
|
||||
:delta => 1.0,
|
||||
:tau_m => 5.0, # membrane time constant in millisecond. It should equals to time use
|
||||
for 1 sequence
|
||||
|
||||
# adaptation time constant in millisecond. It should equals to total time SNN takes to
|
||||
# perform a task i.e. equals to episode length
|
||||
:tau_a => 10.0,
|
||||
:beta => 0.15, # constant.
|
||||
:a => 0.0,
|
||||
)
|
||||
|
||||
neuron1 = alifNeuron(alif_neuron_params)
|
||||
"""
|
||||
function alifNeuron(params::Dict)
|
||||
n = alifNeuron()
|
||||
field_names = fieldnames(typeof(n))
|
||||
for i in field_names
|
||||
if i in keys(params)
|
||||
if i == :optimiser
|
||||
opt_type = string(split(params[i], ".")[end])
|
||||
n.:($i) = load_optimiser(opt_type)
|
||||
else
|
||||
n.:($i) = params[i] # assign params to n struct fields
|
||||
end
|
||||
end
|
||||
end
|
||||
return n
|
||||
end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
""" linearNeuron struct
|
||||
"""
|
||||
Base.@kwdef mutable struct linearNeuron <: outputNeuron
|
||||
id::Float64 = 0.0 # ID of this neuron which is it position in knowledgeFn array
|
||||
type::String = "linearNeuron"
|
||||
knowledgeFnName::String = "not defined" # knowledgeFn that this neuron belongs to
|
||||
subscriptionList::Array{Int64} = Int64[] # list of other neuron that this neuron synapse subscribed to
|
||||
timeStep::Int64 = 0 # current time
|
||||
wRec::Array{Float64} = Float64[] # synaptic weight (for receiving signal from other neuron)
|
||||
v_t::Float64 = 0.0 # vᵗ, postsynaptic neuron membrane potential of previous timestep
|
||||
v_t1::Float64 = rand() # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep
|
||||
v_th::Float64 = 1.0 # vᵗʰ, neuron firing threshold
|
||||
vRest::Float64 = 0.0 # resting potential after neuron fired
|
||||
vError::Float64 = 0.0 # used to compute model error
|
||||
z_t::Bool = false # zᵗ, neuron postsynaptic firing of previous timestep
|
||||
# zᵗ⁺¹, neuron firing status at time = t+1. I need this because the way I calculate all
|
||||
# neurons forward function at each timestep-by-timestep is to do every neuron
|
||||
# forward calculation. Each neuron requires access to other neuron's firing status
|
||||
# during v_t1 calculation hence I need a variable to hold z_t1 so that I'm not replacing z_t
|
||||
z_t1::Bool = false # neuron postsynaptic firing at current timestep (after neuron's calculation)
|
||||
|
||||
# neuron presynaptic firing at current timestep (which is other neuron postsynaptic firing of
|
||||
# previous timestep)
|
||||
z_i_t::Array{Bool} = Bool[]
|
||||
z_i_t_commulative::Array{Int64} = Int64[] # used to compute connection strength
|
||||
synapticStrength::Array{Float64} = Float64[]
|
||||
synapticStrengthLimit::NamedTuple = (lowerlimit=(-5=>-5), upperlimit=(5=>5))
|
||||
|
||||
gammaPd::Float64 = 0.3 # γ_pd, discount factor, value from paper
|
||||
alpha::Float64 = 0.0 # α, neuron membrane potential decay factor
|
||||
phi::Float64 = 0.0 # ϕ, psuedo derivative
|
||||
epsilonRec::Array{Float64} = Float64[] # ϵ_rec, eligibility vector for neuron spike
|
||||
decayedEpsilonRec::Array{Float64} = Float64[] # α * epsilonRec
|
||||
eRec::Array{Float64} = Float64[] # eligibility trace for neuron spike
|
||||
delta::Float64 = 1.0 # δ, discreate timestep size in millisecond
|
||||
refractoryDuration::Int64 = 3 # neuron's refratory period in millisecond
|
||||
refractoryCounter::Int64 = 0
|
||||
tau_out::Float64 = 0.0 # τ_out, membrane time constant in millisecond
|
||||
eta::Float64 = 0.01 # η, learning rate
|
||||
wRecChange::Array{Float64} = Float64[] # Δw_rec, cumulated wRec change
|
||||
recSignal::Float64 = 0.0 # incoming recurrent signal
|
||||
alpha_v_t::Float64 = 0.0 # alpha * v_t
|
||||
|
||||
firingCounter::Int64 = 0 # store how many times neuron fires
|
||||
end
|
||||
|
||||
""" linear neuron outer constructor
|
||||
|
||||
# Example
|
||||
|
||||
linear_neuron_params = Dict(
|
||||
:type => "linearNeuron",
|
||||
:k => 0.9, # output leakink coefficient
|
||||
:tau_out => 5.0, # output time constant in millisecond. It should equals to time use for 1 sequence
|
||||
:out => 0.0, # neuron's output value store here
|
||||
)
|
||||
|
||||
neuron1 = linearNeuron(linear_neuron_params)
|
||||
"""
|
||||
function linearNeuron(params::Dict)
|
||||
n = linearNeuron()
|
||||
field_names = fieldnames(typeof(n))
|
||||
for i in field_names
|
||||
if i in keys(params)
|
||||
if i == :optimiser
|
||||
opt_type = string(split(params[i], ".")[end])
|
||||
n.:($i) = load_optimiser(opt_type)
|
||||
else
|
||||
n.:($i) = params[i] # assign params to n struct fields
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
return n
|
||||
end
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
# function load_optimiser(optimiser_name::String; params::Union{Dict,Nothing} = nothing)
|
||||
# if optimiser_name == "AdaBelief"
|
||||
# params = (0.01, (0.9, 0.8))
|
||||
# return Flux.Optimise.AdaBelief(params...)
|
||||
# elseif optimiser_name == "AdaBelief2"
|
||||
# # output neuron requires slower change pace so η is lower than compute neuron at 0.007
|
||||
# # because if w_out change too fast, compute neuron will not able to
|
||||
# # grapse output neuron moving direction i.e. both compute neuron's direction and
|
||||
# # output neuron direction are out of sync.
|
||||
# params = (0.007, (0.9, 0.8))
|
||||
# return Flux.Optimise.AdaBelief(params...)
|
||||
# else
|
||||
# error("optimiser is not defined yet in load_optimiser()")
|
||||
# end
|
||||
# end
|
||||
|
||||
function init_neuron!(id::Int64, n::passthroughNeuron, n_params::Dict, kfnParams::Dict)
|
||||
n.id = id
|
||||
n.knowledgeFnName = kfnParams[:knowledgeFnName]
|
||||
end
|
||||
|
||||
# function init_neuron!(id::Int64, n::lifNeuron, kfnParams::Dict)
|
||||
# n.id = id
|
||||
# n.knowledgeFnName = kfnParams[:knowledgeFnName]
|
||||
# subscription_options = shuffle!([1:(kfnParams[:input_neuron_number]+kfnParams[:computeNeuronNumber])...])
|
||||
# if typeof(kfnParams[:synapticConnectionPercent]) == String
|
||||
# percent = parse(Int, kfnParams[:synapticConnectionPercent][1:end-1]) / 100
|
||||
# synapticConnectionPercent = floor(length(subscription_options) * percent)
|
||||
# n.subscriptionList = [pop!(subscription_options) for i = 1:synapticConnectionPercent]
|
||||
# end
|
||||
# filter!(x -> x != n.id, n.subscriptionList)
|
||||
# n.epsilonRec = zeros(length(n.subscriptionList))
|
||||
# n.wRec = Random.rand(length(n.subscriptionList))
|
||||
# n.wRecChange = zeros(length(n.subscriptionList))
|
||||
# n.reg_voltage_b = zeros(length(n.subscriptionList))
|
||||
# n.alpha = calculate_α(n)
|
||||
# end
|
||||
|
||||
function init_neuron!(id::Int64, n::lifNeuron, n_params::Dict, kfnParams::Dict)
|
||||
n.id = id
|
||||
n.knowledgeFnName = kfnParams[:knowledgeFnName]
|
||||
subscription_options = shuffle!([1:kfnParams[:totalNeurons]...])
|
||||
subscription_numbers = Int(floor((n_params[:synapticConnectionPercent] / 100.0) *
|
||||
kfnParams[:totalNeurons]))
|
||||
n.subscriptionList = [pop!(subscription_options) for i = 1:subscription_numbers]
|
||||
|
||||
# prevent subscription to itself by removing this neuron id
|
||||
filter!(x -> x != n.id, n.subscriptionList)
|
||||
n.synapticStrength = rand(-5:0.01:-4, length(n.subscriptionList))
|
||||
|
||||
n.epsilonRec = zeros(length(n.subscriptionList))
|
||||
n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
|
||||
n.wRecChange = zeros(length(n.subscriptionList))
|
||||
n.alpha = calculate_α(n)
|
||||
n.z_i_t_commulative = zeros(length(n.subscriptionList))
|
||||
end
|
||||
|
||||
function init_neuron!(id::Int64, n::alifNeuron, n_params::Dict,
|
||||
kfnParams::Dict)
|
||||
n.id = id
|
||||
n.knowledgeFnName = kfnParams[:knowledgeFnName]
|
||||
subscription_options = shuffle!([1:kfnParams[:totalNeurons]...])
|
||||
subscription_numbers = Int(floor((n_params[:synapticConnectionPercent] / 100.0) *
|
||||
kfnParams[:totalNeurons]))
|
||||
n.subscriptionList = [pop!(subscription_options) for i = 1:subscription_numbers]
|
||||
|
||||
# prevent subscription to itself by removing this neuron id
|
||||
filter!(x -> x != n.id, n.subscriptionList)
|
||||
n.synapticStrength = rand(-5:0.01:-4, length(n.subscriptionList))
|
||||
|
||||
n.epsilonRec = zeros(length(n.subscriptionList))
|
||||
n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
|
||||
n.wRecChange = zeros(length(n.subscriptionList))
|
||||
|
||||
# the more time has passed from the last time neuron was activated, the more
|
||||
# neuron membrane potential is reduced
|
||||
n.alpha = calculate_α(n)
|
||||
n.rho = calculate_ρ(n)
|
||||
n.epsilonRecA = zeros(length(n.subscriptionList))
|
||||
n.z_i_t_commulative = zeros(length(n.subscriptionList))
|
||||
end
|
||||
|
||||
|
||||
function init_neuron!(id::Int64, n::linearNeuron, n_params::Dict, kfnParams::Dict)
|
||||
n.id = id
|
||||
n.knowledgeFnName = kfnParams[:knowledgeFnName]
|
||||
|
||||
subscription_options = shuffle!([kfnParams[:totalInputPort]+1 : kfnParams[:totalNeurons]...])
|
||||
subscription_numbers = Int(floor((n_params[:synapticConnectionPercent] / 100.0) *
|
||||
kfnParams[:totalNeurons] - kfnParams[:totalInputPort]))
|
||||
n.subscriptionList = [pop!(subscription_options) for i = 1:subscription_numbers]
|
||||
n.synapticStrength = rand(-5:0.01:-4, length(n.subscriptionList))
|
||||
|
||||
n.epsilonRec = zeros(length(n.subscriptionList))
|
||||
n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
|
||||
n.wRecChange = zeros(length(n.subscriptionList))
|
||||
n.alpha = calculate_k(n)
|
||||
n.z_i_t_commulative = zeros(length(n.subscriptionList))
|
||||
end
|
||||
|
||||
""" Make a neuron intended for use with knowledgeFn
|
||||
"""
|
||||
function init_neuron(id::Int64, n_params::Dict, kfnParams::Dict)
|
||||
n = instantiate_custom_types(n_params)
|
||||
init_neuron!(id, n, n_params, kfnParams)
|
||||
|
||||
return n
|
||||
end
|
||||
|
||||
""" This function instantiate Ironpen type.
|
||||
|
||||
# Example
|
||||
|
||||
new_model = instantiate_custom_types("model")
|
||||
"""
|
||||
function instantiate_custom_types(params::Union{Dict,Nothing} = nothing)
|
||||
type = string(split(params[:type], ".")[end])
|
||||
|
||||
if type == "model"
|
||||
return model()
|
||||
elseif type == "knowledgeFn"
|
||||
return knowledgeFn()
|
||||
elseif type == "passthroughNeuron"
|
||||
return passthroughNeuron(params)
|
||||
elseif type == "lifNeuron"
|
||||
return lifNeuron(params)
|
||||
elseif type == "alifNeuron"
|
||||
return alifNeuron(params)
|
||||
elseif type == "linearNeuron"
|
||||
return linearNeuron(params)
|
||||
else
|
||||
return nothing
|
||||
end
|
||||
end
|
||||
|
||||
""" Add a new neuron into a knowledgeFn
|
||||
|
||||
# Example
|
||||
add_neuron!(kfn.kfnParams[:lif_neuron_params], kfn)
|
||||
"""
|
||||
# function add_neuron!(neuron_Dict::Dict, kfn::knowledgeFn)
|
||||
# id = length(kfn.neuronsArray) + 1
|
||||
# neuron = init_neuron(id, neuron_Dict, kfn.kfnParams,
|
||||
# totalNeurons = (length(kfn.neuronsArray) + 1))
|
||||
# push!(kfn.neuronsArray, neuron)
|
||||
|
||||
# # Randomly select an output neuron to add a new neuron to
|
||||
# add_n_output_n!(Random.rand(kfn.outputNeuronsArray), id)
|
||||
# end
|
||||
|
||||
calculate_α(neuron::lifNeuron) = exp(-neuron.delta / neuron.tau_m)
|
||||
calculate_α(neuron::alifNeuron) = exp(-neuron.delta / neuron.tau_m)
|
||||
calculate_ρ(neuron::alifNeuron) = exp(-neuron.delta / neuron.tau_a)
|
||||
calculate_k(neuron::linearNeuron) = exp(-neuron.delta / neuron.tau_out)
|
||||
|
||||
#------------------------------------------------------------------------------------------------100
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
end # module end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
0
oldVersion/0.0.2/test/etc2.jl
Normal file
0
oldVersion/0.0.2/test/etc2.jl
Normal file
15
oldVersion/0.0.2/test/etc3.jl
Normal file
15
oldVersion/0.0.2/test/etc3.jl
Normal file
@@ -0,0 +1,15 @@
|
||||
src_folder = "C:\\myWork\\my_projects\\AI\\NLP\\my_NLP\\Ironpen_ai\\src"
|
||||
|
||||
include("$src_folder/Utils.jl")
|
||||
using .Utils
|
||||
|
||||
|
||||
pub = "ch1"
|
||||
sub = "ch2"
|
||||
|
||||
function p(x)
|
||||
println("function called")
|
||||
return x + 1
|
||||
end
|
||||
|
||||
service_server("192.168.0.10", pub, sub, "testserver", p)
|
||||
27
oldVersion/0.0.2/test/test_data_prep_for_db.jl
Normal file
27
oldVersion/0.0.2/test/test_data_prep_for_db.jl
Normal file
@@ -0,0 +1,27 @@
|
||||
using Revise
|
||||
using Ironpen_ai
|
||||
using DataStructures
|
||||
using JSON3
|
||||
using Redis
|
||||
|
||||
|
||||
|
||||
# file_location = "C:\\myWork\\my_projects\\AI\\NLP\\my_NLP\\Ironpen_ai\\"
|
||||
# filename = "tonModel_2.json"
|
||||
# jsonString = read(file_location * filename, String)
|
||||
# jsonObject = JSON3.read(jsonString)
|
||||
# model_data = OrderedDict(jsonObject)
|
||||
|
||||
# Ironpen_ai.data_prep_for_db(1, 1, 1, model_data)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user