From 7c4a0dfa6fc977c72e3cda311c9edb4f194856c9 Mon Sep 17 00:00:00 2001 From: tonaerospace Date: Wed, 10 May 2023 20:38:23 +0700 Subject: [PATCH] refractoring --- .vscode/settings.json | 1 + Manifest.toml | 913 ++++++++++++++++++++++++++++++++++ Project.toml | 13 + src/DB_services.jl | 153 ++++++ src/Ironpen.jl | 89 ++++ src/WPembeddings.jl | 200 ++++++++ src/forward.jl | 236 +++++++++ src/interface.jl | 79 +++ src/learn.jl | 304 +++++++++++ src/readout.jl | 83 ++++ src/snn_utils.jl | 319 ++++++++++++ src/types.jl | 763 ++++++++++++++++++++++++++++ test/etc2.jl | 0 test/etc3.jl | 15 + test/test_data_prep_for_db.jl | 27 + 15 files changed, 3195 insertions(+) create mode 100644 .vscode/settings.json create mode 100644 Manifest.toml create mode 100644 Project.toml create mode 100644 src/DB_services.jl create mode 100644 src/Ironpen.jl create mode 100644 src/WPembeddings.jl create mode 100644 src/forward.jl create mode 100644 src/interface.jl create mode 100644 src/learn.jl create mode 100644 src/readout.jl create mode 100644 src/snn_utils.jl create mode 100644 src/types.jl create mode 100644 test/etc2.jl create mode 100644 test/etc3.jl create mode 100644 test/test_data_prep_for_db.jl diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/Manifest.toml b/Manifest.toml new file mode 100644 index 0000000..0080e6f --- /dev/null +++ b/Manifest.toml @@ -0,0 +1,913 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.9.0" +manifest_format = "2.0" +project_hash = "6da2bd801ebd94457c5a5cb36ae71250437066e8" + +[[deps.AbstractFFTs]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "16b6dbc4cf7caee4e1e75c49485ec67b667098a0" +uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" +version = "1.3.1" +weakdeps = ["ChainRulesCore"] + + [deps.AbstractFFTs.extensions] + AbstractFFTsChainRulesCoreExt = 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"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" diff --git a/Project.toml b/Project.toml new file mode 100644 index 0000000..96731de --- /dev/null +++ b/Project.toml @@ -0,0 +1,13 @@ +name = "Ironpen" +uuid = "29a645ab-0d6f-4ef8-acfd-1b192480382c" +authors = ["tonaerospace "] +version = "0.1.0" + +[deps] +Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" +Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" +GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" +Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" diff --git a/src/DB_services.jl b/src/DB_services.jl new file mode 100644 index 0000000..382fd88 --- /dev/null +++ b/src/DB_services.jl @@ -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 + + + + + + + + + + + + + + + diff --git a/src/Ironpen.jl b/src/Ironpen.jl new file mode 100644 index 0000000..a5b9945 --- /dev/null +++ b/src/Ironpen.jl @@ -0,0 +1,89 @@ +module Ironpen + +export kfn_1 + + +""" 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 + +""" + Todo: + [*3] no "start learning" use reset learning and "inference", "learning" mode instead + [4] add time-based learning method. Also implement "thinking period" + [5] verify that model can complete learning cycle with no error + [6] neuroplasticity() with synaptic connection strength concept + [] using RL to control learning signal + [] consider using Dates.now() instead of timestamp because time_stamp may overflow + [] training should include adjusting α, neuron membrane potential decay factor + which defined by neuron.tau_m formular 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 + + 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 diff --git a/src/WPembeddings.jl b/src/WPembeddings.jl new file mode 100644 index 0000000..c49a979 --- /dev/null +++ b/src/WPembeddings.jl @@ -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 \ No newline at end of file diff --git a/src/forward.jl b/src/forward.jl new file mode 100644 index 0000000..c527bc3 --- /dev/null +++ b/src/forward.jl @@ -0,0 +1,236 @@ +module forward + +using Flux.Optimise: apply! + +using Statistics, Flux, Random, LinearAlgebra +using GeneralUtils +using ..types, ..snn_utils + +#------------------------------------------------------------------------------------------------100 + +""" Model forward() +""" +function (m::model)(input_data::AbstractVector) + # m.global_tick += 1 + m.time_stamp += 1 + + # process all corresponding KFN + raw_model_respond = 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 raw_model_respond +end + +#------------------------------------------------------------------------------------------------100 + +""" knowledgeFn forward() +""" + +function (kfn::kfn_1)(m::model, input_data::AbstractVector) + kfn.time_stamp = m.time_stamp + kfn.softreset = m.softreset + kfn.learning_stage = m.learning_stage + kfn.error = m.error + + # 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 start from neuron id 1 + + for n in kfn.neurons_array + timestep_forward!(n) + end + for n in kfn.output_neurons_array + timestep_forward!(n) + end + + kfn.learning_stage = m.learning_stage + if kfn.learning_stage == "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.neurons_array + # epsilon_rec 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 + reset_learning_params!(n) + end + + # clear variables + kfn.firing_neurons_list = Vector{Int64}() + kfn.outputs = nothing + 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.neurons_array[i].z_t1 = data + end + + kfn.snn_firing_state_t0 = [n.z_t for n in kfn.neurons_array] #TODO check if it is used? + + #CHANGE Threads.@threads for n in kfn.neurons_array + for n in kfn.neurons_array + n(kfn) + end + + kfn.snn_firing_state_t1 = [n.z_t1 for n in kfn.neurons_array] + append!(kfn.firing_neurons_list, findall(kfn.snn_firing_state_t1)) # store id of neuron that fires + if kfn.learning_stage == "end_learning" # use for random new neuron connection + kfn.firing_neurons_list |> unique! + end + + # Threads.@threads for n in kfn.output_neurons_array + for n in kfn.output_neurons_array + n(kfn) + end + + out = [n.out_t1 for n in kfn.output_neurons_array] + + return out +end + +#------------------------------------------------------------------------------------------------100 + +""" passthrough_neuron forward() +""" +function (n::passthrough_neuron)(kfn::knowledgeFn) + n.time_stamp = kfn.time_stamp + # n.global_tick = kfn.global_tick +end + +#------------------------------------------------------------------------------------------------100 + +""" lif_neuron forward() +""" +function (n::lif_neuron)(kfn::knowledgeFn) + n.time_stamp = kfn.time_stamp + + # pulling other neuron's firing status at time t + n.z_i_t = getindex(kfn.snn_firing_state_t0, n.subscription_list) + n.z_i_t .*= n.sub_ExIn_type + + if n.refractory_counter != 0 + n.refractory_counter -= 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.recurrent_signal = n.recurrent_signal * 0.0 + + # Exponantial decay of v_t1 + n.v_t1 = n.v_t * n.alpha^(n.time_stamp - n.last_firing_time) # or n.v_t1 = n.alpha * n.v_t + else + n.recurrent_signal = sum(n.w_rec .* 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.recurrent_signal + + if n.v_t1 > n.v_th + n.z_t1 = true + n.refractory_counter = n.refractory_duration + n.firing_counter += 1 + n.v_t1 = n.v_t1 - n.v_th + else + n.z_t1 = false + end + + # there is a difference from alif formula + n.phi = (n.gamma_pd / n.v_th) * max(0, 1 - (n.v_t1 - n.v_th) / n.v_th) + end +end + +#------------------------------------------------------------------------------------------------100 + +""" alif_neuron forward() +""" +function (n::alif_neuron)(kfn::knowledgeFn) + n.time_stamp = kfn.time_stamp + + n.z_i_t = getindex(kfn.snn_firing_state_t0, n.subscription_list) + n.z_i_t .*= n.sub_ExIn_type + + if n.refractory_counter != 0 + n.refractory_counter -= 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.recurrent_signal = n.recurrent_signal * 0.0 + + # Exponantial decay of v_t1 + n.v_t1 = n.v_t * n.alpha^(n.time_stamp - n.last_firing_time) # or n.v_t1 = n.alpha * n.v_t + n.phi = 0 + else + n.z_t = isnothing(n.z_t) ? false : n.z_t + n.a = (n.rho * n.a) + ((1 - n.rho) * n.z_t) + n.av_th = n.v_th + (n.beta * n.a) + n.recurrent_signal = sum(n.w_rec .* 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.recurrent_signal + if n.v_t1 > n.av_th + n.z_t1 = true + n.refractory_counter = n.refractory_duration + n.firing_counter += 1 + n.v_t1 = n.v_t1 - n.v_th + else + n.z_t1 = false + end + + # there is a difference from lif formula + n.phi = (n.gamma_pd / n.v_th) * max(0, 1 - (n.v_t1 - n.av_th) / n.v_th) + end +end + +#------------------------------------------------------------------------------------------------100 + +""" linear_neuron forward() + In this implementation, each output neuron is fully connected to every lif and alif neuron. +""" +function (n::linear_neuron)(kfn::T) where T<:knowledgeFn + n.time_stamp = kfn.time_stamp + n.out_t1 = getindex(kfn.snn_firing_state_t1, n.subscription_list)[1] +end + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +end # end module \ No newline at end of file diff --git a/src/interface.jl b/src/interface.jl new file mode 100644 index 0000000..5896eee --- /dev/null +++ b/src/interface.jl @@ -0,0 +1,79 @@ +module interface + + +# export + +# using + +#------------------------------------------------------------------------------------------------100 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +end \ No newline at end of file diff --git a/src/learn.jl b/src/learn.jl new file mode 100644 index 0000000..b177f5b --- /dev/null +++ b/src/learn.jl @@ -0,0 +1,304 @@ +module learn + +using Flux.Optimise: apply! + +using Statistics, Flux, Random, LinearAlgebra +using GeneralUtils +using ..types + +export learn! + +#------------------------------------------------------------------------------------------------100 + +function learn!(m::model, model_respond, correct_answer) + if m.learning_stage == "learning" + model_error = Flux.logitcrossentropy(model_respond, correct_answer) + output_elements_error = model_respond - correct_answer + + learn!(m.knowledgeFn[:I], model_error, output_elements_error) + + #WORKING compute error + # if m.time_stamp < m.m + model_error = model_respond .- correct_answer + + + + + + + else + model_error = nothing + end + + return model_error +end + + +function learn!(m::model, raw_model_respond, correct_answer=nothing) + if m.learning_stage != "doing_inference" + model_error = Flux.logitcrossentropy(raw_model_respond, correct_answer) + output_elements_error = raw_model_respond - correct_answer + + learn!(m.knowledgeFn[:I], model_error, output_elements_error) + else + model_error = nothing + end + + return model_error +end + + + + +""" knowledgeFn learn() +""" +function learn!(kfn::knowledgeFn, error::Union{Float64,Nothing}=nothing, + output_error::Union{Vector,Nothing}=nothing) + kfn.error = error + kfn.output_error = output_error + + # Threads.@threads for n in kfn.neurons_array + for n in kfn.neurons_array + learn!(n, kfn) # Neurons are always learning, besides error from model output + end + + if kfn.output_error !== nothing + # Threads.@threads for n in kfn.output_neurons_array + for n in kfn.output_neurons_array # not use multithreading because 1st output neuron + # will set learning rate that will be used by + # other output neurons + learn!(n, kfn) + end + #TODO: put other KFN to learn here + + # for main loop user's display and training's exit condition + avg_neurons_firing_rate = 0.0 + for n in kfn.neurons_array + if typeof(n) <: compute_neuron + avg_neurons_firing_rate += n.firing_rate + end + end + kfn.avg_neurons_firing_rate = avg_neurons_firing_rate / + kfn.kfn_params[:compute_neuron_number] + avg_neurons_v_t1 = 0.0 + for n in kfn.neurons_array + if typeof(n) <: compute_neuron + avg_neurons_v_t1 += n.v_t1 + end + end + kfn.avg_neurons_v_t1 = avg_neurons_v_t1 / kfn.kfn_params[:compute_neuron_number] + end +end + +""" passthrough_neuron learn() +""" +function learn!(n::passthrough_neuron, kfn::knowledgeFn) + # skip +end + +""" lif learn() +""" +function learn!(n::lif_neuron, kfn::knowledgeFn) + if n.learnable_flag == true + + n.decayed_epsilon_rec = n.alpha * n.epsilon_rec + n.epsilon_rec = n.decayed_epsilon_rec + n.z_i_t + n.e_rec = n.phi * n.epsilon_rec + end + + # a piece of knowledgeFn error that belongs to this neuron + n.error = isnothing(kfn.error) ? nothing : kfn.error * n.Bn + n.learning_stage = kfn.learning_stage + + # accumulate voltage regularization terms + Snn_utils.cal_v_reg!(n) + + if n.learning_stage == "doing_inference" + # no learning + elseif n.learning_stage == "start_learning" || + n.learning_stage == "start_learning_no_wchange_reset" + + # if error signal available then accumulates Δw + if n.error !== nothing + Snn_utils.firing_rate!(n) + Snn_utils.firing_diff!(n) + n.w_rec_change = n.w_rec_change + + -apply!(n.optimiser, n.w_rec, + (n.error + Snn_utils.voltage_error!(n) + n.firing_rate_error) * n.e_rec) + + -Snn_utils.firing_rate_regulator!(n) + + -Snn_utils.voltage_regulator!(n) + end + elseif n.learning_stage == "during_learning" + # if error signal available then accumulates Δw + if n.error !== nothing + Snn_utils.firing_rate!(n) + Snn_utils.firing_diff!(n) + n.w_rec_change = n.w_rec_change + + -apply!(n.optimiser, n.w_rec, + (n.error + Snn_utils.voltage_error!(n) + n.firing_rate_error) * n.e_rec) + + -Snn_utils.firing_rate_regulator!(n) + + -Snn_utils.voltage_regulator!(n) + end + elseif n.learning_stage == "end_learning" + # if error signal available then accumulates Δw + if n.error !== nothing + Snn_utils.firing_rate!(n) + Snn_utils.firing_diff!(n) + n.w_rec_change = n.w_rec_change + + -apply!(n.optimiser, n.w_rec, + (n.error + Snn_utils.voltage_error!(n) + n.firing_rate_error) * n.e_rec) + + -Snn_utils.firing_rate_regulator!(n) + + -Snn_utils.voltage_regulator!(n) + end + + not_zero = (!iszero).(n.w_rec) + # set 0 in w_rec_change update according to 0 in w_rec for hard constrain connection + n.w_rec = n.w_rec + (not_zero .* n.w_rec_change) + replace!(x -> x < 0 ? 0 : x, n.w_rec) # no negative weight + + Snn_utils.neuroplasticity!(n, kfn.firing_neurons_list) + end +end + +""" alif_neuron learn() +""" +function learn!(n::alif_neuron, kfn::knowledgeFn) + n.decayed_epsilon_rec = n.alpha * n.epsilon_rec + n.epsilon_rec = n.decayed_epsilon_rec + n.z_i_t + n.epsilon_rec_a = (n.phi * n.epsilon_rec) + + ((n.rho - (n.phi * n.beta)) * n.epsilon_rec_a) + n.e_rec_v = n.phi * n.epsilon_rec + n.e_rec_a = -n.phi * n.beta * n.epsilon_rec_a + n.e_rec = n.e_rec_v + n.e_rec_a + + # a piece of knowledgeFn error that belongs to this neuron + n.error = isnothing(kfn.error) ? nothing : kfn.error * n.Bn + n.learning_stage = kfn.learning_stage + + + + if n.learning_stage == "doing_inference" + # no learning + elseif n.learning_stage == "start_learning" || + n.learning_stage == "start_learning_no_wchange_reset" + + # if error signal available then accumulates Δw + if n.error !== nothing + Snn_utils.firing_rate!(n) + Snn_utils.firing_diff!(n) + n.w_rec_change = n.w_rec_change + + -apply!(n.optimiser, n.w_rec, + (n.error + Snn_utils.voltage_error!(n) + n.firing_rate_error) * n.e_rec) + + -Snn_utils.firing_rate_regulator!(n) + + -Snn_utils.voltage_regulator!(n) + end + elseif n.learning_stage == "during_learning" + # if error signal available then accumulates Δw + if n.error !== nothing + Snn_utils.firing_rate!(n) + Snn_utils.firing_diff!(n) + n.w_rec_change = n.w_rec_change + + -apply!(n.optimiser, n.w_rec, + (n.error + Snn_utils.voltage_error!(n) + n.firing_rate_error) * n.e_rec) + + -Snn_utils.firing_rate_regulator!(n) + + -Snn_utils.voltage_regulator!(n) + end + elseif n.learning_stage == "end_learning" + + # if error signal available then accumulates Δw + if n.error !== nothing + Snn_utils.firing_rate!(n) + Snn_utils.firing_diff!(n) + n.w_rec_change = n.w_rec_change + + -apply!(n.optimiser, n.w_rec, + (n.error + Snn_utils.voltage_error!(n) + n.firing_rate_error) * n.e_rec) + + -Snn_utils.firing_rate_regulator!(n) + + -Snn_utils.voltage_regulator!(n) + end + + not_zero = (!iszero).(n.w_rec) + # set 0 in w_rec_change update according to 0 in w_rec for hard constrain connection + n.w_rec = n.w_rec + (not_zero .* n.w_rec_change) + replace!(x -> x < 0 ? 0 : x, n.w_rec) # no negative weight + + Snn_utils.neuroplasticity!(n, kfn.firing_neurons_list) + end +end + +""" linear_neuron learn() +""" +function learn!(n::linear_neuron, kfn::knowledgeFn) + n.error = kfn.output_error[n.id] + n.learning_stage = kfn.learning_stage + + if n.learning_stage == "doing_inference" + # no learning + elseif n.learning_stage == "start_learning" + # if error signal available then accumulates Δw + if n.error !== nothing && n.id == 1 # NOT working w/ multithreading training + Δw = -apply!(n.optimiser, n.w_out, (n.error * n.epsilon_j)) + n.w_out_change = n.w_out_change + Δw + n.eta = n.optimiser.eta + Δb = -n.eta * n.error + n.b_change = n.b_change + Δb + elseif n.error !== nothing && n.id !== 1 + n.eta = kfn.output_neurons_array[1].eta + Δw = -n.eta * n.error * n.epsilon_j + n.w_out_change = n.w_out_change + Δw + Δb = -n.eta * n.error + n.b_change = n.b_change + Δb + end + elseif n.learning_stage == "during_learning" + # if error signal available then accumulates Δw + if n.error !== nothing && n.id == 1 # NOT working w/ multithreading training + Δw = -apply!(n.optimiser, n.w_out, (n.error * n.epsilon_j)) + n.w_out_change = n.w_out_change + Δw + n.eta = n.optimiser.eta + Δb = -n.eta * n.error + n.b_change = n.b_change + Δb + elseif n.error !== nothing && n.id !== 1 + n.eta = kfn.output_neurons_array[1].eta + Δw = -n.eta * n.error * n.epsilon_j + n.w_out_change = n.w_out_change + Δw + Δb = -n.eta * n.error + n.b_change = n.b_change + Δb + end + elseif n.learning_stage == "end_learning" + # if error signal available then accumulates Δw + if n.error !== nothing && n.id == 1 # NOT working w/ multithreading training + Δw = -apply!(n.optimiser, n.w_out, (n.error * n.epsilon_j)) + n.w_out_change = n.w_out_change + Δw + n.eta = n.optimiser.eta + Δb = -n.eta * n.error + n.b_change = n.b_change + Δb + elseif n.error !== nothing && n.id !== 1 + n.eta = kfn.output_neurons_array[1].eta + Δw = -n.eta * n.error * n.epsilon_j + n.w_out_change = n.w_out_change + Δw + Δb = -n.eta * n.error + n.b_change = n.b_change + Δb + end + + n.w_out = n.w_out + n.w_out_change + n.b = n.b + n.b_change + end +end + + + + + + + + + + + + + + + + + + +end # module end \ No newline at end of file diff --git a/src/readout.jl b/src/readout.jl new file mode 100644 index 0000000..287abb3 --- /dev/null +++ b/src/readout.jl @@ -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 + + + + + + + + + + + + + + + + + + + + + diff --git a/src/snn_utils.jl b/src/snn_utils.jl new file mode 100644 index 0000000..bd35889 --- /dev/null +++ b/src/snn_utils.jl @@ -0,0 +1,319 @@ +module snn_utils + +using Flux.Optimise: apply! +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!, reset_learning_params!, reset_learning_history_params!, + cal_v_reg!, calculate_w_change_end!, + firing_rate_error!, firing_rate_regulator!, update_Bn!, cal_firing_reg!, + neuroplasticity!, shakeup!, reset_learning_no_wchange!, adjust_internal_learning_rate!, + gradient_withloss + +using Statistics, Random, LinearAlgebra, Distributions, Zygote + +using ..types + +#------------------------------------------------------------------------------------------------100 + +function timestep_forward!(x::passthrough_neuron) + x.z_t = x.z_t1 +end + +function timestep_forward!(x::compute_neuron) + x.z_t = x.z_t1 + x.v_t = x.v_t1 +end + +function timestep_forward!(x::linear_neuron) + x.out_t = x.out_t1 +end + +no_negative(n) = n < 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::compute_neuron) = n.last_firing_time = 0.0 +reset_refractory_state_active!(n::compute_neuron) = n.refractory_state_active = false +reset_v_t!(n::compute_neuron) = n.v_t = n.v_t_default +reset_z_t!(n::compute_neuron) = n.z_t = false +reset_epsilon_rec!(n::compute_neuron) = n.epsilon_rec = n.epsilon_rec * 0.0 +reset_epsilon_rec_a!(n::alif_neuron) = n.epsilon_rec_a = n.epsilon_rec_a * 0.0 +reset_epsilon_in!(n::compute_neuron) = n.epsilon_in = isnothing(n.epsilon_in) ? nothing : n.epsilon_in * 0.0 +reset_error!(n::Union{compute_neuron, linear_neuron}) = n.error = nothing +reset_w_in_change!(n::compute_neuron) = n.w_in_change = isnothing(n.w_in_change) ? nothing : n.w_in_change * 0.0 +reset_w_rec_change!(n::compute_neuron) = n.w_rec_change = n.w_rec_change * 0.0 +reset_a!(n::alif_neuron) = n.a = n.a * 0.0 +reset_reg_voltage_a!(n::compute_neuron) = n.reg_voltage_a = n.reg_voltage_a * 0.0 +reset_reg_voltage_b!(n::compute_neuron) = n.reg_voltage_b = n.reg_voltage_b * 0.0 +reset_reg_voltage_error!(n::compute_neuron) = n.reg_voltage_error = n.reg_voltage_error * 0.0 +reset_firing_counter!(n::compute_neuron) = n.firing_counter = n.firing_counter * 0.0 +reset_firing_diff!(n::Union{compute_neuron, linear_neuron}) = n.firing_diff = n.firing_diff * 0.0 +reset_previous_error!(n::Union{compute_neuron}) = + n.previous_error = n.previous_error * 0.0 + +# reset function for output neuron +reset_epsilon_j!(n::linear_neuron) = n.epsilon_j = n.epsilon_j * 0.0 +reset_out_t!(n::linear_neuron) = n.out_t = n.out_t * 0.0 +reset_w_out_change!(n::linear_neuron) = n.w_out_change = n.w_out_change * 0.0 +reset_b_change!(n::linear_neuron) = 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::lif_neuron) +# reset_epsilon_rec!(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{alif_neuron, elif_neuron}) +# reset_epsilon_rec!(n) +# reset_epsilon_rec_a!(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::linear_neuron) +# reset_epsilon_j!(n) +# reset_out_t!(n) +# reset_error!(n) +# end + +""" Reset all learning-related params at the END of learning session +""" +function reset_learning_params!(n::lif_neuron) + reset_epsilon_rec!(n) + reset_w_rec_change!(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_params!(n::alif_neuron) + reset_epsilon_rec!(n) + reset_epsilon_rec_a!(n) + reset_w_rec_change!(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::passthrough_neuron) +# end + +function reset_learning_params!(n::passthrough_neuron) + # skip +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.learning_stage == "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.learning_stage == "during_learning" + if kfn.knowledgeFn_error === nothing + #skip + else + push!(kfn.recent_knowledgeFn_error[end], kfn.knowledgeFn_error) + end + elseif kfn.learning_stage == "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.output_neurons_array + 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.kfn_params[:linear_neuron_number] # average + + input_neuron_number = kfn.kfn_params[:input_neuron_number] # skip input neuron + for i = 1:kfn.kfn_params[:compute_neuron_number] + n = kfn.neurons_array[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::lif_neuron) + # 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.epsilon_rec) +end + +function cal_v_reg!(n::alif_neuron) + # 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.epsilon_rec - n.epsilon_rec_a)) +end + +function voltage_error!(n::compute_neuron) + n.reg_voltage_error = 0.5 * n.reg_voltage_a + return n.reg_voltage_error +end + +function voltage_regulator!(n::compute_neuron) # running average + Δw = n.optimiser.eta * n.c_reg_v * n.reg_voltage_b + return Δw +end + +function firing_rate_error(kfn::knowledgeFn) + start_id = kfn.kfn_params[:input_neuron_number] + 1 + return 0.5 * sum([(n.firing_diff)^2 for n in kfn.neurons_array[start_id:end]]) +end + +function firing_rate_regulator!(n::compute_neuron) + # n.firing_rate NOT running average (average over learning batch) + Δw = n.optimiser.eta * n.c_reg * + (n.firing_rate - n.firing_rate_target) * n.e_rec + Δw = n.firing_rate > n.firing_rate_target ? Δw : Δw * 0.0 + return Δw +end + +firing_rate!(n::compute_neuron) = n.firing_rate = (n.firing_counter / n.time_stamp) * 1000 +firing_diff!(n::compute_neuron) = n.firing_diff = n.firing_rate - n.firing_rate_target + +function neuroplasticity!(n::compute_neuron, firing_neurons_list::Vector) + # if there is 0-weight then replace it with new connection + zero_weight_index = findall(iszero.(n.w_rec)) + if length(zero_weight_index) != 0 + """ sampling new connection from list of neurons that fires instead of ramdom choose from + all compute neuron because there is no point to connect to neuron that not fires i.e. + not fire = no information + """ + + subscribe_options = filter(x -> x ∉ [n.id], firing_neurons_list) # exclude this neuron id from the list + filter!(x -> x ∉ n.subscription_list, subscribe_options) # exclude this neuron's subscription_list from the list + shuffle!(subscribe_options) + end + + new_connection_percent = 10 - ((n.optimiser.eta / 0.0001) / 10) # percent is in range 0.1 to 10 + percentage = [new_connection_percent, 100.0 - new_connection_percent] / 100.0 + for i in zero_weight_index + if Utils.random_choices([true, false], percentage) + n.subscription_list[i] = pop!(subscribe_options) + n.w_rec[i] = 0.01 # new connection should not send large signal otherwise it would throw + # RSNN off path. Let weight grow by an optimiser + end + end +end + +function adjust_internal_learning_rate!(n::compute_neuron) + n.internal_learning_rate = n.error_diff[end] < 0.0 ? n.internal_learning_rate * 0.99 : + n.internal_learning_rate * 1.005 +end + +function push_epsilon_rec_a!(n::lif_neuron) + # skip +end + +function push_epsilon_rec_a!(n::alif_neuron) + push!(n.epsilon_rec_a, 0) +end + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +end # end module \ No newline at end of file diff --git a/src/types.jl b/src/types.jl new file mode 100644 index 0000000..f84842b --- /dev/null +++ b/src/types.jl @@ -0,0 +1,763 @@ +module types + +export + # struct + IronpenStruct, model, knowledgeFn, lif_neuron, alif_neuron, linear_neuron, + kfn_1, compute_neuron, neuron, output_neuron, passthrough_neuron, + + # function + instantiate_custom_types, init_neuron, populate_neuron, + add_neuron! + +using Random, Flux, LinearAlgebra + +#------------------------------------------------------------------------------------------------100 + +abstract type Ironpen end +abstract type knowledgeFn <: Ironpen end +abstract type neuron <: Ironpen end +abstract type input_neuron <: neuron end +abstract type output_neuron <: neuron end +abstract type compute_neuron <: neuron end + +#------------------------------------------------------------------------------------------------100 + +""" Model struct +""" +Base.@kwdef mutable struct model <: Ironpen + knowledgeFn::Union{Dict,Nothing} = nothing + model_params::Union{Dict,Nothing} = nothing + error::Union{Float64,Nothing} = 0.0 + output_error::Union{Array,Nothing} = Vector{AbstractFloat}() + + """ "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 w_rec_change then + reset epsilon_j. + "reflect" = neuron will merge w_rec_change into w_rec then reset w_rec_change. """ + learning_stage::String = "inference" + + softreset::Bool = false + time_stamp::Number = 0.0 +end +""" Model outer constructor + + # Example + I_kfnparams = Dict( + :type => "lif_neuron", + :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 + :gamma_pd => 0.3, # discount factor. The value is from the paper + :phi => 0.0, # psuedo derivative + :refractory_duration => 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) + + model_params_1 = Dict(:knowledgeFn => Dict(:I => I_kfn, + :run => run_kfn), + :learning_stage => "doing_inference",) + + model_1 = Ironpen_ai_gpu.model(model_params_1) +""" +function model(params::Dict) + m = model() + m.model_params = 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 + knowledgefn_name::Union{String,Nothing} = nothing + kfn_params::Union{Dict,Nothing} = nothing # store params of knowledgeFn itself for later use + time_stamp::Number = 0.0 + + # Bn contain error coefficient for both neurons and output neurons in one place + Bn::Vector{Float64} = Vector{Float64}() # error projection coefficient from kfn output's error to each neurons's error + neurons_array::Union{Array,Nothing} = [] # 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 """ + output_neurons_array::Union{Array,Nothing} = [] + + """ "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 w_rec_change then + reset epsilon_j. + "reflect" = neuron will merge w_rec_change into w_rec then reset w_rec_change. """ + learning_stage::String = "inference" + + error::Union{Float64,Nothing} = nothing + output_error::Union{Array,Nothing} = Vector{AbstractFloat}() + recent_knowledgeFn_error::Union{Any,Nothing} = nothing + softreset::Bool = false + meta_params::Union{Dict{Any,Any},Nothing} = Dict() + + firing_neurons_list::Array{Int64} = Vector{Int64}() # store id of firing neurons + snn_firing_state_t0::Union{Vector{Bool},Nothing} = nothing # store firing state of all neurons at t0 + snn_firing_state_t1::Union{Vector{Bool},Nothing} = nothing # store firing state of all neurons at t1 + + avg_neurons_firing_rate::Union{Float64,Nothing} = 0.0 # for displaying average firing rate over all neurons + avg_neurons_v_t1::Union{Float64,Nothing} = 0.0 # for displaying average v_t1 over all neurons +end + +#------------------------------------------------------------------------------------------------100 + +""" Knowledge function outer constructor >>> auto generate <<< + + # Example + + lif_neuron_params = Dict( + :type => "lif_neuron", + :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 + :gamma_pd => 0.3, # discount factor. The value is from the paper + :refractory_duration => 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 => "alif_neuron", + :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 + :gamma_pd => 0.3, # discount factor. The value is from the paper + :refractory_duration => 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 => "linear_neuron", + :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( + :knowledgefn_name => "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.neurons_array + :output_neuron_connection_pattern => "100%", # "60%" of kfn.neurons_array 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%", + :neuron_firing_rate_target => 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(kfn_params, lif_neuron_params, alif_neuron_params, linear_neuron_params) +""" +function kfn_1(kfn_params::Dict) + + kfn = kfn_1() + kfn.kfn_params = kfn_params + kfn.knowledgefn_name = kfn.kfn_params[:knowledgefn_name] + + if kfn.kfn_params[:compute_neuron_number] < kfn.kfn_params[:total_input_port] + throw(error("number of compute neuron must be greater than input neuron")) + end + + # Bn + if kfn.kfn_params[:Bn] == "random" + kfn.Bn = [Random.rand(0:0.001:1) for i in 1:kfn.kfn_params[:compute_neuron_number]] + else # in case I want to specify manually + kfn.Bn = [kfn.kfn_params[:Bn] for i in 1:kfn.kfn_params[:compute_neuron_number]] + end + + # assign neurons ID by their position in kfn.neurons array because I think it is + # straight forward way + + # add input port + for (k, v) in kfn.kfn_params[:input_port] + current_type = kfn.kfn_params[:input_port][k] + for i = 1:current_type[:numbers] + n_id = length(kfn.neurons_array) + 1 + neuron = init_neuron(n_id, current_type[:params], kfn.kfn_params) + push!(kfn.neurons_array, neuron) + end + end + + # add compute neurons + for (k, v) in kfn.kfn_params[:compute_neuron] + current_type = kfn.kfn_params[:compute_neuron][k] + for i = 1:current_type[:numbers] + n_id = length(kfn.neurons_array) + 1 + neuron = init_neuron(n_id, current_type[:params], kfn.kfn_params) + push!(kfn.neurons_array, neuron) + end + end + + for i = 1:kfn.kfn_params[:output_port][:numbers] + neuron = init_neuron(i, kfn.kfn_params[:output_port][:params], + kfn.kfn_params) + push!(kfn.output_neurons_array, neuron) + end + + # random which neuron output port subscribed to, 1-compute_neuron for each output port + sub_list = shuffle!([kfn.kfn_params[:total_input_port]+1:length(kfn.neurons_array)...]) + sub_output_neuron = [pop!(sub_list) for i in 1:kfn.kfn_params[:output_port][:numbers]] + for i in kfn.output_neurons_array + i.subscription_list = [pop!(sub_output_neuron)] + end + + for n in kfn.neurons_array + if typeof(n) <: compute_neuron + n.firing_rate_target = kfn.kfn_params[:neuron_firing_rate_target] + end + end + + # excitatory neuron to inhabitory neuron = 60:40 % of compute_neuron + ex_number = Int(floor(0.6 * kfn.kfn_params[:compute_neuron_number])) + ex_n = [1 for i in 1:ex_number] + in_number = kfn.kfn_params[:compute_neuron_number] - 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 reverse(kfn.neurons_array) + try n.ExIn_type = pop!(ex_in) catch end + end + + # add ExIn_type into each compute_neuron sub_ExIn_type + for n in reverse(kfn.neurons_array) + try # input neuron doest have n.subscription_list + for sub_id in n.subscription_list + n_ExIn_type = kfn.neurons_array[sub_id].ExIn_type + push!(n.sub_ExIn_type, n_ExIn_type) + end + catch + end + end + + return kfn +end + +#------------------------------------------------------------------------------------------------100 + +""" passthrough_neuron struct +""" +Base.@kwdef mutable struct passthrough_neuron <: input_neuron + id::Union{Int64,Nothing} = nothing # ID of this neuron which is it position in knowledgeFn array + type::String = "passthrough_neuron" + knowledgefn_name::Union{String,Nothing} = nothing # knowledgeFn that this neuron belongs to + z_t::Bool = false + z_t1::Bool = false + time_stamp::Number = 0.0 # current time + ExIn_type::Integer = 1 # 1 excitatory, -1 inhabitory. input neuron is always excitatory +end + +function passthrough_neuron(params::Dict) + n = passthrough_neuron() + 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 + +""" lif_neuron struct +""" +Base.@kwdef mutable struct lif_neuron <: compute_neuron + id::Union{Int64,Nothing} = nothing # this neuron ID i.e. position of this neuron in knowledgeFn + type::String = "lif_neuron" + ExIn_type::Integer = 1 # 1 excitatory, -1 inhabitory + # Bn::Union{Float64,Nothing} = Random.rand() # Bias for neuron error + knowledgefn_name::Union{String,Nothing} = nothing # knowledgeFn that this neuron belongs to + subscription_list::Union{Array{Int64},Nothing} = nothing # list of other neuron that this neuron synapse subscribed to + sub_ExIn_type::Array{Int64} = Vector{Int64}() # store ExIn type of subscribed neurons + time_stamp::Number = 0.0 # current time + w_rec::Union{Array{Float64},Nothing} = nothing # synaptic weight (for receiving signal from other neuron) + v_t::Float64 = 0.0 # vᵗ, postsynaptic neuron membrane potential of previous timestep + v_t1::Float64 = 0.0 # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep + v_t_default::Union{Float64,Nothing} = 0.0 # default membrane potential voltage + v_th::Float64 = 1.0 # vᵗʰ, neuron firing threshold + 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::Union{Array{Bool},Nothing} = nothing # neuron presynaptic firing at current timestep (which is other neuron postsynaptic firing of previous timestep) + # Bn_wout_decay::Union{Float64,Nothing} = 0.01 # use to balance Bn and w_out + + gamma_pd::Union{Float64,Nothing} = 0.3 # γ_pd, discount factor, value from paper + alpha::Union{Float64,Nothing} = nothing # α, neuron membrane potential decay factor + phi::Union{Float64,Nothing} = nothing # ϕ, psuedo derivative + epsilon_rec::Union{Array{Float64},Nothing} = nothing # ϵ_rec, eligibility vector for neuron spike + decayed_epsilon_rec::Union{Array{Float64},Nothing} = nothing # α * epsilon_rec + e_rec::Union{Array{Float64},Nothing} = nothing # eligibility trace for neuron spike + delta::Union{Float64,Nothing} = 1.0 # δ, discreate timestep size in millisecond + last_firing_time::Union{Float64,Nothing} = 0.0 # the last time neuron fires + refractory_duration::Union{Float64,Nothing} = 3 # neuron's refratory period in millisecond + # refractory_state_active::Union{Bool,Nothing} = false # if true, neuron is in refractory state and cannot process new information + refractory_counter::Integer = 0 + tau_m::Union{Float64,Nothing} = nothing # τ_m, membrane time constant in millisecond + eta::Union{Float64,Nothing} = 0.01 # η, learning rate + w_rec_change::Union{Array{Float64},Nothing} = nothing # Δw_rec, cumulated w_rec change + recurrent_signal::Union{Float64,Nothing} = nothing # incoming recurrent signal + alpha_v_t::Union{Float64,Nothing} = nothing # alpha * v_t + voltage_drop_percentage::Union{Float64,Nothing} = 1.0 # voltage drop as a percentage of v_th + error::Union{Float64,Nothing} = nothing # local neuron error + optimiser::Union{Any,Nothing} = load_optimiser("AdaBelief") # Flux optimizer + + firing_counter::Float64 = 0.0 # store how many times neuron fires + firing_rate_target::Float64 = 20.0 # neuron's target firing rate in Hz + firing_diff::Float64 = 0.0 # e-prop supplement paper equation 5 + firing_rate_error::Float64 = 0.0 # local neuron error w.r.t. firing regularization + firing_rate::Float64 = 0.0 # running average of firing rate in Hz + + current_error::Union{Float64,Nothing} = 0.0 + previous_error::Union{Float64,Nothing} = 0.0 + error_diff::Union{Array{Float64},Nothing} = Vector{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 w_rec_change then + reset epsilon_j. + "reflect" = neuron will merge w_rec_change into w_rec then reset w_rec_change. """ + learning_stage::String = "inference" +end + +""" lif neuron outer constructor + + # Example + + lif_neuron_params = Dict( + :type => "lif_neuron", + :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 + :gamma_pd => 0.3, # discount factor. The value is from the paper + :refractory_duration => 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 = lif_neuron(lif_neuron_params) +""" +function lif_neuron(params::Dict) + n = lif_neuron() + 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 + +""" alif_neuron struct +""" +Base.@kwdef mutable struct alif_neuron <: compute_neuron + id::Union{Int64,Nothing} = nothing # this neuron ID i.e. position of this neuron in knowledgeFn + type::String = "alif_neuron" + ExIn_type::Integer = -1 # 1 excitatory, -1 inhabitory + # Bn::Union{Float64,Nothing} = Random.rand() # Bias for neuron error + knowledgefn_name::Union{String,Nothing} = nothing # knowledgeFn that this neuron belongs to + subscription_list::Union{Array{Int64},Nothing} = nothing # list of other neuron that this neuron synapse subscribed to + sub_ExIn_type::Array{Int64} = Vector{Int64}() # store ExIn type of subscribed neurons + time_stamp::Union{Number,Nothing} = nothing # current time + w_rec::Union{Array{Float64},Nothing} = nothing # synaptic weight (for receiving signal from other neuron) + v_t::Float64 = 0.0 # vᵗ, postsynaptic neuron membrane potential of previous timestep + v_t1::Float64 = 0.0 # vᵗ⁺¹, postsynaptic neuron membrane potential at current timestep + v_t_default::Union{Float64,Nothing} = 0.0 + v_th::Float64 = 1.0 # vᵗʰ, neuron firing threshold + 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::Union{Array{Bool},Nothing} = nothing # neuron presynaptic firing at current timestep (which is other neuron postsynaptic firing of previous timestep) + # Bn_wout_decay::Union{Float64,Nothing} = 0.01 # use to balance Bn and w_out + + alpha::Union{Float64,Nothing} = nothing # α, neuron membrane potential decay factor + delta::Union{Float64,Nothing} = 1.0 # δ, discreate timestep size in millisecond + epsilon_rec::Union{Array{Float64},Nothing} = nothing # ϵ_rec(v), eligibility vector for neuron i spike + epsilon_rec_a::Union{Array{Float64},Nothing} = nothing # ϵ_rec(a) + decayed_epsilon_rec::Union{Array{Float64},Nothing} = nothing # α * epsilon_rec + e_rec_v::Union{Array{Float64},Nothing} = nothing # a component of neuron's eligibility trace resulted from v_t + e_rec_a::Union{Array{Float64},Nothing} = nothing # a component of neuron's eligibility trace resulted from av_th + e_rec::Union{Array{Float64},Nothing} = nothing # neuron's eligibility trace + eta::Union{Float64,Nothing} = 0.01 # eta, learning rate + gamma_pd::Union{Float64,Nothing} = 0.3 # γ_pd, discount factor, value from paper + last_firing_time::Union{Float64,Nothing} = 0.0 # the last time neuron fires + phi::Union{Float64,Nothing} = nothing # ϕ, psuedo derivative + refractory_duration::Union{Float64,Nothing} = 3 # neuron's refractory period in millisecond + # refractory_state_active::Union{Bool,Nothing} = false # if true, neuron is in refractory state and cannot process new information + refractory_counter::Integer = 0 + tau_m::Union{Float64,Nothing} = nothing # τ_m, membrane time constant in millisecond + w_rec_change::Union{Array{Float64},Nothing} = nothing # Δw_rec, cumulated w_rec change + recurrent_signal::Union{Float64,Nothing} = nothing # incoming recurrent signal + alpha_v_t::Union{Float64,Nothing} = nothing # alpha * v_t + voltage_drop_percentage::Union{Float64,Nothing} = 1.0 # voltage drop as a percentage of v_th + error::Union{Float64,Nothing} = nothing # local neuron error + optimiser::Union{Any,Nothing} = load_optimiser("AdaBelief") # Flux optimizer + + firing_counter::Float64 = 0.0 # store how many times neuron fires + firing_rate_target::Float64 = 20.0 # neuron's target firing rate in Hz + firing_diff::Float64 = 0.0 # e-prop supplement paper equation 5 + firing_rate_error::Float64 = 0.0 # local neuron error w.r.t. firing regularization + firing_rate::Float64 = 0.0 # running average of firing rate, Hz + + + current_error::Union{Float64,Nothing} = 0.0 + previous_error::Union{Float64,Nothing} = 0.0 + error_diff::Union{Array{Float64},Nothing} = Vector{Float64}() + + tau_a::Union{Float64,Nothing} = nothing # τ_a, adaption time constant in millisecond + beta::Union{Float64,Nothing} = 0.15 # β, constant, value from paper + rho::Union{Float64,Nothing} = nothing # ρ, threshold adaptation decay factor + a::Union{Float64,Nothing} = 0.0 # threshold adaptation + av_th::Union{Float64,Nothing} = nothing # 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 w_rec_change then + reset epsilon_j. + "reflect" = neuron will merge w_rec_change into w_rec then reset w_rec_change. """ + learning_stage::String = "inference" + +end +""" alif neuron outer constructor + + # Example + + alif_neuron_params = Dict( + :type => "alif_neuron", + :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 + :gamma_pd => 0.3, # discount factor. The value is from the paper + :refractory_duration => 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 = alif_neuron(alif_neuron_params) +""" +function alif_neuron(params::Dict) + n = alif_neuron() + 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 +""" linear_neuron struct +""" +Base.@kwdef mutable struct linear_neuron <: output_neuron + id::Union{Int64,Nothing} = nothing # ID of this neuron which is it position in knowledgeFn array + type::String = "linear_neuron" + knowledgefn_name::Union{String,Nothing} = nothing # knowledgeFn that this neuron belongs to + subscription_list::Union{Array{Int64},Nothing} = nothing # list of other neuron that this neuron synapse subscribed to + time_stamp::Union{Number,Nothing} = nothing # current time + delta::Union{Float64,Nothing} = 1.0 # δ, discreate timestep size in millisecond + out_t::Bool = false # output of linear neuron BEFORE forward() + out_t1::Bool = false # output of linear neuron AFTER forward() +end + +""" linear neuron outer constructor + + # Example + + linear_neuron_params = Dict( + :type => "linear_neuron", + :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 = linear_neuron(linear_neuron_params) +""" +function linear_neuron(params::Dict) + n = linear_neuron() + 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::passthrough_neuron, n_params::Dict, kfn_params::Dict) + n.id = id + n.knowledgefn_name = kfn_params[:knowledgefn_name] +end + +# function init_neuron!(id::Int64, n::lif_neuron, kfn_params::Dict) +# n.id = id +# n.knowledgefn_name = kfn_params[:knowledgefn_name] +# subscription_options = shuffle!([1:(kfn_params[:input_neuron_number]+kfn_params[:compute_neuron_number])...]) +# if typeof(kfn_params[:synaptic_connection_number]) == String +# percent = parse(Int, kfn_params[:synaptic_connection_number][1:end-1]) / 100 +# synaptic_connection_number = floor(length(subscription_options) * percent) +# n.subscription_list = [pop!(subscription_options) for i = 1:synaptic_connection_number] +# end +# filter!(x -> x != n.id, n.subscription_list) +# n.epsilon_rec = zeros(length(n.subscription_list)) +# n.w_rec = Random.rand(length(n.subscription_list)) +# n.w_rec_change = zeros(length(n.subscription_list)) +# n.reg_voltage_b = zeros(length(n.subscription_list)) +# n.alpha = calculate_α(n) +# end + +function init_neuron!(id::Int64, n::lif_neuron, n_params::Dict, kfn_params::Dict) + n.id = id + n.knowledgefn_name = kfn_params[:knowledgefn_name] + subscription_options = shuffle!([1:kfn_params[:total_neurons]...]) + subscription_numbers = Int(floor(n_params[:synaptic_connection_number] * + kfn_params[:total_neurons] / 100.0)) + n.subscription_list = [pop!(subscription_options) for i = 1:subscription_numbers] + + # prevent subscription to itself by removing this neuron id + filter!(x -> x != n.id, n.subscription_list) + + n.epsilon_rec = zeros(length(n.subscription_list)) + n.w_rec = Random.rand(length(n.subscription_list)) + n.w_rec_change = zeros(length(n.subscription_list)) + # n.reg_voltage_b = zeros(length(n.subscription_list)) + n.alpha = calculate_α(n) +end + +function init_neuron!(id::Int64, n::alif_neuron, n_params::Dict, + kfn_params::Dict) + n.id = id + n.knowledgefn_name = kfn_params[:knowledgefn_name] + subscription_options = shuffle!([1:kfn_params[:total_neurons]...]) + subscription_numbers = Int(floor(n_params[:synaptic_connection_number] * + kfn_params[:total_neurons] / 100.0)) + n.subscription_list = [pop!(subscription_options) for i = 1:subscription_numbers] + + # prevent subscription to itself by removing this neuron id + filter!(x -> x != n.id, n.subscription_list) + + n.epsilon_rec = zeros(length(n.subscription_list)) + n.w_rec = Random.rand(length(n.subscription_list)) + n.w_rec_change = zeros(length(n.subscription_list)) + # n.reg_voltage_b = zeros(length(n.subscription_list)) + n.alpha = calculate_α(n) # the more time has passed from the last time neuron was + # activated, the more neuron membrane potential is reduced + n.rho = calculate_ρ(n) + n.epsilon_rec_a = zeros(length(n.subscription_list)) +end + +# function init_neuron!(id::Int64, n::linear_neuron, kfn_params::Dict) +# n.id = id +# n.knowledgefn_name = kfn_params[:knowledgefn_name] +# start_id = kfn_params[:input_neuron_number] + 1 # don't readout from input neurons +# n.subscription_list = [start_id:(start_id+kfn_params[:compute_neuron_number]-1)...] +# n.epsilon_j = zeros(length(n.subscription_list)) +# n.w_out = Random.randn(length(n.subscription_list)) +# n.w_out_change = zeros(length(n.subscription_list)) +# n.b = Random.randn() +# n.b_change = 0.0 +# n.k = calculate_k(n) +# end +#WORKING +function init_neuron!(id::Int64, n::linear_neuron, n_params::Dict, kfn_params::Dict) + n.id = id + n.knowledgefn_name = kfn_params[:knowledgefn_name] + # start_id = kfn_params[:total_input_port] + 1 # don't readout from input neurons + # subscription_options = [start_id:(start_id+kfn_params[:total_compute_neuron]-1)...] + # n.subscription_list = [rand(subscription_options)] + + # n.epsilon_j = zeros(length(n.subscription_list)) + # n.w_out = Random.randn(length(n.subscription_list)) + # n.w_out_change = zeros(length(n.subscription_list)) + # n.b = Random.randn() + # n.b_change = 0.0 + # n.k = calculate_k(n) +end + +""" Make a neuron intended for use with knowledgeFn +""" +function init_neuron(id::Int64, n_params::Dict, kfn_params::Dict) + n = instantiate_custom_types(n_params) + init_neuron!(id, n, n_params, kfn_params) + + 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 == "passthrough_neuron" + return passthrough_neuron(params) + elseif type == "lif_neuron" + return lif_neuron(params) + elseif type == "alif_neuron" + return alif_neuron(params) + elseif type == "linear_neuron" + return linear_neuron(params) + else + return nothing + end +end + +""" Add a new neuron into a knowledgeFn + +# Example + add_neuron!(kfn.kfn_params[:lif_neuron_params], kfn) +""" +# function add_neuron!(neuron_Dict::Dict, kfn::knowledgeFn) +# id = length(kfn.neurons_array) + 1 +# neuron = init_neuron(id, neuron_Dict, kfn.kfn_params, +# total_neurons = (length(kfn.neurons_array) + 1)) +# push!(kfn.neurons_array, neuron) + +# # Randomly select an output neuron to add a new neuron to +# add_n_output_n!(Random.rand(kfn.output_neurons_array), id) +# end + +""" Add a new neuron to output neuron's subscription_list +""" +function add_n_output_n!(o_n::linear_neuron, id::Int64) + push!(o_n.subscription_list, id) + push!(o_n.epsilon_j, 0.0) + push!(o_n.w_out, Random.randn(1)[1]) + push!(o_n.w_out_change, 0.0) +end + +calculate_α(neuron::lif_neuron) = exp(-neuron.delta / neuron.tau_m) +calculate_α(neuron::alif_neuron) = exp(-neuron.delta / neuron.tau_m) +calculate_ρ(neuron::alif_neuron) = exp(-neuron.delta / neuron.tau_a) +calculate_k(neuron::linear_neuron) = exp(-neuron.delta / neuron.tau_out) + +#------------------------------------------------------------------------------------------------100 + + + + + + + + + + + + + + + + + + + + + + + + + + + + +end # module end + + + + + diff --git a/test/etc2.jl b/test/etc2.jl new file mode 100644 index 0000000..e69de29 diff --git a/test/etc3.jl b/test/etc3.jl new file mode 100644 index 0000000..c5e9d4e --- /dev/null +++ b/test/etc3.jl @@ -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) \ No newline at end of file diff --git a/test/test_data_prep_for_db.jl b/test/test_data_prep_for_db.jl new file mode 100644 index 0000000..309b9c4 --- /dev/null +++ b/test/test_data_prep_for_db.jl @@ -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) + + + + + + + + + + + +