827 lines
40 KiB
Julia
827 lines
40 KiB
Julia
using Pkg; Pkg.activate("."); Pkg.resolve(), Pkg.instantiate()
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using Revise
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using Flux #, CUDA
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using BSON, JSON3
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using MLDatasets: MNIST
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using MLUtils, Images, ProgressMeter, Dates, DataFrames, Random, Statistics, LinearAlgebra,
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BenchmarkTools, Serialization, OneHotArrays , GLMakie # ClickHouse
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# if one need to reinstall all python packages
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# try Pkg.rm("PythonCall") catch end # should be removed before using CondaPkg to install packages
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# condapackage = ["numpy", "pytorch", "snntorch"]
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# using CondaPkg # in CondaPkg.toml file, channels = ["anaconda", "conda-forge", "pytorch"]
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# for i in condapackage
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# try CondaPkg.rm(i) catch end
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# end
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# for i in condapackage
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# CondaPkg.add(i)
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# end
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# Pkg.add("PythonCall");
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using PythonCall;
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np = pyimport("numpy")
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torch = pyimport("torch")
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spikegen = pyimport("snntorch.spikegen") # https://github.com/jeshraghian/snntorch
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using Ironpen
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using GeneralUtils
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sep = Sys.iswindows() ? "\\" : "/"
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rootDir = pwd()
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# select compute device
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# device = Flux.CUDA.functional() ? gpu : cpu
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# if device == gpu
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# CUDA.device!(3)
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# end
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#------------------------------------------------------------------------------------------------100
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"""
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Todo:
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- []
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Change from version:
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-
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All features
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-
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"""
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# communication config --------------------------------------------------------------------------100
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database_ip = "localhost"
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# database_ip = "192.168.0.8"
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#------------------------------------------------------------------------------------------------100
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function generate_snn(filename::String, location::String)
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expect_compute_neuron_numbers = 1024 #FIXME change to 512
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signalInput_portnumbers = 50
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noise_portnumbers = signalInput_portnumbers
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output_portnumbers = 10
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lif_neuron_number = Int(floor(expect_compute_neuron_numbers * 0.4))
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alif_neuron_number = expect_compute_neuron_numbers - lif_neuron_number # from Allen Institute, ALIF is 20-40% of LIF
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computeNeuronNumber = lif_neuron_number + alif_neuron_number
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totalNeurons = computeNeuronNumber + noise_portnumbers + signalInput_portnumbers
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totalInputPort = noise_portnumbers + signalInput_portnumbers
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# kfn and neuron config
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passthrough_neuron_params = Dict(
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:type => "passthroughNeuron"
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)
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lif_neuron_params = Dict{Symbol, Any}(
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:type => "lifNeuron",
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:v_t_default => 0.0,
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:v_th => 1.0, # neuron firing threshold (this value is treated as maximum bound if I use auto generate)
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:tau_m => 200.0, # membrane time constant in millisecond.
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:eta => 1e-2,
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# Good starting value is 1/10th of tau_a
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# This is problem specific parameter. It controls how leaky the neuron is.
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# Too high(less leaky) makes learning algo harder to move model into direction that reduce error
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# resulting in model's error to explode exponantially likely because learning algo will try to
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# exert more force (larger w_out_change) to move neuron into direction that reduce error
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# For example, model error from 7 to 2e6.
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:synapticConnectionPercent => 50, # % coverage of total neurons in kfn
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:w_rec_generation_pattern => "random",
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)
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alif_neuron_params = Dict{Symbol, Any}(
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:type => "alifNeuron",
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:v_t_default => 0.0,
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:v_th => 1.0, # neuron firing threshold (this value is treated as maximum bound if I use auto generate)
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:tau_m => 200.0, # membrane time constant in millisecond.
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:eta => 1e-2,
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# Good starting value is 1/10th of tau_a
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# This is problem specific parameter. It controls how leaky the neuron is.
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# Too high(less leaky) makes learning algo harder to move model into direction that reduce error
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# resulting in model's error to explode exponantially likely because learning algo will try to
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# exert more force (larger w_out_change) to move neuron into direction that reduce error
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# For example, model error from 7 to 2e6.
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:tau_a => 500.0, # adaptation time constant in millisecond. it defines neuron memory length.
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# This is problem specific parameter
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# Good starting value is 0.5 to 2 times of info STORE-RECALL length i.e. total time SNN takes to
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# perform a task, for example, equals to episode length.
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# From "Spike frequency adaptation supports network computations on temporally dispersed
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# information"
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:synapticConnectionPercent => 50, # % coverage of total neurons in kfn
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:w_rec_generation_pattern => "random",
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)
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# linear_neuron_params = Dict{Symbol, Any}(
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# :type => "linearNeuron",
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# :v_th => 1.0, # neuron firing threshold (this value is treated as maximum bound if I use auto generate)
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# :tau_out => 50.0, # output time constant in millisecond.
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# :synapticConnectionPercent => 100, # % coverage of total neurons in kfn
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# # Good starting value is 1/50th of tau_a
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# # This is problem specific parameter.
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# # It controls how leaky the neuron is.
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# # Too high(less leaky) makes learning algo harder to move model into direction that reduce error
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# # resulting in model's error to explode exponantially. For example, model error from 7 to 2e6
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# # One can image training output neuron is like Tetris Game.
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# )
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integrate_neuron_params = Dict{Symbol, Any}(
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:type => "integrateNeuron",
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:synapticConnectionPercent => 100, # % coverage of total neurons in kfn
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:eta => 1e-2,
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:tau_out => 100.0,
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# Good starting value is 1/50th of tau_a
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# This is problem specific parameter.
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# It controls how leaky the neuron is.
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# Too high(less leaky) makes learning algo harder to move model into direction that reduce error
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# resulting in model's error to explode exponantially. For example, model error from 7 to 2e6
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# One can image training output neuron is like Tetris Game.
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)
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I_kfnparams = Dict{Symbol, Any}(
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:knowledgeFnName=> "I",
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:computeNeuronNumber=> computeNeuronNumber,
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:neuronFiringRateTarget=> 10.0, # Hz
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:Bn=> "random", # error projection coefficient for EACH neuron
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:totalNeurons=> totalNeurons,
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:totalInputPort=> totalInputPort,
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:totalComputeNeuron=> computeNeuronNumber,
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# group relavent info
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:inputPort=> Dict(
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:noise=> Dict(
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:numbers=> noise_portnumbers,
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:params=> passthrough_neuron_params,
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),
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:signal=> Dict(
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:numbers=> signalInput_portnumbers, # in case of GloVe word encoding, it is 300
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:params=> passthrough_neuron_params,
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),
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),
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:outputPort=> Dict(
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:numbers=> output_portnumbers, # output neuron, this is also the output length
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:params=> integrate_neuron_params,
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),
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:computeNeuron=> Dict(
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:1=> Dict(
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:numbers=> lif_neuron_number,
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:params=> lif_neuron_params,
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),
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:2=> Dict(
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:numbers=> alif_neuron_number,
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:params=> alif_neuron_params,
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),
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),
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)
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#------------------------------------------------------------------------------------------------100
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I_kfn = Ironpen.kfn_1(I_kfnparams)
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model_params_1 = Dict(:knowledgeFn => Dict(
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:I => I_kfn),
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)
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model = Ironpen.model(model_params_1)
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serialize(location * sep * filename, model)
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println("SNN generated")
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end
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function data_loader()
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# test problem
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fullTrainDataset = MNIST(:train)
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prototypeDataset = fullTrainDataset[1:10] # use reshape(test_dataset[1], (:, 1)) to flaten matrix
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trainDataset = fullTrainDataset # total 60000
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validateDataset = fullTrainDataset[1:100]
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labelDict = [0:9...]
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trainData = MLUtils.DataLoader(
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trainDataset; # fullTrainDataset or trainDataset
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batchsize=100,
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collate=true,
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shuffle=true,
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buffer=true,
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partial=false, # better for gpu memory if batchsize is fixed
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# parallel=true, #BUG ?? causing dataloader into forever loop
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)
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validateData = MLUtils.DataLoader(
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validateDataset;
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batchsize=1,
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collate=true,
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shuffle=true,
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buffer=true,
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partial=false, # better for gpu memory if batchsize is fixed
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# parallel=true, #BUG ?? causing dataloader into forever loop
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)
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#CHANGE dummy data used to debug
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# trainData = [(rand(10, 10), [5]), (rand(10, 10), [2])]
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# trainData = [(rand(10, 10), [5]),]
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return trainData, validateData, labelDict
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end
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function train_snn(model_name::String, filename::String, location::String,
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trainData, validateData, labelDict::Vector)
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println("loading SNN model")
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model = deserialize(location * sep * filename)
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println("model loading completed")
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# random seed
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# rng = MersenneTwister(1234)
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logitLog = zeros(10, 2)
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firedNeurons_t1 = zeros(1)
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var1 = zeros(10, 2)
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var2 = zeros(10, 2)
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var3 = zeros(10, 2)
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var4 = zeros(10, 2)
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# ----------------------------------- plot ----------------------------------- #
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plot10 = Observable(firedNeurons_t1)
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plot20 = Observable(logitLog[1 , :])
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plot21 = Observable(logitLog[2 , :])
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plot22 = Observable(logitLog[3 , :])
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plot23 = Observable(logitLog[4 , :])
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plot24 = Observable(logitLog[5 , :])
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plot25 = Observable(logitLog[6 , :])
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plot26 = Observable(logitLog[7 , :])
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plot27 = Observable(logitLog[8 , :])
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plot28 = Observable(logitLog[9 , :])
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plot29 = Observable(logitLog[10, :])
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plot30 = Observable(var1[1 , :])
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plot31 = Observable(var1[2 , :])
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plot32 = Observable(var1[3 , :])
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plot33 = Observable(var1[4 , :])
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plot34 = Observable(var1[5 , :])
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plot35 = Observable(var1[6 , :])
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plot36 = Observable(var1[7 , :])
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plot37 = Observable(var1[8 , :])
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plot38 = Observable(var1[9 , :])
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plot39 = Observable(var1[10, :])
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plot40 = Observable(var2[1 , :])
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plot41 = Observable(var2[2 , :])
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plot42 = Observable(var2[3 , :])
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plot43 = Observable(var2[4 , :])
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plot44 = Observable(var2[5 , :])
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plot45 = Observable(var2[6 , :])
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plot46 = Observable(var2[7 , :])
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plot47 = Observable(var2[8 , :])
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plot48 = Observable(var2[9 , :])
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plot49 = Observable(var2[10, :])
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plot50 = Observable(var3[1 , :])
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plot51 = Observable(var3[2 , :])
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plot52 = Observable(var3[3 , :])
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plot53 = Observable(var3[4 , :])
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plot54 = Observable(var3[5 , :])
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plot55 = Observable(var3[6 , :])
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plot56 = Observable(var3[7 , :])
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plot57 = Observable(var3[8 , :])
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plot58 = Observable(var3[9 , :])
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plot59 = Observable(var3[10, :])
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plot60 = Observable(var4[1 , :])
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plot61 = Observable(var4[2 , :])
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plot62 = Observable(var4[3 , :])
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plot63 = Observable(var4[4 , :])
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plot64 = Observable(var4[5 , :])
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plot65 = Observable(var4[6 , :])
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plot66 = Observable(var4[7 , :])
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plot67 = Observable(var4[8 , :])
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plot68 = Observable(var4[9 , :])
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plot69 = Observable(var4[10, :])
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# main figure
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fig1 = Figure()
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subfig1 = GLMakie.Axis(fig1[1, 1], # define position of this subfigure inside a figure
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title = "RSNN firedNeurons_t1",
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xlabel = "time",
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ylabel = "data"
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)
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lines!(subfig1, plot10, label = "firedNeurons_t1")
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axislegend(subfig1, position = :lb)
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subfig2 = GLMakie.Axis(fig1[2, 1], # define position of this subfigure inside a figure
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title = "output neurons activation",
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xlabel = "time",
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ylabel = "data"
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)
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lines!(subfig2, plot20, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig2, plot21, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig2, plot22, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig2, plot23, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig2, plot24, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig2, plot25, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig2, plot26, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig2, plot27, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig2, plot28, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig2, plot29, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10))
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axislegend(subfig2, position = :lb)
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subfig3 = GLMakie.Axis(fig1[3, 1], # define position of this subfigure inside a figure
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title = "output neurons membrane potential v_t1",
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xlabel = "time",
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ylabel = "data"
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)
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lines!(subfig3, plot30, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig3, plot31, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig3, plot32, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig3, plot33, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig3, plot34, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig3, plot35, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig3, plot36, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig3, plot37, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig3, plot38, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig3, plot39, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10))
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axislegend(subfig3, position = :lb)
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subfig4 = GLMakie.Axis(fig1[4, 1], # define position of this subfigure inside a figure
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title = "output neuron wRec",
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xlabel = "time",
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ylabel = "data"
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)
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lines!(subfig4, plot40, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig4, plot41, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig4, plot42, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig4, plot43, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig4, plot44, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig4, plot45, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig4, plot46, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig4, plot47, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig4, plot48, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig4, plot49, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10))
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axislegend(subfig4, position = :lb)
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subfig5 = GLMakie.Axis(fig1[5, 1], # define position of this subfigure inside a figure
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title = "output neuron epsilonRec",
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xlabel = "time",
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ylabel = "data"
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)
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lines!(subfig5, plot50, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig5, plot51, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig5, plot52, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig5, plot53, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig5, plot54, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig5, plot55, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig5, plot56, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig5, plot57, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig5, plot58, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig5, plot59, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10))
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axislegend(subfig5, position = :lb)
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subfig6 = GLMakie.Axis(fig1[6, 1], # define position of this subfigure inside a figure
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title = "output neuron wRecChange",
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xlabel = "time",
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ylabel = "data"
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)
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lines!(subfig6, plot60, label = "0", color = 1, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig6, plot61, label = "1", color = 2, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig6, plot62, label = "2", color = 3, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig6, plot63, label = "3", color = 4, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig6, plot64, label = "4", color = 5, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig6, plot65, label = "5", color = 6, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig6, plot66, label = "6", color = 7, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig6, plot67, label = "7", color = 8, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig6, plot68, label = "8", color = 9, colormap = :tab10, colorrange = (1, 10) )
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lines!(subfig6, plot69, label = "9", color = 10, colormap = :tab10, colorrange = (1, 10))
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axislegend(subfig6, position = :lb)
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# wait(display(fig1))
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# display(fig1)
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# --------------------------------- end plot --------------------------------- #
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# model learning
|
||
maxRepeatRound = 1 # repeat each image
|
||
thinkingPeriod = 16 # 1000-784 = 216
|
||
for epoch = 1:1000
|
||
println("epoch $epoch")
|
||
for (imgBatch, labelBatch) in trainData
|
||
@showprogress for i in eachindex(labelBatch)
|
||
_img = (imgBatch[:, :, i])
|
||
img = reshape(_img, (:, 1))
|
||
row, col = size(img)
|
||
label = labelBatch[i]
|
||
println("epoch $epoch training label $label")
|
||
|
||
img_tensor = torch.from_numpy( np.asarray(img) )
|
||
|
||
# create more data for RSNN
|
||
spike = spikegen.delta(img_tensor, threshold=0.1, off_spike=true)
|
||
spike1 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike2 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike3 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike4 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike5 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike6 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike7 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike8 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike9 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike10 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
|
||
spike = spikegen.delta(img_tensor, threshold=0.2, off_spike=true)
|
||
spike11 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike12 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike13 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike14 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike15 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike16 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike17 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike18 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike19 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike20 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
|
||
spike = spikegen.delta(img_tensor, threshold=0.3, off_spike=true)
|
||
spike21 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike22 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike23 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike24 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike25 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike26 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike27 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike28 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike29 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike30 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
|
||
spike = spikegen.delta(img_tensor, threshold=0.4, off_spike=true)
|
||
spike31 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike32 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike33 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike34 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike35 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike36 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike37 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike38 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike39 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike40 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
|
||
spike = spikegen.delta(img_tensor, threshold=0.5, off_spike=true)
|
||
spike41 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike42 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike43 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike44 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike45 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike46 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike47 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike48 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike49 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike50 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
|
||
input = [spike1;; spike2;; spike3;; spike4;; spike5;; spike6;; spike7;; spike8;; spike9;; spike10;;
|
||
spike11;; spike12;; spike13;; spike14;; spike15;; spike16;; spike17;; spike18;; spike19;; spike20;;
|
||
spike21;; spike22;; spike23;; spike24;; spike25;; spike26;; spike27;; spike28;; spike29;; spike30;;
|
||
spike31;; spike32;; spike33;; spike34;; spike35;; spike36;; spike37;; spike38;; spike39;; spike40;;
|
||
spike41;; spike42;; spike43;; spike44;; spike45;; spike46;; spike47;; spike48;; spike49;; spike50
|
||
]' # ' to flip 784x10 to 10x784
|
||
|
||
predict = 0
|
||
|
||
for k in 1:maxRepeatRound
|
||
|
||
# insert data into model sequencially
|
||
for i in 1:(row + thinkingPeriod) # sMNIST ihas 784 timestep(pixel) + thinking period = 1000 timestep
|
||
tick = i
|
||
if i <= row
|
||
current_pixel = input[:, i]
|
||
else
|
||
current_pixel = zeros(size(input)[1]) # dummy input in "thinking" period
|
||
end
|
||
|
||
if tick == 1 # tell a model to start learning. 1-time only
|
||
model.learningStage = "start_learning"
|
||
|
||
elseif tick == (row+thinkingPeriod)
|
||
model.learningStage = "end_learning"
|
||
else
|
||
end
|
||
|
||
_firedNeurons_t1, logit, _var1, _var2, _var3, _var4 = model(current_pixel)
|
||
# log answer of all timestep
|
||
logitLog = [logitLog;; logit]
|
||
firedNeurons_t1 = push!(firedNeurons_t1, _firedNeurons_t1)
|
||
var1 = [var1;; _var1]
|
||
var2 = [var2;; _var2]
|
||
var3 = [var3;; _var3]
|
||
var4 = [var4;; _var4]
|
||
|
||
# if tick <= row # online learning, 1-by-1 timestep
|
||
# correctAnswer = zeros(length(logit))
|
||
# modelError = (logit - correctAnswer) * 1.0
|
||
# Ironpen.compute_wRecChange!(model, modelError, correctAnswer)
|
||
# elseif tick == row+1
|
||
# correctAnswer = OneHotArrays.onehot(label, labelDict)
|
||
# modelError = (logit - correctAnswer) * 1.0
|
||
# Ironpen.compute_wRecChange!(model, modelError, correctAnswer)
|
||
# elseif tick > row+1 && tick < row+thinkingPeriod
|
||
# correctAnswer = OneHotArrays.onehot(label, labelDict)
|
||
# modelError = (logit - correctAnswer) * 1.0
|
||
# Ironpen.compute_wRecChange!(model, modelError, correctAnswer)
|
||
# elseif tick == row+thinkingPeriod
|
||
# _predict = logitLog[:, end-thinkingPeriod+1:end] # answer count during thinking period
|
||
# _predict = Int.([sum(row) for row in eachrow(_predict)])
|
||
# # predict = [x > 0 for x in _predict]
|
||
# correctAnswer = OneHotArrays.onehot(label, labelDict)
|
||
# modelError = (logit - correctAnswer) * 1.0
|
||
# Ironpen.compute_wRecChange!(model, modelError, correctAnswer)
|
||
# Ironpen.learn!(model)
|
||
# println("label $label predict $(_predict) model error $(Int.(modelError))")
|
||
# else
|
||
# error("undefined condition line $(@__LINE__)")
|
||
# end
|
||
|
||
if tick <= row # online learning, 1-by-1 timestep
|
||
# no error calculation
|
||
elseif tick > row && tick < row+thinkingPeriod
|
||
# correctAnswer = OneHotArrays.onehot(label, labelDict)
|
||
# modelError = (logit - correctAnswer) * 1.0
|
||
# Ironpen.compute_wRecChange!(model, modelError, correctAnswer)
|
||
|
||
elseif tick == row+thinkingPeriod
|
||
correctAnswer = OneHotArrays.onehot(label, labelDict)
|
||
modelError = Flux.logitcrossentropy(logit, correctAnswer) * 1.0
|
||
outputError = (logit - correctAnswer) * 1.0
|
||
Ironpen.compute_wRecChange!(model, modelError, outputError)
|
||
Ironpen.learn!(model)
|
||
_logit = round.(logit; digits=2)
|
||
predict = findall(isequal.(logit, maximum(logit)))[1] - 1
|
||
y = round.(modelError; digits=2)
|
||
println("")
|
||
println("label $label predict $predict logit $_logit model error $y")
|
||
else
|
||
error("undefined condition line $(@__LINE__)")
|
||
end
|
||
|
||
# update plot
|
||
plot10[] = firedNeurons_t1
|
||
|
||
plot20[] = view(logitLog, 1 , :)
|
||
plot21[] = view(logitLog, 2 , :)
|
||
plot22[] = view(logitLog, 3 , :)
|
||
plot23[] = view(logitLog, 4 , :)
|
||
plot24[] = view(logitLog, 5 , :)
|
||
plot25[] = view(logitLog, 6 , :)
|
||
plot26[] = view(logitLog, 7 , :)
|
||
plot27[] = view(logitLog, 8 , :)
|
||
plot28[] = view(logitLog, 9 , :)
|
||
plot29[] = view(logitLog, 10, :)
|
||
|
||
plot30[] = view(var1, 1 , :)
|
||
plot31[] = view(var1, 2 , :)
|
||
plot32[] = view(var1, 3 , :)
|
||
plot33[] = view(var1, 4 , :)
|
||
plot34[] = view(var1, 5 , :)
|
||
plot35[] = view(var1, 6 , :)
|
||
plot36[] = view(var1, 7 , :)
|
||
plot37[] = view(var1, 8 , :)
|
||
plot38[] = view(var1, 9 , :)
|
||
plot39[] = view(var1, 10, :)
|
||
|
||
plot40[] = view(var2, 1 , :)
|
||
plot41[] = view(var2, 2 , :)
|
||
plot42[] = view(var2, 3 , :)
|
||
plot43[] = view(var2, 4 , :)
|
||
plot44[] = view(var2, 5 , :)
|
||
plot45[] = view(var2, 6 , :)
|
||
plot46[] = view(var2, 7 , :)
|
||
plot47[] = view(var2, 8 , :)
|
||
plot48[] = view(var2, 9 , :)
|
||
plot49[] = view(var2, 10, :)
|
||
|
||
plot50[] = view(var3, 1 , :)
|
||
plot51[] = view(var3, 2 , :)
|
||
plot52[] = view(var3, 3 , :)
|
||
plot53[] = view(var3, 4 , :)
|
||
plot54[] = view(var3, 5 , :)
|
||
plot55[] = view(var3, 6 , :)
|
||
plot56[] = view(var3, 7 , :)
|
||
plot57[] = view(var3, 8 , :)
|
||
plot58[] = view(var3, 9 , :)
|
||
plot59[] = view(var3, 10, :)
|
||
|
||
plot60[] = view(var4, 1 , :)
|
||
plot61[] = view(var4, 2 , :)
|
||
plot62[] = view(var4, 3 , :)
|
||
plot63[] = view(var4, 4 , :)
|
||
plot64[] = view(var4, 5 , :)
|
||
plot65[] = view(var4, 6 , :)
|
||
plot66[] = view(var4, 7 , :)
|
||
plot67[] = view(var4, 8 , :)
|
||
plot68[] = view(var4, 9 , :)
|
||
plot69[] = view(var4, 10, :)
|
||
end
|
||
# end-thinkingPeriod+2; +2 because initialize logitLog = zeros(10, 2)
|
||
# _modelRespond = logitLog[:, end-thinkingPeriod+2:end] # answer count during thinking period
|
||
# _modelRespond = [sum(i) for i in eachrow(_modelRespond)]
|
||
# modelRespond = isequal.(isequal.(_modelRespond, 0), 0)
|
||
|
||
display(fig1)
|
||
# sleep(1)
|
||
if k % 3 == 0
|
||
firedNeurons_t1 = zeros(1)
|
||
logitLog = zeros(10, 2)
|
||
var1 = zeros(10, 2)
|
||
var2 = zeros(10, 2)
|
||
var3 = zeros(10, 2)
|
||
var4 = zeros(10, 2)
|
||
end
|
||
|
||
# if predict == OneHotArrays.onehot(label, labelDict)
|
||
# println("model train $label successfully, $k tries")
|
||
# # wait(display(fig1))
|
||
|
||
# firedNeurons_t1 = zeros(1)
|
||
# logitLog = zeros(10, 2)
|
||
# var1 = zeros(10, 2)
|
||
# var2 = zeros(10, 2)
|
||
# var3 = zeros(10, 2)
|
||
# var4 = zeros(10, 2)
|
||
# break
|
||
# end
|
||
|
||
if k == maxRepeatRound
|
||
# println("model train $label unsuccessfully, $maxRepeatRound tries, skip training")
|
||
# display(fig1)
|
||
firedNeurons_t1 = zeros(1)
|
||
logitLog = zeros(10, 2)
|
||
var1 = zeros(10, 2)
|
||
var2 = zeros(10, 2)
|
||
var3 = zeros(10, 2)
|
||
var4 = zeros(10, 2)
|
||
break
|
||
end
|
||
|
||
GC.gc()
|
||
end
|
||
end
|
||
# check accuracy
|
||
println("validating model")
|
||
answerCorrectly = validate(model, validateData, labelDict)
|
||
println("model accuracy is $answerCorrectly %")
|
||
end
|
||
|
||
# # check mean error and accuracy
|
||
# mean_error = round(mean(model_error_list), sigdigits = 3)
|
||
# accuracy = round(model_accuracy / batch_size * 100, sigdigits = 3)
|
||
# println("------------")
|
||
# println(model_name)
|
||
# println("mean error $mean_error accuracy $accuracy")
|
||
end
|
||
end
|
||
|
||
function validate(model, dataset, labelDict)
|
||
answerCorrectly = 0.0 # %
|
||
thinkingPeriod = 16 # 1000-784 = 216
|
||
@showprogress for (image, label) in dataset
|
||
img = reshape(image, (:, 1))
|
||
row, col = size(img)
|
||
label = label[1]
|
||
|
||
img_tensor = torch.from_numpy( np.asarray(img) )
|
||
|
||
# create more data for RSNN
|
||
spike = spikegen.delta(img_tensor, threshold=0.1, off_spike=true)
|
||
spike1 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike2 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike3 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike4 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike5 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike6 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike7 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike8 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike9 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike10 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
|
||
spike = spikegen.delta(img_tensor, threshold=0.2, off_spike=true)
|
||
spike11 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike12 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike13 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike14 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike15 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike16 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike17 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike18 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike19 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike20 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
|
||
spike = spikegen.delta(img_tensor, threshold=0.3, off_spike=true)
|
||
spike21 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike22 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike23 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike24 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike25 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike26 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike27 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike28 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike29 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike30 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
|
||
spike = spikegen.delta(img_tensor, threshold=0.4, off_spike=true)
|
||
spike31 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike32 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike33 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike34 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike35 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike36 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike37 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike38 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike39 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike40 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
|
||
spike = spikegen.delta(img_tensor, threshold=0.5, off_spike=true)
|
||
spike41 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike42 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike43 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike44 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike45 = isequal.(pyconvert(Array, spike.data.numpy()), 1)
|
||
spike46 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike47 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike48 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike49 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
spike50 = isequal.(pyconvert(Array, spike.data.numpy()), -1)
|
||
|
||
input = [spike1;; spike2;; spike3;; spike4;; spike5;; spike6;; spike7;; spike8;; spike9;; spike10;;
|
||
spike11;; spike12;; spike13;; spike14;; spike15;; spike16;; spike17;; spike18;; spike19;; spike20;;
|
||
spike21;; spike22;; spike23;; spike24;; spike25;; spike26;; spike27;; spike28;; spike29;; spike30;;
|
||
spike31;; spike32;; spike33;; spike34;; spike35;; spike36;; spike37;; spike38;; spike39;; spike40;;
|
||
spike41;; spike42;; spike43;; spike44;; spike45;; spike46;; spike47;; spike48;; spike49;; spike50
|
||
]' # ' to flip 784x10 to 10x784
|
||
|
||
# insert data into model sequencially
|
||
logit = Float64[]
|
||
for i in 1:(row + thinkingPeriod) # sMNIST ihas 784 timestep(pixel) + thinking period = 1000 timestep
|
||
if i <= row
|
||
current_pixel = input[:, i]
|
||
else
|
||
current_pixel = zeros(size(input)[1]) # dummy input in "thinking" period
|
||
end
|
||
|
||
_firedNeurons_t1, logit, _var1, _var2, _var3, _var4 = model(current_pixel)
|
||
end
|
||
|
||
predict = findall(isequal.(logit, maximum(logit)))[1] - 1
|
||
if predict == label
|
||
answerCorrectly += 1
|
||
# println("model answer $label correctly")
|
||
else
|
||
# println("img $label, model answer $predict")
|
||
end
|
||
GC.gc()
|
||
end
|
||
|
||
correctPercent = answerCorrectly * 100.0 / length(dataset)
|
||
|
||
return correctPercent::Float64
|
||
end
|
||
|
||
|
||
function main()
|
||
training_start_time = Dates.now()
|
||
println("program started ", training_start_time)
|
||
|
||
filelocation = string(@__DIR__)
|
||
|
||
# generate SNN
|
||
for i = 1:1
|
||
modelname = "v06_36"
|
||
filename = "$modelname.jl163"
|
||
generate_snn(filename, filelocation)
|
||
end
|
||
|
||
modelname = "v06_36"
|
||
filename = "$modelname.jl163"
|
||
# filename = "v06_31c.jl163"
|
||
|
||
trainDataset, validateDataset, labelDict = data_loader()
|
||
|
||
train_snn(modelname, filename, filelocation, trainDataset, validateDataset, labelDict)
|
||
|
||
finish_training_time = Dates.now()
|
||
println("training done, $training_start_time ==> $finish_training_time ")
|
||
println(" ///////////////////////////////////////////////////////////////////////")
|
||
end
|
||
|
||
# only runs main() if julia isn’t started interactively
|
||
# https://discourse.julialang.org/t/scripting-like-a-julian/50707
|
||
!isinteractive() && main()
|
||
#------------------------------------------------------------------------------------------------100
|
||
|
||
|
||
|
||
|
||
|
||
|