add multithreading

Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.9.0"
 manifest_format = "2.0"
-project_hash = "6da2bd801ebd94457c5a5cb36ae71250437066e8"
+project_hash = "b9e7ae4b78dc59a5adb629a04e856c4fedc6fb60"
 
 [[deps.AbstractFFTs]]
 deps = ["LinearAlgebra"]

Project.toml

@@ -7,6 +7,7 @@ version = "0.1.0"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
+JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

src/forward.jl

@@ -2,7 +2,7 @@ module forward
 
 using Flux.Optimise: apply!
 
-using Statistics, Flux, Random, LinearAlgebra
+using Statistics, Flux, Random, LinearAlgebra, JSON3
 using GeneralUtils
 using ..types, ..snn_utils
 
@@ -77,8 +77,8 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
 
     kfn.firedNeurons_t0 = [n.z_t for n in kfn.neuronsArray]    #TODO check if it is used?
 
-    #CHANGE Threads.@threads for n in kfn.neuronsArray
-    for n in kfn.neuronsArray
+    Threads.@threads for n in kfn.neuronsArray
+    # for n in kfn.neuronsArray
         n(kfn)
     end
 
@@ -88,8 +88,8 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
         kfn.firedNeurons |> unique!    # use for random new neuron connection
     end
 
-    # Threads.@threads for n in kfn.outputNeuronsArray
-    for n in kfn.outputNeuronsArray
+    Threads.@threads for n in kfn.outputNeuronsArray
+    # for n in kfn.outputNeuronsArray
         n(kfn)
     end
 

src/learn.jl

@@ -2,7 +2,7 @@ module learn
 
 using Flux.Optimise: apply!
 
-using Statistics, Flux, Random, LinearAlgebra
+using Statistics, Flux, Random, LinearAlgebra, JSON3
 using GeneralUtils
 using ..types, ..snn_utils
 
@@ -30,8 +30,8 @@ function learn!(kfn::kfn_1, correctAnswer::AbstractVector)
             kfnError = (kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError) * 
                         100 / kfn.outputNeuronsArray[i].v_th
 
-            # Threads.@threads for n in kfn.neuronsArray
-            for n in kfn.neuronsArray
+            Threads.@threads for n in kfn.neuronsArray
+            # for n in kfn.neuronsArray
                 learn!(n, kfnError)
             end
 
@@ -41,8 +41,8 @@ function learn!(kfn::kfn_1, correctAnswer::AbstractVector)
     
     # wrap up learning session
     if kfn.learningStage == "end_learning"
-        # Threads.@threads for n in kfn.neuronsArray
-        for n in kfn.neuronsArray
+        Threads.@threads for n in kfn.neuronsArray
+        # for n in kfn.neuronsArray
             if typeof(n) <: computeNeuron
                 wSign_0 = sign.(n.wRec) # original sign
                 n.wRec += n.wRecChange  # merge wRecChange into wRec