add zit_cumulative

src/forward.jl

@@ -18,6 +18,8 @@ function (kfn::kfn_1)(input::AbstractArray)
     # what to do at the start of learning round
     if view(kfn.learningStage, 1)[1] == 1
         # reset learning params
+        kfn.zit_cumulative .= 0
+
         kfn.lif_vt .= 0
         kfn.lif_wRecChange .= 0
         kfn.lif_epsilonRec .= 0
@@ -109,6 +111,7 @@ function (kfn::kfn_1)(input::AbstractArray)
                 reshape(kfn.lif_zt, (size(input, 1), :, 1, size(input, 3))), 
                 reshape(kfn.alif_zt, (size(input, 1), :, 1, size(input, 3))), dims=2)
     kfn.zit .= reshape(_zit, (size(input, 1), :, size(input, 3)))
+    kfn.zit_cumulative .+= kfn.zit
     
     # project 3D kfn zit into 4D on zit
     i1, i2, i3, i4 = size(kfn.on_zit)

src/type.jl

@@ -23,6 +23,7 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
     learningStage::Union{AbstractArray, Nothing} = nothing  # 0 inference, 1 start, 2 during, 3 end learning
     inputSize::Union{AbstractArray, Nothing} = nothing
     zit::Union{AbstractArray, Nothing} = nothing     # 3D activation matrix
+    zit_cumulative::Union{AbstractArray, Nothing} = nothing
     modelError::Union{AbstractArray, Nothing} = nothing     # store RSNN error
     outputError::Union{AbstractArray, Nothing} = nothing    # store output neurons error
 
@@ -175,6 +176,7 @@ function kfn_1(params::Dict; device=cpu)
 
     # activation matrix
     kfn.zit = zeros(row, col, batch)        |> device
+    kfn.zit_cumulative = (similar(kfn.zit) .= 0)                        |> device
     kfn.modelError = zeros(1)     |> device
 
     # ---------------------------------------------------------------------------- #