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CNN_lhogho.lgo
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CNN_lhogho.lgo
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load "Hair.lgo
load "euler.lgo
;之前的神经网络部分
to sigmoid :x
op 1/(1+exp -:x)
end
to map :l1 :l2
if empty? :l1 [op []]
op fput (first :l1)*(first :l2) map bf :l1 bf :l2
end
to neural :abvector :vector
op sigmoid ((sumlist (map bl :abvector :vector)) + (last :abvector))
end
to network :abvector :xy :xy2
if empty? :abvector [op reverse :xy2]
local "_xy
make "_xy []
repeat count first :abvector [
make "_xy fput neural item repcount first :abvector :xy :_xy
]
make "_xy reverse :_xy
op network bf :abvector :_xy fput :_xy :xy2
end
to abvector :layer
if empty? bl :layer [op []]
local "sum1
make "sum1 []
local "ji
make "ji (count last :layer)*(count last bl :layer)
make "ji rseq (0 - power :ji -0.5) (power :ji -0.5) 10000
repeat last :layer [
local "sum0
make "sum0 []
repeat 1+last bl :layer [
make "sum0 lput pick :ji :sum0]
make "sum1 lput :sum0 :sum1
]
op se abvector bl :layer (list :sum1)
end
;神经网络结束
to map2 :l
if empty? :l [op []]
op fput (first :l)/255 map2 bf :l
end
;卷积残差神经网络的logo实现
;以9*9五子棋AI为例
;输入为9*9*7（前后三步加当前走子方）
;使用3*3*7的16个卷积核
;一共进行1+12+1次卷积，一次全连接
;最后输出棋盘上走子的概率，以及当前局面的胜率
;全局卷积核权重参数blocklist[13 16]、权重参数blist[13 16]、全连接权重参数表alist
cnn hair.old.array [9 9 7] 1
to cnn :gobang_board
;初始化权重参数
;make "blocklist hair.old.array [13 16] hair.old.array [9 9 16] 1
make "blocklist hair.old.array [9 9 16] 1
make "blist hair.old.array [13 16] 0
make "alist abvector se 9*9*16 1
make "t hair.base.timemilli
;0
make "gobang_board layer dataempty :gobang_board 7 hair.old.array [11 11 7] [] 7 16 hair.old.array [9 9 16] [] 0
pr 1
;1-12
repeat 3 [
make "gobang_board2 :gobang_board
make "_rep repcount
repeat 4 [make "gobang_board2 layer dataempty :gobang_board2 16 hair.old.array [11 11 16] [] 16 16 hair.old.array [9 9 16] [] (:_rep-1)*3+repcount]
make "gobang_board dataadd :gobang_board :gobang_board2 16 hair.old.array [9 9 16] []
]
;13/14
make "gobang_board2 []
for "o [0 8] [
for "p [0 8] [
for "q [0 15] [
make "gobang_board2 fput hair.old.aget :gobang_board (se :o :p :q) :gobang_board2
]
]
]
make "gobang_board2 reverse :gobang_board2
pr network :alist :gobang_board2 []
pr layer dataempty :gobang_board 16 hair.old.array [11 11 16] [] 16 1 hair.old.array [9 9 1] [] 13
pr hair.base.timemilli-:t
end
;计算一次卷积
;data被卷积的数据3*3*16
;n告诉系统深度是16还是7
;block卷积核的各个权重
;b偏置
to convolution :data :block :b :n
pr :block
local "_data
make "_data 0
make "_nm :n-1
for "i [0 2] [
for "j [0 2] [
for "k [0 :_nm] [
make "_data :_data + (hair.old.aget :data (se :i :j :k))*(hair.old.aget :block (se :i :j :k))
]
]
]
op :_data+:b
end
;计算一层卷积
;需要提前给data数据周围增加一圈空值
;n表示数据的深度
;_n表示几个卷积核，在第一层、最后一层深度与核数不等，其余是相等的
to layer :data :n :_n :result :layerid
if :_n<1 [op :result]
make "_nm :n-1
for "o [0 8] [
for "p [0 8] [
make "wa hair.old.array se [3 3] :n []
for "q [0 :_nm] [
repeat 3 [
make "wa hair.old.aset :wa (se repcount-1 0 :q) hair.old.aget :data (se :o+repcount-1 :p :q)
make "wa hair.old.aset :wa (se repcount-1 1 :q) hair.old.aget :data (se :o+repcount-1 :p+1 :q)
make "wa hair.old.aset :wa (se repcount-1 2 :q) hair.old.aget :data (se :o+repcount-1 :p+2 :q)]
]
pr :blocklist
;make "result hair.old.aset :result (se :o :p :_n-1) convolution :wa hair.old.aget :blocklist se :layerid :_n-1 hair.old.aget :blist se :layerid :_n-1 :n
make "result hair.old.aset :result (se :o :p :_n-1) convolution :wa :blocklist hair.old.aget :blist se :layerid :_n-1 :n
]
]
op layer :data :n :_n-1 :result :layerid
end
;为data周围加空值
to dataempty :data :n :result
for "o [0 10] [
for "p [0 10] [
make "n2 :n-1
for "q [0 :n2] [
ifelse (or :o=0 :p=0 :o=10 :p=10) [
make "result hair.old.aset :result (se :o :p :q) 0
][
make "result hair.old.aset :result (se :o :p :q) hair.old.aget :data (se :o-1 :p-1 :q)
]
]
]
]
op :result
end
;将两个数组相加
;用于将卷积结果和残差合并
to dataadd :data1 :data2 :n :result
make "n2 :n-1
for "o [0 8] [
for "p [0 8] [
for "q [0 :n2] [
make "result hair.old.aset "result (se :o :p :q) (hair.old.aget :data1 (se :o :p :q))+(hair.old.aget :data2 (se :o :p :q))
]
]
]
op :result
end