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Hng dn s dng phn mm Neural Network Spice-MLP 2013-07-11
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HNG DN S DNG PHN MM MNG N RON SPICE-MLP
Cao Thng 2003 2007
Cp nht 2011 Jan.
1. GII THIU
Mc ch chnh ca ti liu ny l hng dn bn c s dng
phn mm Spice-MLP, v khng i su vo l thuyt mng n
ron. Nu mun tm hiu thm v l thuyt, cc bn c th tham
kho cc ti liu v mng n ron c sn trn internet hoc sch
chuyn ngnh.
Mc ch ca cc phn mm Spice-MLP v SpiceSOM l gip
bn bit cch s dng mng n ron mt cch c bn, nhanh chng
v hiu qu m khng phi c nhiu v l thuyt mng n ron.
Khi bn hiu r tng chc nng ca cc phm mm ny, bn
c th d dng tip cn vi l thuyt cng nh cc ti liu tham
kho v mng n ron.
Spice-MLP l phn mm mng n ron 3 lp, vi nhiu u vo v
nhiu u ra. Spice-MLP c vit vi mc ch hng dn sinh
vin v nghin cu sinh hc tp v s dng mng n ron m
hnh ha nhiu loi d liu khc nhau. Hin ti Spice-MLP ang
c nhiu bn trn th gii s dng. Spice-MLP c giao din vi
ting Vit, ting Anh v ting Nht.
nm
l
ijw jkw
1x
ix
nx
1y
ky
ly
LAYER INPUT LAYER HIDDEN LAYER OUTPUT
1 1 1
ij
k
Weight_IJ Weight_JK
Bias
Bias
00 JIw 00 KJw
0_ JTeta
0_ KTeta
00

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Ty thuc vo phin bn Spice-MLP m bn ang s dng, mt
s hng dn hoc giao din minh ha trong ti liu ny c th
khc vi thc t.
Spice-MLP c vit bi CAO THANG khi tc gi lm vic ti Soft Intelligence Laboratory,
Ritsumeikan University, Japan, 2003-2007 v thng xuyn c cp nht theo yu cu ca
ngi s dng
SpiceSOM v Spice-MLP c th download c ti: download.cnet.com
hiu hn v mng n ron vi cc ng dng nh nhn dng khun mt, ngi i b, d bo
chng khon, t gi..., cc bn nn c thm ti liu Mt s v d phn loi dng SOM v MLP
Neural Network (neural_network_practical_use_vi.pdf).
Nu c thc mc hoc cn yu cu thm v chc nng ca Spice-MLP, bn c th lin h vi tc
gi ti http://spiceneuro.wordpress.com hoc spiceneuro AT gmail DOT com. Cm n cc bn.
2. CI T SPICE-MLP
Download file ci t ca Spice-MLP v chy setup.exe, trn mn hnh hin ra:
Hnh 1. Ci t
Chn Next, sau chn th mc m bn mun ci t Spice-MLP vo, chn Next v Next tip.
Spice-MLP s c ci vo th mc m bn chn.

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Hnh 2. Chn Folder ci vo
Lu :
Vi Windows version c, Nu sau khi ci t, chng trnh khng chy, c th bn cn
ci Microsoft .NET Framework Redistributable Package 3.5.21022 trc khi ci Spice-
MLP.
Nu d liu ca bn dng di dng MDB format, c th bn cn ci thm Microsoft
Data Access Components.
3. S DNG SPICE-MLP
Chy Spice-MLP bng cch click vo biu tng Spice-MLP trn desktop hoc trong Start
Programs Cao Thangs Spice-MLP Spice-MLP.
Trn mn hnh hin ra giao din ting Anh, bn c th chn ngn ng ting Vit hoc ting Nht
bng cch chn Options Languages.
Hnh 3. Chn ngn ng
Trong cc giao din minh ha di y, ngn ng c s dng l ting Vit.

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Menu V chng trnh. Hy c trc khi s dng l gii thiu vn tt v Spice-MLP v s
ng ca ngi s dng. Bn cn c k trc khi s dng Spice-MLP.
Hnh 4. V chng trnh, nh trong chp nm 2007
3.1. Chun b d liu
Spice-MLP c c d liu ca bn, bn cn chun b d liu ca mnh theo chun sau.
3.1.1. D liu dng file text
D liu dng file text cn c chun b thnh cc hng v ct. u tin l ID, sau l u vo
v tip theo l u ra. Cc gi tr c phn cch bng du phy vi file CSV (Comma
Separated Value File Format) hoc du Tab vi file TXT (Tab Separated Value File Format).
Bn c th dng MS Excel bin son d liu, sau lu vo file file text hoc file csv. V d
d liu vi 2 u vo v 3 u ra c t chc nh bng 1.

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Bng 1. D liu text vi 2 u vo v 3 u ra
Lu :
D liu phi l dng s (ngoi tr nhn (label) v cc k hiu u vo, u ra).
Nu c d liu trng hoc null, Spice-MLP s khng c c. Trong d liu kim tra, nu
khng c gi tr u ra, cc bn cn t gi tr ra l 0 hoc 1, hoc mt gi tr s no .
Mt s v d v d liu c t trong th mc \Data ca Spice-MLP:
Boolean functions.csv l v d vi 4 datasets, 2 inputs v 3 outputs. u vo l cc gi
tr nh phn 0 v 1, u ra l gi tr ca cc hm XOR (Y0), AND (Y1) v OR(Y2) .
Herbal data.csv l v d vi 640 datasets, 16 inputs v 33 outputs. u vo l mc
triu chng c gi tr t 0 ti 1, u ra l h s cc v thuc c chun ha trong [0,
1].
sincos.csv l v d vi 100 datasets, 1 inputs v 2 outputs. u vo l i s c gi tr
t 0 ti 2, c chun ha trong [0,1] v u ra l gi tr hai hm Sin v Cos ca i s
u vo .
iris_for_mlp_4inputs_1output.csv, iris_for_mlp_4inputs_3outputs.csv l d liu ca
ba loi hoa (Iris setosa, Iris virginica v Iris versicolor), mi loi 50 mu. Cc thuc tnh
l di v rng ca i hoa (sepal) v cnh hoa (petal) tnh theo centimeters. Chi tit ti
http://archive.ics.uci.edu/ml/datasets/Iris. iris_for_mlp_4inputs_1output.csv l d liu
4 u vo 1 u ra, iris_for_mlp_4inputs_3outputs.csv l d liu 3 u vo 1 u ra.
"CAD_USD_JPN.csv", "CAD_USD_JPN_Normalized.csv", 2489 datasets v t gi
CAD USD, CAD JPN vi 30 u vo, 2 u ra.
ID X0 X1 Y0 Y1 Y2 LABEL
0 0 0 0 0 0 Data 1
1 0 1 1 0 1 Data 2
2 1 0 1 0 1 Data 3
3 1 1 0 1 1 Data 4
ID: th t
DataSet
X: u vo
(Input Data)
Y: u ra
(Output Data)
LABEL: Nhn ca
tng DataSet

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NASDAQ_5026_data_15inputs_1output.csv l 5026 datasets v ch s chng khon
NASDAQ, vi 15 u vo v 1 u ra.
Hoabinh_water_level_3_input_1_output.csv l d liu v d bo lu lng nc tng
lai trc 10 ngy Q(t+10) ca h Ha Bnh da vo cc lu lng nc ti thi im
hin ti v qu kh. D liu c 3 u vo gm lu lng nc hin ti Q(t), lu lng
nc trc 10 ngy Q(t-10) v lu lng nc trc 20 ngy Q(t-20). S d liu
l 480 mu hc (t 1 ti 480) v 90 mu kim tra (t 481 ti 570). D liu ny do bn
Phm Th Hong Nhung, trng H Thy li cung cp, bn c c th tham kho lun
vn Master ca Phm Th Hong Nhung (1997) v " kho st mt s phng php hc
my tin tin, thc hin vic kt hp gia phng php hc my mng neuron vi thut
ton gene v ng dng vo bi ton d bo lu lng nc n h Ha Bnh". Xin cm
n bn Phm Th Hong Nhung cho php s dng d liu lu lng nc h Ha Bnh
minh ha trong ti liu ny.
3.2. Load d liu
Gi s chng ta dng d liu l file sincos.txt l v d vi 100 datasets, 1 u vo v 2 u ra
ni trn. Trong mc S Neuron v D liu Ta chn nh hnh 5:
Hnh 5. Chn tham s load d liu
Chn nt Ti t file text, d liu s c ti vo b nh. Trong mc XEM D LIU bn
phi, bn c th xem li tng dataset ca d liu m bn va load:

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3.3. Chun ha d liu
Nu d liu ca bn cha c chun ha, bn c th dng chc nng chun ha d liu nh
hnh 7 sau. Bn c th chun ha d liu u vo hoc u ra, hoc c hai:
Hnh 6. Xem d liu
Thanh chn
d liu
u vo
u ra (dng th)
u ra ca d liu hc v u ra ca mng
c biu th bng hai mu khc nhau
u vo
(dng bng)
u ra ca d liu
hc (dng bng)
u ra ca mng
(dng bng)
Hnh 7. Chun ha d liu
Hm chun ha La chn chun ha d
liu vo hoc d liu ra

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3.4. o to mng.
3.4.1. Chia d liu
Nu bn mun chia d liu lm hai phn, mt phn hc v mt phn kim tra, bn dng
chc nng Chia d liu. Sau bn cn chn phn d liu no hc. V d sau minh ha
vic chia d liu ngu nhin thnh hai phn 70% v 30%, dng 70% hc v 30% kim tra.
3.4.2. Chn d liu hc v cc tham s
Tip theo, bn cn chn s nron cho lp n (hidden layer), s ln lp, thi gian hc v MSE
(Mean of Square Error) yu cu. Bn cng c th la chn hc thch nghi (t l hc bin i da
vo MSE hc.
Hm bin i: bn cn chn hm bin i (Activated Functions) cho lp n v lp ra. Spice-
MLP cung cp cho bn nhiu la chn. Nu l ngi mi bt u nghin cu v NN, bn nn
chn cc hm Sigmoid, HyperTanh, Tanh, ArcTan, ArcSinh.
Th t u vo trong khi hc: bn c th chn vo ngu nhin hoc vo tun t.
Hnh 8. Chia d liu
Hnh 9. Chn d liu hc v cc tham s

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Hin ti Spice-MLP dng 27 hm bin i. Cng thc v th ca mt s hm i c minh
ha bng 2 sau.
Sigmoid
HyperTanh
Tanh
ArcTan
Hnh 10. Chn hm bin i (Activated Functions) cho lp n v lp ra

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ArcSinh
Sin
Linear
Cos
Exp(-x)
Exp(-x*x)

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x*x*x
InvertAbs
3.4.3. o to mng (training)
Sau khi chn s nron cho lp n, chn t l d liu hc v cc thng s cn thit, bn c th bt
u o to mng. Sau y l cc nt lnh chnh o to.
Khi to trng s ban u: khi to (Reset) li trng s ban u cho cc nt mng.
o to: o to mng.
Load trng s mng t File nh phn: ti trng s mng t file nh phn c sn. Lu ,
nu thng s mng t file nh phn c sn khc vi thng s mng hin ti ca bn, c
th chng trnh s bo li hoc a ra kt qu sai.
Lu trng s mng vo File nh phn: lu trng s mng hin thi vo file nh phn, mi
gi tr s c ghi vi di 4 bytes. Th t nh sau:
Weight_IJ: I0J0, I0J1,
Weight_TetaJ: Teta_J0, Teta_J1,
Weight_JK: J0K0, J0K1,
Weight_Te

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