run log¶
runfile('C:/Users/NWPU/Pictures/Work Repository/Elman Neural Network/ElmanNN_NumPy.py', wdir='C:/Users/NWPU/Pictures/Work Repository/Elman Neural Network', post_mortem=True)
Iterations = 100001Cost:7.17764788768309e-08
[[ 3.96519093 0.59930827 -3.33208209 4.89577237 0.01972838 -3.65786839]
[-3.56524449 0.70084172 -2.48942562 2.61544921 0.34699735 3.47130228]
[-0.81548563 0.6146014 2.96881885 -2.14980695 0.71953898 0.85605029]]
[[-10.98211539 -14.18740974 6.72372159]
[ -3.11319394 -2.31327809 -3.92956636]
[-15.73459694 14.29975559 -0.80954778]
[ 10.56618043 -16.57160697 -6.4710749 ]
[ -3.01466166 -2.13154558 -3.83872816]
[ 6.54177363 12.24881412 -11.42449926]]
[[ 0.5028387 ]
[-1.03744111]
[-0.61196499]]
[[ 0.88324799]
[ -5.87771622]
[ -6.43653342]
[ -1.63319756]
[ -6.01968921]
[-11.18115085]]
train 12.474156379699707
runfile('C:/Users/NWPU/Pictures/Work Repository/Elman Neural Network/ElmanNN_NumPy.py', wdir='C:/Users/NWPU/Pictures/Work Repository/Elman Neural Network', post_mortem=True)
Iterations = 1000001Cost:2.551063864544286e-10
[[-5.43675307 0.00678236 2.58566288 1.85330734 0.99421473 2.77622797]
[ 2.24505024 0.27475652 -2.86945232 -2.9664289 0.85236791 4.81502055]
[ 4.84581852 0.0700831 -4.59436203 5.29796706 0.23592358 -3.2271536 ]]
[[ 5.24731516 -19.39144049 -19.57999375]
[ -4.76198745 -2.69300842 -3.04016222]
[ 5.14826426 19.21405311 -14.91027451]
[-16.49384263 5.89615742 5.95535063]
[ -5.14644103 -2.81359287 -3.21994391]
[ 2.83729084 -16.6693786 19.19882579]]
[[ 0.40946385]
[-0.1641545 ]
[ 0.58363984]]
[[ 4.21434767]
[ -7.12653066]
[-14.67144194]
[ -0.66630909]
[ -6.76103256]
[-12.42210168]]
train 122.093510389328
runfile('C:/Users/NWPU/Pictures/Work Repository/Elman Neural Network/ElmanNN_NumPy.py', wdir='C:/Users/NWPU/Pictures/Work Repository/Elman Neural Network', post_mortem=True)
Iterations = 1000001Cost:3.144754377494503e-10
[[ 1.00754762 0.56777365 5.53291787 -2.59183096 0.19856753 -2.68338576]
[ 3.45026908 0.71686063 -2.78901442 3.56924647 0.23518678 -3.77294235]
[-4.36010206 0.00829102 1.23894443 2.56766988 0.02410035 3.39138931]]
[[ 19.30976784 -18.51874461 -0.12791457]
[ -3.02304411 -2.8614991 -4.85641516]
[-17.52436421 -19.52045376 8.84002443]
[ 3.96645477 7.97279042 -17.98920646]
[ -2.82626067 -3.1137749 -4.9275717 ]
[-17.42965291 19.12728344 3.51225545]]
[[-0.62552336]
[ 0.09709697]
[ 0.44306044]]
[[ -9.65720729]
[ -7.02144003]
[ 0.62110249]
[ -1.13995077]
[ -6.96766644]
[-13.09346818]]
[[ 0.92707881 -4.54829784 1.62638093]
[ 3.9646403 0.0133975 -3.35478832]
[-1.27074082 3.1091682 -0.51417442]]
train 134.1071891784668