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