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Training_lr0.0001.log
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Training_lr0.0001.log
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INFO : class_names = ['Etat', 'Inland', 'International', 'Kultur', 'Panorama', 'Sport', 'Web', 'Wirtschaft', 'Wissenschaft']
INFO : ****** Current epoch: 1 ******
INFO : Training sample: 1656/ 8280 ...
INFO : Training sample: 3312/ 8280 ...
INFO : Training sample: 4968/ 8280 ...
INFO : Training sample: 6624/ 8280 ...
INFO : Training sample: 8280/ 8280 ...
INFO : Sample: 193/ 965 ...
INFO : Sample: 386/ 965 ...
INFO : Sample: 579/ 965 ...
INFO : Sample: 772/ 965 ...
INFO : Sample: 965/ 965 ...
INFO : NLL loss=1.18 ; accuracy=0.616
INFO : precision=[0.7, 0.36, 0.61, 0.0, 0.6, 0.74, 0.74, 0.58, 0.83]
Recall=[0.21, 0.58, 0.68, 0.0, 0.65, 0.8, 0.85, 0.6, 0.52]
INFO : F1_score=[0.32, 0.45, 0.64, 0.0, 0.62, 0.77, 0.79, 0.59, 0.64]
INFO : confusion_matrix=
[[ 14 12 9 0 9 7 7 9 0]
[ 2 50 2 0 21 1 1 9 0]
[ 1 2 95 0 20 2 8 12 0]
[ 1 19 4 0 16 9 3 0 1]
[ 0 25 25 0 122 4 4 8 1]
[ 1 4 7 0 4 80 1 3 0]
[ 1 1 4 0 4 1 132 9 3]
[ 0 20 6 0 6 2 18 77 0]
[ 0 5 4 0 2 2 4 5 24]]
INFO : validation_loss=1.178 ; best_validation_loss=inf
INFO : ****** Current epoch: 2 ******
INFO : Training sample: 1656/ 8280 ...
INFO : Training sample: 3312/ 8280 ...
INFO : Training sample: 4968/ 8280 ...
INFO : Training sample: 6624/ 8280 ...
INFO : Training sample: 8280/ 8280 ...
INFO : Sample: 193/ 965 ...
INFO : Sample: 386/ 965 ...
INFO : Sample: 579/ 965 ...
INFO : Sample: 772/ 965 ...
INFO : Sample: 965/ 965 ...
INFO : NLL loss=0.91 ; accuracy=0.71
INFO : precision=[0.77, 0.54, 0.75, 0.5, 0.67, 0.88, 0.81, 0.68, 0.74]
Recall=[0.4, 0.67, 0.76, 0.55, 0.69, 0.88, 0.85, 0.67, 0.63]
INFO : F1_score=[0.53, 0.6, 0.76, 0.52, 0.68, 0.88, 0.83, 0.67, 0.68]
INFO : confusion_matrix=
[[ 27 7 6 12 5 2 4 4 0]
[ 2 58 1 2 14 0 1 6 2]
[ 1 2 107 2 16 1 5 5 1]
[ 1 4 1 29 9 5 1 1 2]
[ 1 23 17 4 130 3 1 9 1]
[ 1 1 3 2 3 88 1 1 0]
[ 1 1 3 1 5 0 131 10 3]
[ 0 11 4 4 9 0 14 86 1]
[ 1 1 1 2 4 1 3 4 29]]
INFO : validation_loss=0.914 ; best_validation_loss=1.178
INFO : ****** Current epoch: 3 ******
INFO : Training sample: 1656/ 8280 ...
INFO : Training sample: 3312/ 8280 ...
INFO : Training sample: 4968/ 8280 ...
INFO : Training sample: 6624/ 8280 ...
INFO : Training sample: 8280/ 8280 ...
INFO : Sample: 193/ 965 ...
INFO : Sample: 386/ 965 ...
INFO : Sample: 579/ 965 ...
INFO : Sample: 772/ 965 ...
INFO : Sample: 965/ 965 ...
INFO : NLL loss=0.78 ; accuracy=0.752
INFO : precision=[0.83, 0.58, 0.78, 0.55, 0.73, 0.97, 0.82, 0.73, 0.77]
Recall=[0.52, 0.76, 0.8, 0.68, 0.72, 0.9, 0.85, 0.67, 0.74]
INFO : F1_score=[0.64, 0.66, 0.79, 0.61, 0.73, 0.93, 0.83, 0.7, 0.76]
INFO : confusion_matrix=
[[ 35 7 5 9 3 2 3 3 0]
[ 0 65 1 2 8 0 1 7 2]
[ 1 2 112 3 15 0 4 3 0]
[ 2 3 0 36 8 1 0 1 2]
[ 1 21 16 6 137 0 1 5 2]
[ 1 0 2 2 3 90 1 1 0]
[ 1 1 3 2 5 0 131 9 3]
[ 0 13 4 4 6 0 15 86 1]
[ 1 0 0 2 3 0 3 3 34]]
INFO : validation_loss=0.781 ; best_validation_loss=0.914
INFO : ****** Current epoch: 4 ******
INFO : Training sample: 1656/ 8280 ...
INFO : Training sample: 3312/ 8280 ...
INFO : Training sample: 4968/ 8280 ...
INFO : Training sample: 6624/ 8280 ...
INFO : Training sample: 8280/ 8280 ...
INFO : Sample: 193/ 965 ...
INFO : Sample: 386/ 965 ...
INFO : Sample: 579/ 965 ...
INFO : Sample: 772/ 965 ...
INFO : Sample: 965/ 965 ...
INFO : NLL loss=0.71 ; accuracy=0.778
INFO : precision=[0.81, 0.61, 0.82, 0.56, 0.76, 0.97, 0.84, 0.79, 0.78]
Recall=[0.58, 0.76, 0.83, 0.7, 0.78, 0.89, 0.86, 0.68, 0.76]
INFO : F1_score=[0.68, 0.68, 0.82, 0.62, 0.77, 0.93, 0.85, 0.73, 0.77]
INFO : confusion_matrix=
[[ 39 7 5 9 3 1 1 2 0]
[ 0 65 1 2 8 0 1 7 2]
[ 1 2 116 3 12 0 4 2 0]
[ 2 4 0 37 7 1 0 1 1]
[ 2 15 13 5 148 0 1 2 3]
[ 1 0 2 2 4 89 1 1 0]
[ 2 1 2 3 3 0 134 7 3]
[ 0 12 3 3 7 1 14 88 1]
[ 1 0 0 2 3 0 3 2 35]]
INFO : validation_loss=0.705 ; best_validation_loss=0.781
INFO : ****** Current epoch: 5 ******
INFO : Training sample: 1656/ 8280 ...
INFO : Training sample: 3312/ 8280 ...
INFO : Training sample: 4968/ 8280 ...
INFO : Training sample: 6624/ 8280 ...
INFO : Training sample: 8280/ 8280 ...
INFO : Sample: 193/ 965 ...
INFO : Sample: 386/ 965 ...
INFO : Sample: 579/ 965 ...
INFO : Sample: 772/ 965 ...
INFO : Sample: 965/ 965 ...
INFO : NLL loss=0.67 ; accuracy=0.785
INFO : precision=[0.76, 0.61, 0.81, 0.55, 0.78, 0.95, 0.86, 0.82, 0.8]
Recall=[0.63, 0.73, 0.83, 0.68, 0.77, 0.89, 0.88, 0.72, 0.8]
INFO : F1_score=[0.69, 0.67, 0.82, 0.61, 0.77, 0.92, 0.87, 0.77, 0.8]
INFO : confusion_matrix=
[[ 42 5 5 7 3 2 1 2 0]
[ 2 63 1 2 8 0 1 7 2]
[ 2 2 116 3 11 0 4 2 0]
[ 3 4 1 36 5 2 0 1 1]
[ 3 15 14 5 145 0 1 3 3]
[ 1 0 2 2 4 89 2 0 0]
[ 2 1 2 4 2 0 137 5 2]
[ 0 12 3 3 6 1 10 93 1]
[ 0 1 0 3 2 0 3 0 37]]
INFO : validation_loss=0.667 ; best_validation_loss=0.705
INFO : ****** Current epoch: 6 ******
INFO : Training sample: 1656/ 8280 ...
INFO : Training sample: 3312/ 8280 ...
INFO : Training sample: 4968/ 8280 ...
INFO : Training sample: 6624/ 8280 ...
INFO : Training sample: 8280/ 8280 ...
INFO : Sample: 193/ 965 ...
INFO : Sample: 386/ 965 ...
INFO : Sample: 579/ 965 ...
INFO : Sample: 772/ 965 ...
INFO : Sample: 965/ 965 ...
INFO : NLL loss=0.65 ; accuracy=0.791
INFO : precision=[0.75, 0.62, 0.83, 0.57, 0.79, 0.94, 0.87, 0.81, 0.79]
Recall=[0.67, 0.73, 0.83, 0.68, 0.78, 0.89, 0.87, 0.72, 0.83]
INFO : F1_score=[0.71, 0.67, 0.83, 0.62, 0.79, 0.91, 0.87, 0.76, 0.81]
INFO : confusion_matrix=
[[ 45 4 5 6 2 2 1 2 0]
[ 3 63 1 2 8 0 0 6 3]
[ 2 2 116 3 11 0 3 3 0]
[ 3 4 1 36 4 3 0 1 1]
[ 3 15 10 5 148 0 2 3 3]
[ 1 0 1 2 4 89 2 1 0]
[ 2 2 2 4 2 0 135 6 2]
[ 1 11 3 2 6 1 11 93 1]
[ 0 1 0 3 2 0 2 0 38]]
INFO : validation_loss=0.65 ; best_validation_loss=0.667
INFO : ****** Current epoch: 7 ******
INFO : Training sample: 1656/ 8280 ...
INFO : Training sample: 3312/ 8280 ...
INFO : Training sample: 4968/ 8280 ...
INFO : Training sample: 6624/ 8280 ...
INFO : Training sample: 8280/ 8280 ...
INFO : Sample: 193/ 965 ...
INFO : Sample: 386/ 965 ...
INFO : Sample: 579/ 965 ...
INFO : Sample: 772/ 965 ...
INFO : Sample: 965/ 965 ...
INFO : NLL loss=0.66 ; accuracy=0.799
INFO : precision=[0.73, 0.65, 0.83, 0.6, 0.79, 0.94, 0.88, 0.81, 0.8]
Recall=[0.67, 0.74, 0.83, 0.68, 0.79, 0.9, 0.87, 0.77, 0.8]
INFO : F1_score=[0.7, 0.7, 0.83, 0.64, 0.79, 0.92, 0.87, 0.79, 0.8]
INFO : confusion_matrix=
[[ 45 4 5 6 2 2 1 2 0]
[ 3 64 1 2 8 0 0 5 3]
[ 2 2 116 3 11 0 2 4 0]
[ 4 3 1 36 4 3 0 1 1]
[ 4 12 11 4 149 0 3 3 3]
[ 1 0 1 1 4 90 2 1 0]
[ 2 2 2 3 2 0 135 7 2]
[ 1 10 2 2 5 1 9 99 0]
[ 0 1 0 3 3 0 2 0 37]]
INFO : validation_loss=0.657 ; best_validation_loss=0.65
INFO : Early stopping