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Training_lr5e-05.log
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Training_lr5e-05.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=1.78 ; accuracy=0.45
INFO : precision=[0.0, 0.34, 0.49, 0.0, 0.39, 0.94, 0.4, 0.54, 0.0]
Recall=[0.0, 0.51, 0.51, 0.0, 0.55, 0.66, 0.79, 0.2, 0.0]
INFO : F1_score=[0.0, 0.41, 0.5, 0.0, 0.46, 0.78, 0.53, 0.29, 0.0]
INFO : confusion_matrix=
[[ 0 11 9 0 24 1 20 2 0]
[ 0 44 2 0 28 0 10 2 0]
[ 0 6 72 0 26 0 28 8 0]
[ 0 3 4 0 23 0 23 0 0]
[ 0 28 27 0 104 1 27 2 0]
[ 0 0 9 0 12 66 12 1 0]
[ 0 4 9 0 13 1 122 6 0]
[ 0 11 14 0 29 1 48 26 0]
[ 0 23 2 0 5 0 15 1 0]]
INFO : validation_loss=1.778 ; 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=1.21 ; accuracy=0.608
INFO : precision=[0.73, 0.42, 0.54, 0.0, 0.54, 0.92, 0.72, 0.56, 0.74]
Recall=[0.24, 0.62, 0.68, 0.0, 0.58, 0.84, 0.81, 0.58, 0.61]
INFO : F1_score=[0.36, 0.5, 0.6, 0.0, 0.56, 0.88, 0.76, 0.57, 0.67]
INFO : confusion_matrix=
[[ 16 12 11 0 13 2 6 7 0]
[ 1 53 2 0 20 0 2 8 0]
[ 1 3 95 0 18 0 8 14 1]
[ 1 15 5 0 21 3 3 2 3]
[ 0 25 33 0 110 1 7 11 2]
[ 2 1 6 0 2 84 3 2 0]
[ 1 0 9 0 4 0 126 12 3]
[ 0 13 12 0 11 1 16 75 1]
[ 0 3 2 0 4 0 5 4 28]]
INFO : validation_loss=1.212 ; best_validation_loss=1.778
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=1.02 ; accuracy=0.678
INFO : precision=[0.74, 0.49, 0.69, 0.7, 0.65, 0.9, 0.75, 0.58, 0.74]
Recall=[0.34, 0.65, 0.76, 0.3, 0.66, 0.88, 0.79, 0.65, 0.74]
INFO : F1_score=[0.47, 0.56, 0.72, 0.42, 0.66, 0.89, 0.77, 0.61, 0.74]
INFO : confusion_matrix=
[[ 23 10 8 1 8 4 6 7 0]
[ 2 56 0 0 17 0 2 9 0]
[ 1 3 106 0 13 0 6 9 2]
[ 2 10 1 16 13 3 2 2 4]
[ 1 18 22 2 125 1 4 14 2]
[ 1 1 4 0 2 88 2 2 0]
[ 1 1 5 2 4 1 122 16 3]
[ 0 14 7 0 7 1 15 84 1]
[ 0 1 1 2 2 0 4 2 34]]
INFO : validation_loss=1.016 ; best_validation_loss=1.212
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=0.9 ; accuracy=0.723
INFO : precision=[0.75, 0.57, 0.74, 0.64, 0.7, 0.96, 0.78, 0.63, 0.76]
Recall=[0.49, 0.7, 0.77, 0.53, 0.7, 0.91, 0.8, 0.67, 0.76]
INFO : F1_score=[0.59, 0.62, 0.76, 0.58, 0.7, 0.93, 0.79, 0.65, 0.76]
INFO : confusion_matrix=
[[ 33 6 6 3 8 1 5 5 0]
[ 2 60 1 3 11 0 1 8 0]
[ 1 2 108 1 12 0 7 8 1]
[ 4 4 0 28 11 1 1 1 3]
[ 1 17 18 4 133 1 2 10 3]
[ 1 1 2 0 3 91 1 1 0]
[ 2 1 4 2 4 0 124 15 3]
[ 0 14 6 0 7 1 14 86 1]
[ 0 1 1 3 0 0 4 2 35]]
INFO : validation_loss=0.901 ; best_validation_loss=1.016
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=0.83 ; accuracy=0.742
INFO : precision=[0.76, 0.6, 0.77, 0.62, 0.73, 0.97, 0.79, 0.66, 0.74]
Recall=[0.55, 0.74, 0.79, 0.55, 0.72, 0.91, 0.81, 0.68, 0.76]
INFO : F1_score=[0.64, 0.66, 0.78, 0.58, 0.73, 0.94, 0.8, 0.67, 0.75]
INFO : confusion_matrix=
[[ 37 5 6 4 6 0 3 6 0]
[ 1 64 1 3 10 0 1 6 0]
[ 1 1 110 1 12 0 7 7 1]
[ 6 3 0 29 9 1 1 1 3]
[ 1 17 13 5 137 1 3 8 4]
[ 1 1 2 1 3 91 0 1 0]
[ 2 2 4 2 3 0 125 14 3]
[ 0 12 5 0 8 1 14 88 1]
[ 0 2 1 2 0 0 4 2 35]]
INFO : validation_loss=0.826 ; best_validation_loss=0.901
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=0.77 ; accuracy=0.752
INFO : precision=[0.73, 0.62, 0.8, 0.62, 0.74, 0.97, 0.79, 0.69, 0.71]
Recall=[0.6, 0.76, 0.79, 0.57, 0.73, 0.91, 0.82, 0.69, 0.76]
INFO : F1_score=[0.66, 0.68, 0.8, 0.59, 0.73, 0.94, 0.81, 0.69, 0.74]
INFO : confusion_matrix=
[[ 40 4 5 5 4 0 3 6 0]
[ 1 65 1 3 10 0 1 5 0]
[ 1 1 111 1 12 0 7 5 2]
[ 7 2 0 30 9 1 1 1 2]
[ 2 16 12 4 138 1 3 7 6]
[ 1 1 2 1 3 91 0 1 0]
[ 3 2 3 2 2 0 127 13 3]
[ 0 11 4 0 9 1 14 89 1]
[ 0 2 1 2 0 0 4 2 35]]
INFO : validation_loss=0.774 ; best_validation_loss=0.826
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=0.73 ; accuracy=0.767
INFO : precision=[0.72, 0.64, 0.8, 0.64, 0.76, 0.97, 0.81, 0.72, 0.72]
Recall=[0.61, 0.76, 0.81, 0.57, 0.74, 0.93, 0.84, 0.71, 0.78]
INFO : F1_score=[0.66, 0.7, 0.8, 0.6, 0.75, 0.95, 0.82, 0.72, 0.75]
INFO : confusion_matrix=
[[ 41 5 5 5 4 0 4 3 0]
[ 1 65 1 2 10 0 1 6 0]
[ 1 0 113 1 12 0 7 4 2]
[ 7 2 0 30 8 1 1 2 2]
[ 2 14 12 4 140 2 2 7 6]
[ 1 1 2 1 1 93 0 1 0]
[ 3 2 3 2 2 0 130 10 3]
[ 1 11 4 0 8 0 12 92 1]
[ 0 1 1 2 0 0 4 2 36]]
INFO : validation_loss=0.734 ; best_validation_loss=0.774
INFO : ****** Current epoch: 8 ******
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=0.7 ; accuracy=0.782
INFO : precision=[0.75, 0.66, 0.81, 0.72, 0.76, 0.97, 0.82, 0.75, 0.73]
Recall=[0.66, 0.78, 0.83, 0.58, 0.75, 0.92, 0.86, 0.73, 0.78]
INFO : F1_score=[0.7, 0.71, 0.82, 0.65, 0.76, 0.94, 0.84, 0.74, 0.76]
INFO : confusion_matrix=
[[ 44 5 5 3 4 0 4 2 0]
[ 1 67 1 1 9 0 1 6 0]
[ 1 0 116 1 11 0 7 2 2]
[ 5 2 0 31 8 1 1 3 2]
[ 2 15 11 4 142 2 1 6 6]
[ 1 1 1 1 2 92 0 2 0]
[ 3 2 4 1 2 0 133 8 2]
[ 2 9 5 0 7 0 11 94 1]
[ 0 1 1 1 1 0 4 2 36]]
INFO : validation_loss=0.704 ; best_validation_loss=0.734
INFO : ****** Current epoch: 9 ******
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=0.68 ; accuracy=0.788
INFO : precision=[0.75, 0.67, 0.81, 0.73, 0.77, 0.99, 0.82, 0.75, 0.74]
Recall=[0.69, 0.79, 0.84, 0.6, 0.76, 0.92, 0.86, 0.71, 0.8]
INFO : F1_score=[0.72, 0.73, 0.82, 0.66, 0.76, 0.95, 0.84, 0.73, 0.77]
INFO : confusion_matrix=
[[ 46 4 4 2 4 0 4 3 0]
[ 1 68 1 1 9 0 1 5 0]
[ 1 0 117 1 10 0 8 2 1]
[ 5 3 0 32 6 1 1 3 2]
[ 2 15 11 5 143 0 1 6 6]
[ 1 0 1 1 2 92 0 2 1]
[ 3 2 4 1 2 0 133 8 2]
[ 2 8 6 0 9 0 11 92 1]
[ 0 1 1 1 1 0 4 1 37]]
INFO : validation_loss=0.682 ; best_validation_loss=0.704
INFO : ****** Current epoch: 10 ******
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=0.796
INFO : precision=[0.75, 0.69, 0.82, 0.7, 0.79, 0.98, 0.84, 0.75, 0.76]
Recall=[0.7, 0.79, 0.86, 0.62, 0.75, 0.91, 0.87, 0.73, 0.83]
INFO : F1_score=[0.72, 0.74, 0.84, 0.66, 0.77, 0.94, 0.85, 0.74, 0.79]
INFO : confusion_matrix=
[[ 47 3 4 3 2 0 4 4 0]
[ 2 68 1 1 8 1 0 5 0]
[ 1 0 120 1 9 0 6 2 1]
[ 5 3 0 33 6 0 1 3 2]
[ 2 15 11 5 142 0 1 7 6]
[ 1 0 1 2 2 91 0 2 1]
[ 3 2 4 1 2 0 135 7 1]
[ 2 7 5 0 9 1 10 94 1]
[ 0 1 1 1 0 0 4 1 38]]
INFO : validation_loss=0.667 ; best_validation_loss=0.682
INFO : ****** Current epoch: 11 ******
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=0.802
INFO : precision=[0.74, 0.71, 0.82, 0.69, 0.8, 0.98, 0.85, 0.75, 0.76]
Recall=[0.73, 0.79, 0.84, 0.62, 0.77, 0.91, 0.88, 0.74, 0.83]
INFO : F1_score=[0.74, 0.75, 0.83, 0.65, 0.79, 0.94, 0.86, 0.75, 0.79]
INFO : confusion_matrix=
[[ 49 3 4 3 2 0 3 3 0]
[ 2 68 1 1 8 1 0 5 0]
[ 2 0 118 1 9 0 6 3 1]
[ 5 2 0 33 6 0 1 3 3]
[ 2 13 10 5 146 0 1 7 5]
[ 1 0 1 2 2 91 0 2 1]
[ 3 2 4 1 1 0 136 7 1]
[ 2 7 5 1 8 1 9 95 1]
[ 0 1 1 1 0 0 4 1 38]]
INFO : validation_loss=0.658 ; best_validation_loss=0.667
INFO : ****** Current epoch: 12 ******
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=0.809
INFO : precision=[0.75, 0.7, 0.82, 0.75, 0.82, 0.98, 0.86, 0.77, 0.71]
Recall=[0.75, 0.79, 0.87, 0.62, 0.76, 0.91, 0.89, 0.74, 0.87]
INFO : F1_score=[0.75, 0.74, 0.84, 0.68, 0.79, 0.94, 0.88, 0.75, 0.78]
INFO : confusion_matrix=
[[ 50 3 4 2 2 0 3 3 0]
[ 2 68 1 1 6 1 0 6 1]
[ 2 0 122 0 7 0 5 2 2]
[ 5 2 0 33 6 0 1 2 4]
[ 3 13 11 4 144 0 1 7 6]
[ 1 0 1 2 2 91 0 2 1]
[ 2 2 4 1 1 0 138 6 1]
[ 2 8 5 1 7 1 9 95 1]
[ 0 1 1 0 0 0 3 1 40]]
INFO : validation_loss=0.654 ; best_validation_loss=0.658
INFO : ****** Current epoch: 13 ******
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=0.809
INFO : precision=[0.74, 0.71, 0.8, 0.76, 0.83, 0.98, 0.87, 0.77, 0.7]
Recall=[0.75, 0.79, 0.88, 0.64, 0.75, 0.91, 0.88, 0.76, 0.87]
INFO : F1_score=[0.74, 0.75, 0.84, 0.69, 0.79, 0.94, 0.87, 0.76, 0.78]
INFO : confusion_matrix=
[[ 50 3 4 2 2 0 3 3 0]
[ 2 68 1 1 6 1 0 6 1]
[ 2 0 123 0 7 0 4 2 2]
[ 5 2 0 34 5 0 1 2 4]
[ 3 13 12 4 141 0 1 8 7]
[ 1 0 1 2 2 91 0 2 1]
[ 4 2 4 1 1 0 136 6 1]
[ 1 7 7 1 5 1 8 98 1]
[ 0 1 1 0 0 0 3 1 40]]
INFO : validation_loss=0.659 ; best_validation_loss=0.654
INFO : Early stopping