upcoder / centerloss Goto Github PK

您好，
下面是几个阶段的训练输出，center-loss 下降的特别快，而softmax loss 基本不动，随着训练进行，centerloss 逐渐增加，softmax loss 逐渐下降，在其他的数据集上训练过程也是如此，这样正常吗，能否解释一下这个过程的原因？ (正常情况下 l2 loss 也是这样，一般先降 l2 loss 然后再降 softmax loss， 这样训练就特别慢, 您能帮忙解释一下不？) 。

谢谢！
step: 0, training accuracy: 0.01, training loss: 9.13, center_loss_value: 8.95, softmax_loss_value: 4.60
8.9476
step: 100, training accuracy: 0.00, training loss: 4.60, center_loss_value: 0.00, softmax_loss_value: 4.60
0.000475059
step: 200, training accuracy: 0.00, training loss: 4.60, center_loss_value: 0.00, softmax_loss_value: 4.60
0.000334069
step: 300, training accuracy: 0.00, training loss: 4.61, center_loss_value: 0.00, softmax_loss_value: 4.61
0.000232262
...
step: 7000, validation accuracy: 0.00, validation loss: 4.61
step: 7000, training accuracy: 0.02, training loss: 4.61, center_loss_value: 0.01, softmax_loss_value: 4.60
0.00856465
step: 7100, training accuracy: 0.00, training loss: 4.60, center_loss_value: 0.01, softmax_loss_value: 4.60
0.00611517
step: 7200, training accuracy: 0.03, training loss: 4.59, center_loss_value: 0.01, softmax_loss_value: 4.59
0.00610098
step: 7300, training accuracy: 0.02, training loss: 4.60, center_loss_value: 0.01, softmax_loss_value: 4.59
0.00591285
...
step: 10000, validation accuracy: 0.02, validation loss: 4.60
step: 10000, training accuracy: 0.03, training loss: 4.60, center_loss_value: 0.02, softmax_loss_value: 4.55
0.0197095
step: 10100, training accuracy: 0.05, training loss: 4.58, center_loss_value: 0.02, softmax_loss_value: 4.56
0.019367
step: 10200, training accuracy: 0.01, training loss: 4.59, center_loss_value: 0.02, softmax_loss_value: 4.56
0.0246912
step: 10300, training accuracy: 0.00, training loss: 4.59, center_loss_value: 0.03, softmax_loss_value: 4.56
0.0319362
step: 10400, training accuracy: 0.03, training loss: 4.56, center_loss_value: 0.03, softmax_loss_value: 4.53
0.0250489
....
step: 15000, validation accuracy: 0.03, validation loss: 4.59
step: 15000, training accuracy: 0.02, training loss: 4.59, center_loss_value: 0.06, softmax_loss_value: 4.48
0.0584633
step: 15100, training accuracy: 0.01, training loss: 4.55, center_loss_value: 0.07, softmax_loss_value: 4.49
0.0664418
step: 15200, training accuracy: 0.04, training loss: 4.48, center_loss_value: 0.05, softmax_loss_value: 4.43
0.0492492
step: 15300, training accuracy: 0.05, training loss: 4.53, center_loss_value: 0.05, softmax_loss_value: 4.48
...step: 79000, validation accuracy: 0.09, validation loss: 12.37
step: 79000, training accuracy: 0.25, training loss: 12.37, center_loss_value: 0.12, softmax_loss_value: 2.98
0.117979
step: 79100, training accuracy: 0.23, training loss: 3.09, center_loss_value: 0.11, softmax_loss_value: 2.98
0.11036
step: 79200, training accuracy: 0.25, training loss: 3.10, center_loss_value: 0.12, softmax_loss_value: 2.98
0.118568
step: 79300, training accuracy: 0.23, training loss: 3.15, center_loss_value: 0.16, softmax_loss_value: 2.99
0.161232
step: 79400, training accuracy: 0.23, training loss: 3.24, center_loss_value: 0.22, softmax_loss_value: 3.01
0.220702
step: 79500, training accuracy: 0.21, training loss: 3.14, center_loss_value: 0.16, softmax_loss_value: 2.98
0.159162
step: 79600, training accuracy: 0.21, training loss: 3.09, center_loss_value: 0.12, softmax_loss_value: 2.97
0.121266
step: 79700, training accuracy: 0.22, training loss: 3.18, center_loss_value: 0.18, softmax_loss_value: 2.99
0.184051
step: 79800, training accuracy: 0.18, training loss: 3.14, center_loss_value: 0.17, softmax_loss_value: 2.98
0.1673
step: 79900, training accuracy: 0.19, training loss: 3.13, center_loss_value: 0.15, softmax_loss_value: 2.98

你好，我按照您的方法，在softmax_loss基础上，增加了center loss, 但是会出现loss = nan的情况，从而运行不了了，这是什么问题呢