I wonder to know why when batch size=512(16 in code), loss is increasing ?

[ivysoftware]

Line 872: Epoch 0 all_batch_num 510  loss 2.18 acc 0.61 0.8349 time 225.83
Line 1418: Epoch 1 all_batch_num 510  loss 2.70 acc 0.75 0.8872 time 224.98
Line 1964: Epoch 2 all_batch_num 510  loss 2.43 acc 0.83 0.9069 time 221.33
Line 2510: Epoch 3 all_batch_num 510  loss 2.96 acc 0.87 0.9104 time 221.77
Line 3056: Epoch 4 all_batch_num 510  loss 3.74 acc 0.88 0.8987 time 216.13
Line 3602: Epoch 5 all_batch_num 510  loss 4.38 acc 0.89 0.9288 time 210.82
Line 4148: Epoch 6 all_batch_num 510  loss 5.18 acc 0.90 0.9268 time 218.51
Line 4694: Epoch 7 all_batch_num 510  loss 6.28 acc 0.90 0.9312 time 210.94
Line 5240: Epoch 8 all_batch_num 510  loss 7.62 acc 0.91 0.9326 time 218.76
Line 5786: Epoch 9 all_batch_num 510  loss 8.98 acc 0.91 0.9245 time 221.23
Line 6332: Epoch 10 all_batch_num 510  loss 10.36 acc 0.91 0.9182 time 212.32
Line 6878: Epoch 11 all_batch_num 510  loss 11.74 acc 0.92 0.9292 time 211.89

[FrankWork]

the classification loss is decreased but the adv loss is increased

[xljhtq]

@FrankWork when I am training the adversarial network by rebuilding network with TF , the domain_loss is decreasing when the loss_adv is increasing. But due to the discrimanor of loss_adv, the loss_adv will be decreasing so that the domain_loss will be increasing and the acc will be decreasing. So it will lose convergence and what i can do to prevent this ? Or I build the network incorrectly?