Epoch [1/5], Iter [1/51200] TotalLoss: 14.3430 Loss1: 3.4093 Loss2: 3.7797 Loss3: 3.4102 Loss3: 3.7438
Epoch [1/5], Iter [2/51200] TotalLoss: 9.1436 Loss1: 2.0375 Loss2: 2.9503 Loss3: 2.7624 Loss3: 1.3935
Epoch [1/5], Iter [3/51200] TotalLoss: 12.7019 Loss1: 3.1669 Loss2: 3.1706 Loss3: 3.1461 Loss3: 3.2183
Epoch [1/5], Iter [4/51200] TotalLoss: 199.0285 Loss1: 45.2067 Loss2: 60.3769 Loss3: 52.5529 Loss3: 40.8920
Epoch [1/5], Iter [5/51200] TotalLoss: 13.5216 Loss1: 3.3813 Loss2: 3.3809 Loss3: 3.3761 Loss3: 3.3833
Epoch [1/5], Iter [6/51200] TotalLoss: 12.3115 Loss1: 3.0906 Loss2: 3.0759 Loss3: 3.0606 Loss3: 3.0844
Epoch [1/5], Iter [7/51200] TotalLoss: 9.6911 Loss1: 2.4250 Loss2: 2.0567 Loss3: 2.1717 Loss3: 3.0377
Epoch [1/5], Iter [8/51200] TotalLoss: 11.4812 Loss1: 2.8772 Loss2: 2.8731 Loss3: 2.8619 Loss3: 2.8690
Epoch [1/5], Iter [9/51200] TotalLoss: 7.7518 Loss1: 1.9965 Loss2: 1.8900 Loss3: 1.9414 Loss3: 1.9240
Epoch [1/5], Iter [10/51200] TotalLoss: 5.8163 Loss1: 1.3725 Loss2: 1.4375 Loss3: 1.5680 Loss3: 1.4383
Epoch [1/5], Iter [11/51200] TotalLoss: 5.2633 Loss1: 1.3306 Loss2: 1.3322 Loss3: 1.2640 Loss3: 1.3365
Epoch [1/5], Iter [12/51200] TotalLoss: 3.2123 Loss1: 0.8192 Loss2: 0.7681 Loss3: 0.7401 Loss3: 0.8849
Epoch [1/5], Iter [13/51200] TotalLoss: 2.6675 Loss1: 0.6967 Loss2: 0.6703 Loss3: 0.6426 Loss3: 0.6579
Epoch [1/5], Iter [14/51200] TotalLoss: 2.8774 Loss1: 0.7332 Loss2: 0.7385 Loss3: 0.6811 Loss3: 0.7245
Epoch [1/5], Iter [15/51200] TotalLoss: 2.8546 Loss1: 0.7366 Loss2: 0.7264 Loss3: 0.7202 Loss3: 0.6713
Epoch [1/5], Iter [16/51200] TotalLoss: 2.7758 Loss1: 0.6806 Loss2: 0.6549 Loss3: 0.7345 Loss3: 0.7058
Epoch [1/5], Iter [17/51200] TotalLoss: 2.8139 Loss1: 0.7092 Loss2: 0.6574 Loss3: 0.7800 Loss3: 0.6674
Epoch [1/5], Iter [18/51200] TotalLoss: 2.7282 Loss1: 0.6225 Loss2: 0.6997 Loss3: 0.7237 Loss3: 0.6823
Epoch [1/5], Iter [19/51200] TotalLoss: 2.8259 Loss1: 0.7428 Loss2: 0.7204 Loss3: 0.6845 Loss3: 0.6783
Epoch [1/5], Iter [20/51200] TotalLoss: 2.5689 Loss1: 0.6701 Loss2: 0.6525 Loss3: 0.6837 Loss3: 0.5626
Epoch [1/5], Iter [21/51200] TotalLoss: 2.7335 Loss1: 0.6944 Loss2: 0.7051 Loss3: 0.6600 Loss3: 0.6740
Epoch [1/5], Iter [22/51200] TotalLoss: 2.8239 Loss1: 0.6977 Loss2: 0.7478 Loss3: 0.6584 Loss3: 0.7199
Epoch [1/5], Iter [23/51200] TotalLoss: 2.7279 Loss1: 0.7237 Loss2: 0.6032 Loss3: 0.6741 Loss3: 0.7270
Epoch [1/5], Iter [24/51200] TotalLoss: 2.6124 Loss1: 0.6750 Loss2: 0.6018 Loss3: 0.6210 Loss3: 0.7146
Epoch [1/5], Iter [25/51200] TotalLoss: 2.9644 Loss1: 0.7071 Loss2: 0.6792 Loss3: 0.7112 Loss3: 0.8669
Epoch [1/5], Iter [26/51200] TotalLoss: 3.0921 Loss1: 0.6923 Loss2: 0.7369 Loss3: 0.6726 Loss3: 0.9902
Epoch [1/5], Iter [27/51200] TotalLoss: 3.1947 Loss1: 0.7637 Loss2: 0.8234 Loss3: 0.7893 Loss3: 0.8183
Epoch [1/5], Iter [28/51200] TotalLoss: 2.9257 Loss1: 0.6981 Loss2: 0.7647 Loss3: 0.6602 Loss3: 0.8028
my network is vgg like artitecture and the task is to recognize captcha
it’s a multi-label task by using cnn.
but it didn’t work, it cannot  even fit two image by training a lot of steps…