Comments (3)
I guess so.
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Because the author used the nll_loss...
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Very nice work! During my training, I found that loss can become negative:
Train Epoch: 198 [0/100 (0%)] Loss1: 0.024868 Loss2: 0.022132 Discrepancy: 0.018226 Test set: Average loss: -0.0588, Accuracy C1: 9449/10000 (94%) Accuracy C2: 9509/10000 (95%) Accuracy Ensemble: 9554/10000 (96%) recording record/usps_mnist_k_4_alluse_no_onestep_False_1_test.txt Train Epoch: 199 [0/100 (0%)] Loss1: 0.012343 Loss2: 0.020431 Discrepancy: 0.030520 Test set: Average loss: -0.0581, Accuracy C1: 9419/10000 (94%) Accuracy C2: 9518/10000 (95%) Accuracy Ensemble: 9537/10000 (95%) recording record/usps_mnist_k_4_alluse_no_onestep_False_1_test.txt
Do you think this is normal?
have you solve this problem?and If my pytorch version is 0.4.1.could I get the same peformance?
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Related Issues (20)
- About datasets on classification HOT 3
- About the classifiers. HOT 1
- Replicating the results HOT 5
- When do segmentation task, you also need labels for target domain.
- Code for " Strong-Weak Distribution Alignment for Adaptive Object Detection " HOT 1
- Question about the handwritten digit experiment HOT 7
- cityscapes/info.json
- Some problems with building my own datasets on classificaion HOT 1
- question for location HOT 2
- Asking for the visualization code
- Reproduce the segmentation result HOT 3
- sharing svhn2mnist result
- Model Selection
- USPS->Mnist source-only result can't reach 0.634 HOT 2
- The misnt_data.mat dataset
- drn_d_105-12b40979.pth HOT 3
- The link (http://crcv.ucf.edu/data/adaptationseg/ICCV_dataset.zip) is lost.
- The difference between trainer and trainer_one_step
- synth traffic dataset
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