Comments (4)
shaohua0116
commented on May 28, 2024
If you want to keep generating samples along the training process, just use the argument --dump_result. If you want to generate samples while testing trained models, you can simply fetch self.model.fake_img.
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douglas01996
commented on May 28, 2024
After generating samples along the training process, how to use visualize_training.py to create gif?
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shaohua0116
commented on May 28, 2024
Taking CIFAR10 for example, use the command python visualize_training .py --h 32 --w 32 --c 3 --n 8 --output_file visualized_sample --train_dir train_dir/default-CIFAR10_lr_0.0001_update_G5_D1-20170101-194957
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douglas01996
commented on May 28, 2024
Thank you for your quick reply!
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Related Issues (20)
- About Generator loss HOT 1
- Batch normalization should behave differently in training and testing phase. HOT 2
- How do you test the model accuracy? + Check points are not stored correctly HOT 2
- Are unlabeled images used in SSGAN? HOT 4
- ValueError: num_outputs should be int or long, got 11. HOT 4
- Number of labelled samples per class HOT 2
- label of generative samples HOT 1
- why the _groundtruths in the evaler.py is different from the original test label? HOT 1
- predicted probabilities not summed to 1 HOT 2
- visualize_training.py is not working HOT 1
- Why the g_loss becomes negative? HOT 6
- Have you use unlabeled data๏ผ
- Adding a requirements.txt file
- Could you give an example for building and training own datasets?
- TypeError: argument of type 'NoneType' is not iterable
- ValueError: num_outputs should be int or long, got 400.
- Where is the generated image stored? HOT 6
- can't create generator HOT 9
- errors when I try two separate data.hy files HOT 1
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