Comments (2)
I met this problem, too. Does it relate to reproducibility?
from tf-gqn.
As I've explained here the loss seems to plateau because of the reconstruction term having reached the perfect means and the only thing which is optimized afterwards is the KL term which is, however, of a different order of magnitude. I admit that this loss setup is suboptimal and a bit confusing, so I'm happy about any suggestions.
from tf-gqn.
Related Issues (20)
- How can I test the model? HOT 8
- Predict result problem.
- measuring quality of pre-trained your snapshot HOT 4
- DRAW module as Generation architecture HOT 2
- Cannot reproduce visualization from snapshots HOT 28
- TypeError: Can't instantiate abstract class GQNTFRecordDataset with abstract methods _inputs HOT 3
- Question about pack_context function HOT 2
- data format
- AttributeError: 'GQNTFRecordDataset' object has no attribute '_graph_attr' HOT 2
- Training GQN with different dataset HOT 1
- GQN trained on CLEVR dataset HOT 10
- Self-Attention and other extensions HOT 2
- eval_summary_hook error HOT 2
- Dataset
- how many step we need
- It stays in local minimum and never goes down HOT 1
- Here is why the loss stays in local minimum ... HOT 3
- How to train using multi-GPU HOT 1
- Gradient flowing between inference_cell and generator_cell
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from tf-gqn.