Comments (2)
Hi @curryJ, sorry for the long silence, I've been busy with other projects. I'm currently in the middle of re-writing the input data pipeline (see commits on the dev-branch) and will get rid of the deprecated data loading functionality used in DeepMind's original release. Stay tuned, the new input pipeline will be merged within the week. I aim for tensorflow 1.12.1. Caveat: I've never tried the maze or jaco datasets provided by DeepMind. But for the sake of completeness, I'd like to add them at some point as well. Feel free to open separate issues for those datasets, but maybe wait until the new data loader is online.
Thanks,
Oliver
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Hi @curryJ , the new input_fn should be able to handle all GQN datasets. Also, both training script and input_fn now come with img_size parameters to resize images from different datasets. Let me know, if you still run into issues. If not, I'll close this issue.
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Related Issues (20)
- 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
- 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
- how many train step we need HOT 2
- 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
- Does it run on Windows? HOT 1
- KernelRestarter: restarting kernel (1/5), keep random ports HOT 2
- How can I get the right result?
- ERROR:tensorflow:Model diverged with loss = NaN
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