90bb7b1fbb
* Removed image generation inside the training step. It was overwriting the image grid generated in `on_epoch_end`. I also made `adversarial_loss` a static method. * Incorporated Hyperparameter best practices Using ArgumentParser and hparams as defined in the Hyperparameters section of the documentation. This way we can set trainer flags (such as precision, and gpus) from the command line. * Incorporated Hyperparameter best practices Using ArgumentParser and hparams as defined in the Hyperparameters section of the documentation. This way we can set trainer flags (such as precision, and gpus) from the command line. * Split the data part into a LightningDataModule * Update pl_examples/domain_templates/generative_adversarial_net.py Co-authored-by: Jeff Yang <ydcjeff@outlook.com> |
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| .. | ||
| __init__.py | ||
| computer_vision_fine_tuning.py | ||
| generative_adversarial_net.py | ||
| imagenet.py | ||
| reinforce_learn_Qnet.py | ||
| semantic_segmentation.py | ||
| unet.py | ||