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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| basic_examples | ||
| domain_templates | ||
| README.md | ||
| __init__.py | ||
| bug_report_model.py | ||
| test_examples.py | ||
README.md
Examples
Our most robust examples showing all sorts of implementations can be found in our sister library PyTorch-Lightning-Bolts.
Basic examples
In this folder we add 3 simple examples:
- Image Classifier (trains arbitrary datasets with arbitrary backbones).
- MNIST classifier (defined the model inside the lightningModule).
- Autoencoder (shows how the LightningModule is meant to be used as a system)
Domain examples
This folder contains older examples. You should instead use the examples in PyTorch-Lightning-Bolts for advanced use cases.