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| # Learning Unsupervised Learning Rules | |
| This repository contains code and weights for the learned update rule | |
| presented in "Learning Unsupervised Learning Rules." At this time, this | |
| code can not meta-train the update rule. | |
| ### Structure | |
| `run_eval.py` contains the main training loop. This constructs an op | |
| that runs one iteration of the learned update rule and assigns the | |
| results to variables. Additionally, it loads the weights from our | |
| pre-trained model. | |
| The base model and the update rule architecture definition can be found in | |
| `architectures/more_local_weight_update.py`. For a complete description | |
| of the model, see our [paper](https://arxiv.org/abs/1804.00222). | |
| ### Dependencies | |
| [absl]([https://github.com/abseil/abseil-py), [tensorflow](https://tensorflow.org), [sonnet](https://github.com/deepmind/sonnet) | |
| ### Usage | |
| First, download the [pre-trained optimizer model weights](https://storage.googleapis.com/learning_unsupervised_learning/200_tf_graph.zip) and extract it. | |
| ```bash | |
| # move to the folder above this folder | |
| cd path_to/research/learning_unsupervised_learning/../ | |
| # launch the eval script | |
| python -m learning_unsupervised_learning.run_eval \ | |
| --train_log_dir="/tmp/learning_unsupervised_learning" \ | |
| --checkpoint_dir="/path/to/downloaded/model/tf_graph_data.ckpt" | |
| ``` | |
| ### Contact | |
| Luke Metz, Niru Maheswaranathan, Github: @lukemetz, @nirum. Email: {lmetz, nirum}@google.com | |