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The Population-based training code is imported fromGenetic Curriculum experiment conducted atOctavian.AI. The idea ofPopulation-based training originates fromDeepMind.

The core codebase implements graph question answering (GQA), usingCLEVR-graph as the dataset andMACnets as the reasoning architecture.

Apologies that the training data isn't available - I've yet to find a quick solution to this, when I get the system working on more questions I'll publish a stable "all question" dataset with 1M items. For now, you can easily build you own data - ask David for help.

For more in-depth information about what works/doesn't work, check out theexperiment log.

SeeRUNNING.md for how to both run the network and also use a cluster to optimize its hyper-parameters.

The short summary of how to train locally:

$ pipenv install$ pipenv shell(mac-graph-sjOzWQ6Y) $ python -m macgraph.train

Thanks to Drew Hudson and Christopher Manning for publishing their work,Compositional Attention Networks for Machine Reasoning upon which this is based. Thanks also to DeepMind for publishing their Differentiable Neural Computer results in Nature with a demonstration of that architecture solving graph problems, it is a reassurance that this endeavor is not ill-founded.

Since you're here.

There once was an old man of Esser,
Whose knowledge grew lesser and lesser,
It at last grew so small
He knew nothing at all
And now he's a college professor.