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[P] I re-implemented Hyperband, check it out!

[P] I re-implemented Hyperband, check it out!

Hyperband is a state-of-the-art algorithm for hyperparameter tunning that focuses on resource efficiency. It does so by encorperating early-stopping into it’s strategy. Here are some of the results:

For more, go here:

I was unable to find any great implementations of hyperband, so I implemented it! Here it is:

The implementation is commented and documented to help ensure correctness and improve code readability.

I believe I improve hyperband by allowing support for model checkpoints. The original hyperband assumed that each model was trained from scratch instead of checkpointing. We don’t need to train the same model with the same hyperparameters over and over again!

Finally, I also explored other improvements to hyperband like splitting based on the largest performance gap instead of splitting in half the search space every time.

submitted by /u/Deepblue129
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.