WeightWatcher 0.7 has just been released, and it includes the new and improved advanced feature for analyzing Deep Neural Networks (DNN) called fix_fingers. To activate this, simply use: details = watcher.analyze(..., fix_fingers='clip_xmax', ...) This will take a tiny bit longer, and will yield more reliable alpha for your model layers,...

First, let me say thanks to all the users in our great community — we have reached over 93K downloads as of March 2023 ! The latest release of the open-source weightwatcher tool includes several important advances, including removing explicit dependence on tensorflow and torch on install the ability to process...

AI has taaken the world by storm. With recent advances like AlphaFold, Stable Difussion, and ChatGPT, Deep Neural Networks (DNNs) have had their Sputnik moment. And yet, we really don’t understand why DNNs even work. Unless, of course, you follow this blog and use the widely popular open-source weightwatcher tool. The open-source weightwatcher...

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Have you ever had to sort through HuggingFace to find your best model ? There are over 54,000 models on HuggingFace! So it’s not an easy task. Most people just choose the most popular model–and this is usually BERT. Or some BERT variant. Bert was created by Google, so it must be good. But is BERT the really best choice for...

Say you are training a Deep Neural Network (DNN), and you see your model is over-trained. Or just not performing well. Is there a way to detect which layer is actually over-trained? In this post, we will show how to use the open-source weightwatcher tool to answer this. WeightWatcher is an open-source, data-free diagnostic tool for...