2021 01 04 Implementing A Learning Rate Finder From Scratch Ipynb Cola

Choosing the right learning rate is important when training Deep Learning models. Let's implement a Learning Rate Finder from scratch, taking inspiration from Leslie Smith's LR range test--a nifty technique to find optimal learning rates. In this post we will implement a learning rate finder from scratch. A learning rate finder helps us find sensible learning rates for our models to train with, including minimum and maximum values to use in a cyclical learning rate policy. Both concepts were invented by Leslie Smith and I suggest you check out his paper1! We'll implement the learning rate finder (and cyclical learning rates in a future post) into our Deep Neural Network created from scratch in the 2-part series Implementing a Deep Neural Network from Scratch.

Check that out first if you haven't read it already! Before we start, what is the learning rate? The learning rate is just a value we multiply our gradients by in Stochastic Gradient Descent before updating our parameters with those values. Think of it like a "weight" that reduces the impact of each step change so as to ensure we are not over-shooting our loss function. If you imagine our loss function like a parabola, and our parameters starting somehwere along the parabola, descending along by a specific amount will bring us further "down" in the parabola, to it's minimum... If the step amount is too big however, we risk overshooting that minimum point.

That's where the learning rate comes into play: it helps us achieve very small steps if desired. To refresh your memory, here is what happens in the optimization step of Stochastic Gradient Descent (Part 1 of our DNN series explains this formula in more detail, so read that first): There was an error while loading. Please reload this page. For training deep neural networks, selecting a good learning rate is essential for both better performance and faster convergence. Even optimizers such as Adam that are self-adjusting the learning rate can benefit from more optimal choices.

To reduce the amount of guesswork concerning choosing a good initial learning rate, a learning rate finder can be used. As described in this paper a learning rate finder does a small run where the learning rate is increased after each processed batch and the corresponding loss is logged. The result of this is a lr vs. loss plot that can be used as guidance for choosing a optimal initial lr. For the moment, this feature only works with models having a single optimizer. LR Finder support for DDP and any of its variations is not implemented yet.

It is coming soon. To enable the learning rate finder, your lightning module needs to have a learning_rate or lr property. Then, set Trainer(auto_lr_find=True) during trainer construction, and then call trainer.tune(model) to run the LR finder. The suggested learning_rate will be written to the console and will be automatically set to your lightning module, which can be accessed via self.learning_rate or self.lr. If your model is using an arbitrary value instead of self.lr or self.learning_rate, set that value as auto_lr_find: There was an error while loading.

Please reload this page. This article is also a Jupyter Notebook available to be run from the top down. There will be code snippets that you can then run in any environment. Below are the versions of fastai, fastcore, and wwf currently running at the time of writing this: Before we get started, there's a few questions we need to understand. Quite simply, a bad learning rate can mean bad performance.

There are 2 ways this can happen. Learning too slowly: If the learning rate is too small it will take a really long time to train your model. This can mean that to get a model of the same accuracy, you either would need to spend more time or more money. Said another way, it will either take longer to train the model using the same hardware or you will need more expensive hardware (or some combination of the two). There was an error while loading. Please reload this page.

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