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How do I choose the right learning rate?
There are multiple ways to select a good starting point for the learning rate. A naive approach is to try a few different values and see which one gives you the best loss without sacrificing speed of training. We might start with a large value like 0.1, then try exponentially lower values: 0.01, 0.001, etc.
What if the learning rate is too high?
A learning rate that is too large can cause the model to converge too quickly to a suboptimal solution, whereas a learning rate that is too small can cause the process to get stuck. The learning rate is perhaps the most important hyperparameter. If you have time to tune only one hyperparameter, tune the learning rate.
Is high learning rate good?
If your learning rate is set too low, training will progress very slowly as you are making very tiny updates to the weights in your network. However, if your learning rate is set too high, it can cause undesirable divergent behavior in your loss function. 3e-4 is the best learning rate for Adam, hands down.
How to find optimal learning rates with the learning rate range?
Show you why learning rate decay suffers from the same issue as fixed learning rates, i.e. that humans still have to make a guess about where to start. Introduce the Learning Rate Range Test based on academic works and other Medium blogs, which allows you to select the optimal learning rate for your model empirically and easily.
Is there such a thing as a just right learning rate?
In between, there is a band of “just right” learning rates that successfully train. There is no learning rate that works for all optimizers. Learning rate can affect training time by an order of magnitude.
Why is it important to choose a learning rate?
The larger learning rates appear to help the model locate regions of general, large-scale optima, while smaller rates help the model focus on one particular local optimum. If the loss does not decrease for several epochs, the learning rate might be too low. The optimization process might also be stuck in a local minimum.
How to choose the best learning rate for your machine?
A common problem we all face when working on deep learning projects is choosing a learning rate and optimizer (the hyper-parameters). If you’re like me, you find yourself guessing an optimizer and learning rate, then checking if they work ( and we’re not alone ).