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Understanding Learning Rates and How It Improves Performance in Deep Learning This post is an attempt to document my understanding on the following topic: What is the learning rate? What is it’s signibcance? How does one systematically arrive at a good learning rate? Why do we change the learning rate during training? How do we deal with learning rates when using pretrained model?
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HadzZulkii
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DataScientistatSEEK
Jan22 · 8minread
UnderstandingLearningRatesandHowIt
ImprovesPerformanceinDeepLearning
This post is an attempt to document my understanding on the following
topic:
What is the learning rate? What is it’s signicance?
How does one systematically arrive at a good learning rate?
Why do we change the learning rate during training?
How do we deal with learning rates when using pretrained model?
Much of this post are based on the stu written by past fast.ai fellows
[1], [2], [5] and [3]. This is a concise version of it, arranged in a way
for one to quickly get to the meat of the material. Do go over the
references for more details.
Firsto,whatisalearningrate?
Learning rate is a hyper-parameter that controls how much we are
adjusting the weights of our network with respect the loss gradient. The
lower the value, the slower we travel along the downward slope. While
this might be a good idea (using a low learning rate) in terms of making
sure that we do not miss any local minima, it could also mean that we’ll
be taking a long time to converge — especially if we get stuck on a
plateau region.
The following formula shows the relationship.
new_weight = existing_weight — learning_rate * gradient
•
•
•
•
Typically learning rates are congured naively at random by the user.
At best, the user would leverage on past experiences (or other types of
learning material) to gain the intuition on what is the best value to use
in setting learning rates.
As such, it’s often hard to get it right. The below diagram demonstrates
the dierent scenarios one can fall into when conguring the learning
rate.
Gradientdescentwithsmall(top)andlarge(bottom)learningrates.Source:AndrewNg’sMachine
LearningcourseonCoursera
Eectofvariouslearningratesonconvergence(ImgCredit:cs231n)
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