FAQ: Hyperparameter Tuning - Manual tuning: learning rate

Frequently Asked Questions Uncategorized
1

Community%20FAQs%20on%20Codecademy%20Exercises
Community%20FAQs%20on%20Codecademy%20Exercises1000×208 141 KB

This community-built FAQ covers the “Manual tuning: learning rate” exercise from the lesson “Hyperparameter Tuning”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Build Deep Learning Models with TensorFlow

FAQs on the exercise Manual tuning: learning rate

There are currently no frequently asked questions associated with this exercise – that’s where you come in! You can contribute to this section by offering your own questions, answers, or clarifications on this exercise. Ask or answer a question by clicking reply (reply) below.

If you’ve had an “aha” moment about the concepts, formatting, syntax, or anything else with this exercise, consider sharing those insights! Teaching others and answering their questions is one of the best ways to learn and stay sharp.

Join the Discussion. Help a fellow learner on their journey.

Ask or answer a question about this exercise by clicking reply (reply) below!
You can also find further discussion and get answers to your questions over in #get-help.

Agree with a comment or answer? Like (like) to up-vote the contribution!

Need broader help or resources? Head to #get-help and #community:tips-and-resources. If you are wanting feedback or inspiration for a project, check out #project.

Looking for motivation to keep learning? Join our wider discussions in #community

Learn more about how to use this guide.

Found a bug? Report it online, or post in #community:Codecademy-Bug-Reporting

Have a question about your account or billing? Reach out to our customer support team!

None of the above? Find out where to ask other questions here!

2

How can I interpret the graph? What are the key aspects? Is it good if learning rate = validation rate?
And is it good if the graph is declining?

3

Having some issues with this path as well!
I’m quite lost on the shape of the layers subject.
But I think that the graphs are meant to show that the blue line (training set loss) never actually reaches the orange line (validation set loss) if the learning rate is too small. That means that the validation set isn’t converging with the training set, which means that the model will perform poorly, since any other dataset should have a close performance to the training set. Hope that’s what we are supposed to take from this exercise.

4

i think you guys maybe didnt see the different values on the y-axis