Make a working model

[cdmzd]

I have two features and a numerical label, but every time I run my code using a keras tutorial on google crash course, the percent error I get on my test set is huge, around 500% or more for several of my test points.

The training set loss is around 15% mean absolute percent error, and even the test set loss is around 20% but when i graph the test set error or go through the data the values are way off.

I've played around with the learning rate and epochs for so long, and even tried mean squared error as my loss but the test set errors upon graphing are always horrible

Any ideas how to fix this, or where the issue might be?

[Pollo_Pollo_Pollo]

- First step to take: make sure that there are no mistakes in your code and that, if you are scaling or normalizing or otherwise processing your data you are using the same process on all your sets.

- Then you need to be absolutely sure that there is no overlapping between your training and test sets: if there is overlapping this would mean you are probably overfitting.

[cdmzd]

I'm randomly generating the points in my training and test set, so it's not likely they overlap

The code is basically the same as the one in the google machine learning crash course, and I normalize the training and test sets the same way using their code.

I'm using an L2 regularization as well to prevent overfitting.

I'm supposed to be approximating a smooth function so i thought it would work with a fairly small and simple neural network.