I'd like to ask for any kind of assistance regarding the following problem:

I was given the following training data: 100 numbers, each one is a parameter, they together define a number X(also given).This is one instance,I have 20 000 instances for training.Next, I have 5000 lines given, each containing the 100 numbers as parameters.My task is to predict the number X for these 5000 instances.

I am stuck because I only know of the sigmoid activation function so far, and I assume it's not suitable for cases like this where the output values aren't either 0 or 1.

So my question is this : What's a good choice for an activation function and how does one go about implementing a neural network for a problem such as this?


Usually you're normalizing the data first, meaning that your whole dataset will be in between 0 and 1. Afterwords after you're having the model predictions, when computing the cost function or evaluating the model, you can apply the inverse of the normalization function.

|improve this answer|||||
  • $\begingroup$ I'm a little confused now, the point of the sigmoid function is to put the data set between 0 and 1? $\endgroup$ – Grimnast Dec 11 '19 at 23:35
  • 1
    $\begingroup$ No, you use sigmoid to introduce non-linearity in your model. In addition sigmoid is especially used for models where we have to predict the probability as an output since it's defined in the range of 0 and 1. For normalizing the data i would suggest you to look at min-max normalization or z-score normalization. $\endgroup$ – razvanc92 Dec 12 '19 at 8:13

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.