After finding the weighted sum, the second step is to constrain the weighted sum to produce a desired output.

Why is that important? Imagine if a perceptron had inputs in the range of 100-1000 but the goal was to simply predict whether or not something would occur — 1 for “Yes” and 0 for “No”. This would result in a very large weighted sum.

How can the perceptron produce a meaningful output in this case? This is exactly where activation functions come in! These are special functions that transform the weighted sum into a desired and constrained output.

For example, if you want to train a perceptron to detect whether a point is above or below a line (which we will be doing in this lesson!), you might want the output to be a +1 or -1 label. For this task, you can use the “sign activation function” to help the perceptron make the decision:

  • If weighted sum is positive, return +1
  • If weighted sum is negative, return -1

In this lesson, we will focus on using the sign activation function because it is the simplest way to get started with perceptrons and eventually visualize one in action.



Inside the .activation() method, return 1 if the weighted_sum is greater than or equal to 0.


Inside the .activation() method, return -1 if the weighted_sum is less than 0.


Try it out for yourself!

Print out the result of the method .activation() called on cool_perceptron if the weighted sum is 52.

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