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Perceptron and Linear Classification

Activation

Activation is the rule that converts a model's score into an output.

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Activation

Definition

Activation is the rule that converts a score into an output.

For a perceptron, the activation is usually a step rule.

Example:

if score >= 0:
  output = 1
else:
  output = -1

Activation = output rule applied to a score.


Score Before Activation

The model first computes a score.

Example:

score = dot(w, x) + b

This score is a number.

Example:

score = 4.7

The activation decides what to do with that number.

Activation comes after the weighted score.


Step Activation

A step activation jumps from one output to another at a threshold.

Example:

if score >= 0:
  output = 1
else:
  output = -1

Scores:

3  -> 1
0  -> 1
-2 -> -1

Step activation turns a score into one of two outputs.


Activation vs Threshold

The threshold is the cutoff.

The activation is the rule using that cutoff.

Example:

threshold = 0
activation rule = output 1 if score >= threshold else -1

Threshold is the value. Activation is the decision function.


Activation Produces The Prediction

For classification, the activation output is the prediction.

Example:

score = -3
activation(score) = -1
prediction = -1

The model can then compare:

prediction vs label

Activation produces the model's predicted class.


Activation Does Not Mean Awareness

The word "activation" does not mean the model understands anything.

It only means a rule produced an output.

Example:

score = 5
activation = 1

This is rule-based computation.

Activation is a mathematical output rule, not awareness.


Later Activations

Later neural networks use other activation functions.

Examples:

ReLU
sigmoid
tanh

Those come later.

For the perceptron, the important activation is the step rule.

The perceptron uses a simple threshold-based activation.


Final Summary

  • Activation converts a score into an output.
  • It comes after the weighted sum plus bias.
  • A perceptron usually uses step activation.
  • Threshold is the cutoff; activation is the rule.
  • Activation produces the prediction.
  • Activation does not imply awareness.
  • More activation functions come later.

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Continue the series.

Decision Boundary