~/writing/math-foundations-for-reasoning-models $ ls
Perceptron and Linear Classification
Activation
Activation is the rule that converts a model's score into an output.
~/writing/math-foundations-for-reasoning-models $ cat topic.md
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.
~/writing/math-foundations-for-reasoning-models $ open
Continue the series.
Decision Boundary