Activation Classification
- Classifies activation functions into main types to describe how a neural network produces outputs.
- Common types are linear, binary, threshold, and sigmoid.
- Enables using different activation functions in different layers for greater flexibility and accuracy.
Definition
Section titled “Definition”Activation classification is a method used in machine learning to determine the output of a neural network by categorizing the various activation functions used in the network, which helps to understand how the network processes information.
Explanation
Section titled “Explanation”Activation classification groups activation functions into four main types:
- Linear: Calculate a network output as a linear combination of input values.
- Binary: Decide whether a given input should be considered part of the network output (binary decision).
- Threshold: Decide inclusion in the output based on whether an input meets a specified threshold.
- Sigmoid: Map inputs to an S-shaped (sigmoidal) curve, producing bounded outputs.
A key advantage of activation classification is that it permits using different activation functions in different parts (layers) of a single neural network. This allows greater flexibility and can improve accuracy by choosing activation types appropriate to each layer’s role.
Examples
Section titled “Examples”Linear activation example
Section titled “Linear activation example”A linear activation function might be used to calculate the output of a neural network that is trying to predict the stock price of a company based on a set of input values such as the company’s earnings and revenue.
Binary activation example
Section titled “Binary activation example”A binary activation function might be used to determine whether or not an image contains a cat based on a set of input values such as the pixels in the image.
Threshold activation example
Section titled “Threshold activation example”A threshold activation function might be used to determine whether or not a given image contains a cat based on whether or not the number of pixels in the image that are considered to be part of a cat exceeds a certain threshold.
Sigmoid activation example
Section titled “Sigmoid activation example”A sigmoid activation function might be used to calculate the probability that a given image contains a cat based on a set of input values such as the pixels in the image; sigmoid outputs are often bounded between 0 and 1.
Composite (layered) example
Section titled “Composite (layered) example”A neural network used for image recognition might use a linear activation function in the first layer to extract features from the input image, a binary activation function in the second layer to determine which features are relevant for the task at hand, and a sigmoid activation function in the third layer to calculate the probability that the input image contains a cat.
Use cases
Section titled “Use cases”- Image recognition (explicitly cited as a task where different activation functions can be used across layers).
- Predicting numeric targets such as stock price (example of linear activation usage).
Related terms
Section titled “Related terms”- Activation function
- Linear activation
- Binary activation
- Threshold activation
- Sigmoid activation