Neural networks are powerful tools for tackling prediction and classification problems. However, one of the most important decisions when building a network is choosing the output activation function. This choice directly affects the shape and range of your model’s predictions, impacting overall performance.
In this article, we’ll guide you on how to select the right activation based on your problem type: binary classification, multi-class classification, or regression.
What is an Output Activation Function?
The output activation function transforms the values produced by the last layer of your network into a format that can be interpreted as predictions.
Specifically, it determines:
The shape of the output – a single number, a vector, or a tensor.
The range of the output – a continuous number or a probability between 0 and 1.
It is essential that your network’s output matches the shape of your labels.
Problem Types and Recommended Activations
1. Binary Classification
Binary classification is when your task is to choose between two possibilities (e.g., “Is this a cat or not?”).
The network predicts the probability that an example belongs to the positive class.
Recommended activation: sigmoid, which maps any input to a value between 0 and 1.
Loss function to minimize: binary cross-entropy.
Example: If you want to predict whether an image contains a cat, the output might be 0.87, meaning an 87% chance that it’s a cat.
2. Multi-Class Classification
When there are more than two classes (e.g., cat, dog, fish, rabbit), it’s a multi-class classification problem.
The output is a vector of probabilities, one for each class.
Recommended activation: softmax, which converts scores into a probability distribution that sums to 1.
Loss function: multi-class cross-entropy.
The predicted class is the one with the highest probability, often determined with the argmax function.
Example: For an image of a cat, the output might be [0.9, 0.05, 0.03, 0.02], indicating a 90% probability for the “cat” class.
3. Regression
Regression problems involve predicting a continuous numerical value (e.g., the price of a house).
The output is the predicted value itself.
Recommended activation: identity (no activation) or ReLU if you want to ensure positive outputs.
Common loss functions: mean squared error (MSE) or mean absolute error (MAE).
Example: Predicting the price of a house might yield an output of 250000.
Key Takeaways
The choice of output activation function is never arbitrary; it depends on your problem type and the shape of your labels:
Sigmoid → binary classification
Softmax → multi-class classification
Identity/ReLU → regression Mean Squared Error