Comparison of Activation Functions - Sigmoid, ReLU, and Softmax
Activation functions transform input values in an artificial neural network and transmit them to the next layer.
The Sigmoid, ReLU (Rectified Linear Unit), and Softmax functions that you have learned so far each have their own characteristics, advantages, and disadvantages.
Comparison of Activation Functions
| Function | Output Range | Features and Advantages | Disadvantages and Limitations |
|---|---|---|---|
| Sigmoid | (0, 1) | Probabilistic interpretation, suitable for binary classification | Vanishing gradient problem for large values |
| ReLU | (0, ∞) | Alleviates vanishing gradient problem, simple to compute | Neuron deactivation for values ≤ 0 |
| Softmax | (0, 1) | Suitable for multi-class classification, provides probability values | One class value can influence other classes |
Activation functions have a significant impact on the performance of neural network models.
It's important to choose the appropriate activation function based on the problem's characteristics.
In the next lesson, we will take a brief quiz to review what we've learned so far.
Want to learn more?
Join CodeFriends Plus membership or enroll in a course to start your journey.