ReLU (Rectified Linear Unit) and Sigmoid are both commonly used activation functions in neural networks.
Features:
ReLU is a simple and effective activation function. It returns the input directly for the positive part and zero for the negative part. This non-linear transformation helps the network learn more complex representations. ReLU is widely used in many deep learning models because it is relatively simple to compute in gradient descent and effectively prevents the vanishing gradient problem.
The sigmoid function maps the input to the range between (0, 1) and is often used in binary classification problems. Its output can be interpreted as a probability value and is therefore used in the output layer for the model's confidence that the sample belongs to a certain class. However, the gradient of the Sigmoid function approaches zero when the input is far from zero, which may lead to the vanishing gradient problem, especially in deep networks.
ReLU activation function graph:
- The graph of the ReLU function is a straight line, the output is equal to the input when the input is greater than zero, and the output is zero when the input is less than or equal to zero.
Sigmoid activation function graph:
- The graph of the sigmoid function is an S-shaped curve that maps the input to the range (0, 1).
In actual use, ReLU is often used in hidden layers, while Sigmoid is often used in output layers (for binary classification tasks). As research progresses, there are some variants, such as Leaky ReLU, Parametric ReLU, ELU, etc., aimed at improving the performance of the activation function. The choice of activation function usually depends on the specific task and network structure.