Rectifier (neural networks)
In the context of artificial neural networks, a "Rectifier" refers to a type of activation function. Activation functions introduce non-linearity into the network, allowing it to learn complex patterns and relationships in data. Without activation functions, a neural network would essentially function as a linear regression model, severely limiting its capabilities.
The term "Rectifier" most commonly describes the Rectified Linear Unit (ReLU) activation function. ReLU is defined as f(x) = max(0, x), meaning it outputs the input directly if it is positive, otherwise, it outputs zero.
ReLU has gained widespread adoption due to its simplicity and efficiency in training deep neural networks. It addresses the vanishing gradient problem, which can occur in deep networks using activation functions like sigmoid or tanh, where gradients become increasingly small as they propagate backward through the layers, hindering learning. By having a constant gradient of 1 for positive inputs, ReLU mitigates this issue, enabling faster and more effective training.
However, ReLU also has a potential drawback known as the "dying ReLU" problem. If a neuron's weights are updated such that the input to the ReLU activation function is consistently negative, the neuron will effectively become inactive as it will always output zero and its gradient will also be zero, preventing it from learning.
Variations of ReLU have been developed to address the "dying ReLU" problem. These include Leaky ReLU, Parametric ReLU (PReLU), and Exponential Linear Units (ELU). Leaky ReLU introduces a small slope for negative inputs, such as f(x) = ax (where a is a small constant like 0.01) for x < 0, preventing the neuron from becoming completely inactive. PReLU is similar to Leaky ReLU, but the slope 'a' is learned during training. ELU uses an exponential function for negative inputs, which allows for negative values and provides saturation to small inputs.
The choice of activation function, including the type of rectifier to use, is a crucial aspect of neural network design and depends on the specific task and data.