ReLUfunktsiooni

The ReLU function, short for Rectified Linear Unit, is a fundamental activation function used in artificial neural networks. Its mathematical definition is simple: f(x) = max(0, x). This means that for any input value greater than zero, the output is the input itself. However, for any input value less than or equal to zero, the output is zero.

This non-linear behavior is crucial for neural networks, enabling them to learn complex patterns and relationships in data. Unlike linear functions, which would simply scale the input, the ReLU function introduces a degree of non-linearity that allows networks to approximate a wide range of functions.

One of the key advantages of ReLU is its computational efficiency. The calculation involves a simple comparison and selection, making it much faster to compute than other activation functions like sigmoid or tanh, especially during the training of deep neural networks. This speed advantage contributes significantly to the feasibility of training large and complex models.

Another beneficial property of ReLU is its role in alleviating the vanishing gradient problem. In deep networks, gradients can become extremely small as they propagate backward through layers, hindering learning. ReLU's gradient is 1 for positive inputs, which helps to maintain the gradient's magnitude and facilitates effective backpropagation. However, it's worth noting that for negative inputs, the gradient is zero, which can lead to the "dying ReLU" problem, where neurons become inactive and cease to learn. Despite this, ReLU remains a popular choice due to its overall effectiveness and computational advantages.