Understanding Parametric ReLU in Deep Learning
Parametric ReLU (PReLU) is a type of activation function used in deep learning neural networks. It is an extension of the popular ReLU (Rectified Linear Unit) activation function.
Like ReLU, PReLU is a piecewise linear function that sets all negative values in the input to zero and keeps all positive values unchanged. However, unlike ReLU, PReLU introduces a learnable parameter that controls the slope of the function for negative input values. This allows PReLU to learn an adaptive slope for the negative inputs, instead of using a fixed slope of zero as in ReLU.
The formula for PReLU is as follows:
f(x) = max(0, x) + alpha * min(0, x)
where alpha is the learnable parameter that controls the slope for negative input values.
The main advantage of PReLU over ReLU is that it can handle the problem of "dying ReLU" where a large portion of the neurons in the network become inactive and produce zero outputs due to a fixed slope of zero for negative inputs. By introducing a learnable parameter for the negative inputs, PReLU can adapt to the distribution of the input data and prevent this problem.
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