Thoughts and Theory
The Dying ReLU Problem, Clearly Explained
Keep your neural network alive by understanding the downsides of ReLU
Contents
(1) What is ReLU and what are its advantages?
(2) What’s the Dying ReLU problem?
(3) What causes the Dying ReLU problem?
(4) How to solve the Dying ReLU problem?
Activation functions are mathematical equations that define how the weighted sum of the input of a neural node is transformed into an output, and they are key parts of an artificial neural network (ANN) architecture.
Activation functions add non-linearity to a neural network, allowing the network to learn complex patterns in the data. The choice of activation function has a significant impact on an ANN’s performance, and one of the most popular choices is the Rectified Linear Unit (ReLU).
What is ReLU, and what are its advantages?
The Rectified Linear Unit (ReLU) activation function can be described as:
f(x) = max(0, x)
What it does is:
(i) For negative input values, output = 0
(ii) For positive input values, output = original input value