ReLU stands for Rectified Linear Unit. It’s defined as:
f(x) = max(0, x)
This means that if the input ( x ) is positive, the output is ( x ); if the input is negative, the output is 0.
Why is ReLU Important?
-
- Simplicity: ReLU is computationally efficient because it involves simple thresholding at zero.
- Non-linearity: Despite its simplicity, ReLU introduces non-linearity, which helps neural networks learn complex patterns.
- Sparse Activation: ReLU can lead to sparse activations, meaning that in a given layer, many neurons will output zero. This can make the network more efficient and reduce the risk of overfitting.
Advantages of ReLU
-
- Efficient Computation: ReLU is faster to compute compared to other activation functions like sigmoid or tanh.
- Mitigates Vanishing Gradient Problem: Unlike sigmoid and tanh, ReLU does not saturate for positive values, which helps in mitigating the vanishing gradient problem during backpropagation.
Disadvantages of ReLU
-
- Dying ReLU Problem: Sometimes, neurons can get stuck during training, always outputting zero. This is known as the “dying ReLU” problem.
Variants of ReLU
To address some of its limitations, several variants of ReLU have been proposed, such as:
-
- Leaky ReLU: Allows a small, non-zero gradient when the input is negative.
- Parametric ReLU (PReLU): Similar to Leaky ReLU but with a learnable parameter for the slope of the negative part.
- Exponential Linear Unit (ELU): Smooths the negative part to avoid the dying ReLU problem.