I am unable to understand when to use ReLU, ELU and Leaky ReLU. How do they compare to other activation functions(like the sigmoid and the tanh) and their pros and cons.


1 Answer 1


Look at this ML glossary:


ELU is very similiar to RELU except negative inputs. They are both in identity function form for non-negative inputs. On the other hand, ELU becomes smooth slowly until its output equal to $-\alpha$ whereas RELU sharply smoothes.


  • ELU becomes smooth slowly until its output equal to $-\alpha$ whereas RELU sharply smoothes.
  • ELU is a strong alternative to ReLU.
  • Unlike to ReLU, ELU can produce negative outputs.


  • For $x > 0$, it can blow up the activation with the output range of [0, inf].



  • It avoids and rectifies vanishing gradient problem.
  • ReLu is less computationally expensive than tanh and sigmoid because it involves simpler mathematical operations.


  • One of its limitations is that it should only be used within hidden layers of a neural network model.
  • Some gradients can be fragile during training and can die. It can cause a weight update which will makes it never activate on any data point again. In other words, ReLu can result in dead neurons.
  • In another words, For activations in the region ($x<0$) of ReLu, gradient will be 0 because of which the weights will not get adjusted during descent. That means, those neurons which go into that state will stop responding to variations in error/ input (simply because gradient is 0, nothing changes). This is called the dying ReLu problem.
  • The range of ReLu is $[0,\infty)$. This means it can blow up the activation.


LeakyRelu is a variant of ReLU. Instead of being 0 when $z<0$, a leaky ReLU allows a small, non-zero, constant gradient α (Normally, $\alpha=0.01$). However, the consistency of the benefit across tasks is presently unclear. [1]


  • Leaky ReLUs are one attempt to fix the “dying ReLU” problem by having a small negative slope (of 0.01, or so).


  • As it possess linearity, it can’t be used for the complex Classification. It lags behind the Sigmoid and Tanh for some of the use cases.

Further reading

  • Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, Kaiming He et al. (2015)
  • 2
    $\begingroup$ So is the Leaky ReLU better or the ELU? $\endgroup$
    – Ayazzia01
    Commented Sep 26, 2021 at 13:36
  • 2
    $\begingroup$ @Ayazzia01 In general, we can say yes. $\endgroup$
    – OmG
    Commented Sep 26, 2021 at 17:21

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