Answers.Body: <p>There is definitely no 'mathematically optimal' LeakyReLU value, as it is dependent on the data, the architecture etc.</p>
  <p>In addition, to the best of my knowledge, there is no 'best practice' when it comes to LeakyReLU slope values. Most of the deep learning papers just state they use LeakyReLU with some slope parameter without mentioning why they choose the parameter. They might, or even might not, have tuned the parameter with simple empirical testing.</p>
  <p>The goal of LeakyRelu is simply to reduce the 'dying relu' problem. As long as there is <em>any</em> slope, this dying relu problem is mitigated. You could probably show that the dying relu problem persists by slope values very close to 0. With a default of 0.02, 0.01 or anything relatively significant, leaky relu does the job it is meant to do. I have seen LeakyRelu slopes up to 0.2-0.3 in GAN literature.</p>
  <p>Do not expect a small difference in LeakyReLU values to drastically influence the result of your network. It might help a tiny bit during tuning, but I'd recommend starting with the default value and then continuing with the rest of your model training. Come back to tuning the LeakyRelu only during the final model optimization and see if you can make any significant difference by wiggling the value around the default value.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
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Answers.Text: Thanks for selecting my answer as the correct one. In addition, it is always helpful for future visitors to also upvote the correct answer to show that it was helpful in answering your question.
Answers.UserDisplayName: Robin van Hoorn
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Answers.UserDisplayName: Robin van Hoorn
Answers.Body: <p>When this slope in the negative part is learned, the activation function is called Parametric ReLU or PReLU.</p>
  <p><a href="https://pytorch.org/docs/stable/generated/torch.nn.PReLU.html" rel="nofollow noreferrer">https://pytorch.org/docs/stable/generated/torch.nn.PReLU.html</a></p>
  
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Answers.UserDisplayName: Jaume Oliver Lafont
Body: <p>I am using <code>LeakyReLU</code> activation function in my architecture. We know that the default slope value is <code>1e-2</code>. I want to understand how do we decide that what slope value should we choose?</p>
  
Tags: <pytorch><activation-functions><leaky-relu>
Title: How do we determine the slope for leakyrelu activation function?
UserDisplayName: Rohit Singh
ThreadText: # How do we determine the slope for leakyrelu activation function?
  
  (Asked by: username_0 on 2023-04-12)
  
  I am using `LeakyReLU` activation function in my architecture. We know that the default slope value is `1e-2`. I want to understand how do we decide that what slope value should we choose?
  
  **Tags:** pytorch, activation-functions, leaky-relu
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2023-04-12. Score: 2 | Accepted answer
  
  There is definitely no 'mathematically optimal' LeakyReLU value, as it is dependent on the data, the architecture etc.
  
  In addition, to the best of my knowledge, there is no 'best practice' when it comes to LeakyReLU slope values. Most of the deep learning papers just state they use LeakyReLU with some slope parameter without mentioning why they choose the parameter. They might, or even might not, have tuned the parameter with simple empirical testing.
  
  The goal of LeakyRelu is simply to reduce the 'dying relu' problem. As long as there is *any* slope, this dying relu problem is mitigated. You could probably show that the dying relu problem persists by slope values very close to 0. With a default of 0.02, 0.01 or anything relatively significant, leaky relu does the job it is meant to do. I have seen LeakyRelu slopes up to 0.2-0.3 in GAN literature.
  
  Do not expect a small difference in LeakyReLU values to drastically influence the result of your network. It might help a tiny bit during tuning, but I'd recommend starting with the default value and then continuing with the rest of your model training. Come back to tuning the LeakyRelu only during the final model optimization and see if you can make any significant difference by wiggling the value around the default value.
  
  > **Comments:**
  > **username_1**: Thanks for selecting my answer as the correct one. In addition, it is always helpful for future visitors to also upvote the correct answer to show that it was helpful in answering your question.  
  
  ---
  
  ### Answer by username_2 on 2023-04-27. Score: 1
  
  When this slope in the negative part is learned, the activation function is called Parametric ReLU or PReLU.
  
  <https://pytorch.org/docs/stable/generated/torch.nn.PReLU.html>
  
  
  ---
  
  