Answers.Body: <p>Sigmoid is used for binary cases and softmax is its  generalized version for multiple classes. But, essentially what they do is over exaggerate the distances between the various values.</p>
  <p>If you have values on a unit sphere, apply sigmoid or softmax on those values would lead to the points going to the poles of the sphere.</p>
  
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Answers.CreationDate: 2021-04-15T20:09:19.917Z
Answers.LastActivityDate: 2021-04-15T20:09:19.917Z
Answers.UserDisplayName: Abhishek Verma
Body: <p>I know that when using <em>Sigmoid</em>, you only need 1 output neuron (binary classification) and for <em>Softmax</em> - it's 2 neurons (multiclass classification). But for performance improvement (if there is one), is there any difference which of these 2 approaches works better, or when would you recommend using one over the other. Or maybe there are certain situations when using one of these is better than the other.
  Any comments or shared experience will be appreciated.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-04-15T13:55:10.637Z
Comments.Text: there is no difference in performance - sigmoid is a special case of softmax. take a look at this [question](https://stats.stackexchange.com/questions/233658/softmax-vs-sigmoid-function-in-logistic-classifier)
Comments.UserDisplayName: Aray Karjauv
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-04-15T14:01:25.437Z
Comments.Text: pls take a look at this [The Differences between Sigmoid and Softmax Activation Functions](https://medium.com/arteos-ai/the-differences-between-sigmoid-and-softmax-activation-function-12adee8cf322) & this [Multi-label vs. Multi-class Classification: Sigmoid vs. Softmax](https://glassboxmedicine.com/2019/05/26/classification-sigmoid-vs-softmax/)
Comments.UserDisplayName: Faizy
Tags: <neural-networks><machine-learning><deep-learning><activation-functions>
Title: What are the pros and cons of using sigmoid or softmax approach when dealing with 2 classes?
UserDisplayName: Artūras Drūteika
ThreadText: # What are the pros and cons of using sigmoid or softmax approach when dealing with 2 classes?
  
  (Asked by: username_0 on 2021-04-15)
  
  I know that when using *Sigmoid*, you only need 1 output neuron (binary classification) and for *Softmax* - it's 2 neurons (multiclass classification). But for performance improvement (if there is one), is there any difference which of these 2 approaches works better, or when would you recommend using one over the other. Or maybe there are certain situations when using one of these is better than the other.
  Any comments or shared experience will be appreciated.
  
  **Tags:** neural-networks, machine-learning, deep-learning, activation-functions
  
  > **Comments:**
  > **username_1**: there is no difference in performance - sigmoid is a special case of softmax. take a look at this [question](https://stats.stackexchange.com/questions/233658/softmax-vs-sigmoid-function-in-logistic-classifier)  
  > **username_2**: pls take a look at this [The Differences between Sigmoid and Softmax Activation Functions](https://medium.com/arteos-ai/the-differences-between-sigmoid-and-softmax-activation-function-12adee8cf322) & this [Multi-label vs. Multi-class Classification: Sigmoid vs. Softmax](https://glassboxmedicine.com/2019/05/26/classification-sigmoid-vs-softmax/)  
  
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  ## 1 answers
  
  ### Answer by username_3 on 2021-04-15. Score: 1
  
  Sigmoid is used for binary cases and softmax is its generalized version for multiple classes. But, essentially what they do is over exaggerate the distances between the various values.
  
  If you have values on a unit sphere, apply sigmoid or softmax on those values would lead to the points going to the poles of the sphere.
  
  
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