Answers.Body: <p>One way to do this would be to add a small amount of noise to all of your inputs and determine the change in output.  Steadily increase the noise and measure impact on output.  Local gradients around the model's final weights are a measure of stability.</p>
  
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Answers.CreationDate: 2022-11-14T13:04:55.670Z
Answers.LastActivityDate: 2022-11-14T13:04:55.670Z
Answers.UserDisplayName: Snehal Patel
Body: <p>Let's say I have a trained deep learning model. It would be good if it would be numerically stable, so if I change input by small amount, the output will also change by small amount.
  How should I approach checking numerical stability of deep learning algorithm? Should I just test many different inputs and check whether the outputs for similar inputs are similar or are there any other ways to check that?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-12-11T13:16:12.623Z
Comments.Text: You need to define "numerical stability". It seems that you're not using the precise mathematical definition and that you're not aware of certain issues in ML.
Comments.UserDisplayName: nbro
Tags: <deep-learning><stability>
Title: How would one go about checking numerical stability of the deep learning algorithms?
UserDisplayName: SlimakSlimak
ThreadText: # How would one go about checking numerical stability of the deep learning algorithms?
  
  (Asked by: username_0 on 2022-11-14)
  
  Let's say I have a trained deep learning model. It would be good if it would be numerically stable, so if I change input by small amount, the output will also change by small amount.
  How should I approach checking numerical stability of deep learning algorithm? Should I just test many different inputs and check whether the outputs for similar inputs are similar or are there any other ways to check that?
  
  **Tags:** deep-learning, stability
  
  > **Comments:**
  > **username_1**: You need to define "numerical stability". It seems that you're not using the precise mathematical definition and that you're not aware of certain issues in ML.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2022-11-14. Score: 1
  
  One way to do this would be to add a small amount of noise to all of your inputs and determine the change in output. Steadily increase the noise and measure impact on output. Local gradients around the model's final weights are a measure of stability.
  
  
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