Answers.Body: <p>send us your loss function plot over epochs ( or steps ). this will help to get a better guidance(use log scale for loss axis). sending more details of your learning process may help too.</p>
  <p>but in this situation, i think you should decrease the learning rate and using the learning rate decrease method. this method helps you to see stepwise decrease of loss to the best losses. you can see more details of this method in this <a href="https://medium.com/analytics-vidhya/learning-rate-decay-and-methods-in-deep-learning-2cee564f910b#:%7E:text=Learning%20rate%20decay%20is%20a,help%20both%20optimization%20and%20generalization." rel="nofollow noreferrer">link</a>.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-02-21T08:16:22.257Z
Answers.LastActivityDate: 2022-02-21T08:16:22.257Z
Answers.UserDisplayName: reza karbasi
Answers.Body: <p>There might be several reasons for that:</p>
  <ul>
  <li>the data is easily understood by the model you are using</li>
  <li>the model you use is fitted to the problem</li>
  <li>the problem complexity is low</li>
  </ul>
  <p>There are a lot more reasons to explain the convergence of an algorithm.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-01-20T14:58:28.973Z
Answers.LastActivityDate: 2022-01-20T14:58:28.973Z
Answers.UserDisplayName: DueSouth
Answers.Body: <p>If a NN converges in a few steps to the absolute minimum of the loss function it means the loss function has a gradient (in the domain that the inputs defines) very regular, pointing to the absolute minimum.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-02-20T10:31:31.980Z
Answers.LastActivityDate: 2022-02-20T10:31:31.980Z
Answers.UserDisplayName: pasaba por aqui
Body: <p>I am running a model with fixed hyperparameters. To my surprise/shock, the model converged extremely fast with the least loss possible.</p>
  <p>I want to know the causes of this phenomenon. I have the following guesses:</p>
  <ol>
  <li><p>Underlying mapping is so simple.</p>
  </li>
  <li><p>Hyperparameters are apt.</p>
  </li>
  <li><p>Both.</p>
  </li>
  </ol>
  <p>Are there any other reasons for this phenomenon?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-02-19T17:22:29.457Z
Comments.Text: What you mean by "Hyperparameters are apt." ?
Comments.UserDisplayName: pasaba por aqui
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-02-20T08:31:31.810Z
Comments.Text: @pasabaporaqui I mean, correct hyperparameters for the model
Comments.UserDisplayName: hanugm
Tags: <neural-networks><convergence><hyper-parameters>
Title: What can I infer if my model is converging extremely fast?
UserDisplayName: hanugm
ThreadText: # What can I infer if my model is converging extremely fast?
  
  (Asked by: username_0 on 2022-01-20)
  
  I am running a model with fixed hyperparameters. To my surprise/shock, the model converged extremely fast with the least loss possible.
  
  I want to know the causes of this phenomenon. I have the following guesses:
  
  1. Underlying mapping is so simple.
  2. Hyperparameters are apt.
  3. Both.
  
  Are there any other reasons for this phenomenon?
  
  **Tags:** neural-networks, convergence, hyper-parameters
  > **Comments:**
  > **username_1**: What you mean by "Hyperparameters are apt." ?  
  > **username_0**: @pasabaporaqui I mean, correct hyperparameters for the model  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_2 on 2022-02-21. Score: 1
  
  send us your loss function plot over epochs ( or steps ). this will help to get a better guidance(use log scale for loss axis). sending more details of your learning process may help too.
  
  but in this situation, i think you should decrease the learning rate and using the learning rate decrease method. this method helps you to see stepwise decrease of loss to the best losses. you can see more details of this method in this [link](https://medium.com/analytics-vidhya/learning-rate-decay-and-methods-in-deep-learning-2cee564f910b#:%7E:text=Learning%20rate%20decay%20is%20a,help%20both%20optimization%20and%20generalization.).
  
  
  ---
  
  ### Answer by username_3 on 2022-01-20. Score: 0
  
  There might be several reasons for that:
  
  * the data is easily understood by the model you are using
  * the model you use is fitted to the problem
  * the problem complexity is low
  
  There are a lot more reasons to explain the convergence of an algorithm.
  
  
  ---
  
  ### Answer by username_1 on 2022-02-20. Score: 0
  
  If a NN converges in a few steps to the absolute minimum of the loss function it means the loss function has a gradient (in the domain that the inputs defines) very regular, pointing to the absolute minimum.
  
  
  ---
  
  