<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>

CC BY-SA 4.0
2022-02-21T08:16:22.257Z
2022-02-21T08:16:22.257Z
reza karbasi
<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>

CC BY-SA 4.0
2022-01-20T14:58:28.973Z
2022-01-20T14:58:28.973Z
DueSouth
<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>

CC BY-SA 4.0
2022-02-20T10:31:31.980Z
2022-02-20T10:31:31.980Z
pasaba por aqui
<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>

CC BY-SA 4.0
2022-02-19T17:22:29.457Z
What you mean by "Hyperparameters are apt." ?
pasaba por aqui
CC BY-SA 4.0
2022-02-20T08:31:31.810Z
@pasabaporaqui I mean, correct hyperparameters for the model
hanugm
<neural-networks><convergence><hyper-parameters>
What can I infer if my model is converging extremely fast?
hanugm
# 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  

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## 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.).


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### 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.


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### 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.


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