Answers.Body: <p>There is nothing specific about this particular numbers. Everything depends on the NN, software, model and data.
  As <a href="https://towardsdatascience.com/epoch-vs-iterations-vs-batch-size-4dfb9c7ce9c9" rel="nofollow noreferrer">illustrated</a> as the number of epoch increases, more number of times the weights are changed and the curve goes from underfitting to overfitting. And overfitting is exactly eccentric and crazy, see the picture at left:.</p>
  <p><a href="https://i.sstatic.net/q7kYz.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/q7kYz.png" alt="enter image description here" /></a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-09-09T04:05:35.257Z
Answers.Text: OP is asking about the learning curve (dynamics of the loss function and metrics with time), not the overfitting (to close alignment of the curve to the data).
Answers.UserDisplayName: spiridon_the_sun_rotator
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-09-09T10:59:36.077Z
Answers.Text: @spiridon_the_sun_rotator, what makes you think so? Any prove with a quote from the question text? Anyway, it would be better if the author clarifies.
Answers.UserDisplayName: Damir Tenishev
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-09-09T12:13:03.337Z
Answers.Text: he says, that after some time, the network starts to predict strange values - actually, the point to be clarified is whether it is on unseen data, or on any sample. Increase of number of epochs doesn't lead usually to rapid changes in the loss - the common phenomenon is the improvement on the train set, and deterioration (slow) on the test or validation.
Answers.UserDisplayName: spiridon_the_sun_rotator
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-09-09T12:24:06.043Z
Answers.Text: Where does he say "after some time"? Where is "about the learning curve"? Where is "dynamics of the loss function and metrics *with time*". He says that the model provides crazy values when he adjusts the number of echoes. That's it. Let's listen to @hanugm and avoid speculations.
Answers.UserDisplayName: Damir Tenishev
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-09-08T23:18:13.283Z
Answers.LastActivityDate: 2021-09-08T23:18:13.283Z
Answers.UserDisplayName: Damir Tenishev
Body: <p>Recently, I ran a code on my system that involves deep neural networks. The number of epochs provided by the designers are 301.</p>
  <p>I tried to increase the number of epochs to 501. To my shock, the model after 350 epochs is behaving eccentric. And I can say that they are just returning crazy values.</p>
  <p>What makes such phenomena possible?  Is &quot;number of epochs&quot; also a hyperaparameter/ magic number as an upper bound beyond which the model fails?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-09T12:14:27.190Z
Comments.Text: Model returns crazy values on unseen samples or on the training dataset as well? Do you have any specific learning schedule (dependence of learning rate on time)?
Comments.UserDisplayName: spiridon_the_sun_rotator
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-09T13:18:21.227Z
Comments.Text: @spiridon_the_sun_rotator On training data itself. While learning only.
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-10T21:33:08.057Z
Comments.Text: @hanugm, so does my answer below works for you or you still have the question open?
Comments.UserDisplayName: Damir Tenishev
Tags: <implementation><epochs>
Title: Does an increase in the number of epochs lead to complete breakdown?
UserDisplayName: hanugm
ThreadText: # Does an increase in the number of epochs lead to complete breakdown?
  
  (Asked by: username_0 on 2021-09-08)
  
  Recently, I ran a code on my system that involves deep neural networks. The number of epochs provided by the designers are 301.
  
  I tried to increase the number of epochs to 501. To my shock, the model after 350 epochs is behaving eccentric. And I can say that they are just returning crazy values.
  
  What makes such phenomena possible? Is "number of epochs" also a hyperaparameter/ magic number as an upper bound beyond which the model fails?
  
  **Tags:** implementation, epochs
  
  > **Comments:**
  > **username_1**: Model returns crazy values on unseen samples or on the training dataset as well? Do you have any specific learning schedule (dependence of learning rate on time)?  
  > **username_0**: @username_1 On training data itself. While learning only.  
  > **username_2**: @username_0, so does my answer below works for you or you still have the question open?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2021-09-08. Score: 1
  
  There is nothing specific about this particular numbers. Everything depends on the NN, software, model and data.
  As [illustrated](https://towardsdatascience.com/epoch-vs-iterations-vs-batch-size-4dfb9c7ce9c9) as the number of epoch increases, more number of times the weights are changed and the curve goes from underfitting to overfitting. And overfitting is exactly eccentric and crazy, see the picture at left:.
  
  [![enter image description here](https://i.sstatic.net/q7kYz.png)](https://i.sstatic.net/q7kYz.png)
  
  > **Comments:**
  > **username_1**: OP is asking about the learning curve (dynamics of the loss function and metrics with time), not the overfitting (to close alignment of the curve to the data).  
  > **username_2**: @username_1, what makes you think so? Any prove with a quote from the question text? Anyway, it would be better if the author clarifies.  
  > **username_1**: he says, that after some time, the network starts to predict strange values - actually, the point to be clarified is whether it is on unseen data, or on any sample. Increase of number of epochs doesn't lead usually to rapid changes in the loss - the common phenomenon is the improvement on the train set, and deterioration (slow) on the test or validation.  
  > **username_2**: Where does he say "after some time"? Where is "about the learning curve"? Where is "dynamics of the loss function and metrics *with time*". He says that the model provides crazy values when he adjusts the number of echoes. That's it. Let's listen to @username_0 and avoid speculations.  
  
  ---
  
  