Body: <p>I am training a neural network which produces the following errors (epoch number on the x axis). I have some questions regrading interpreting it.</p>
  
  <ul>
  <li><p>When I say <code>model.predict</code> is it giving me the result based on the final state (that is epoch 5000)?</p></li>
  <li><p>Towards the end (and some places in the middle) there are places where the training error and validation error are farther apart. Does this mean that the model was over-fitting on those epochs?</p></li>
  <li><p>Based on the graph, can one determine that the model was <code>best</code> at a certain epoch? </p></li>
  <li><p>Does Keras have API methods to retrieve the model at a specific epoch so that I can retrieve the <code>best</code> model?</p></li>
  </ul>
  
  <p><a href="https://i.sstatic.net/jKlil.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/jKlil.png" alt="enter image description here"></a></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-09T14:56:23.987Z
Comments.Text: @DuttaA thank you for your comment. Can you please elaborate? Also looking at the train error,there is a wave like pattern. Would you not say that the wave like pattern indicates that the model is learning (that is it was performing best when the wave was at a minimum?)
Comments.UserDisplayName: Can't Tell
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-10T09:21:43.140Z
Comments.Text: @DuttaA loss, as drawn in this example https://www.tensorflow.org/tutorials/keras/basic_regression, although the data is not the same. I just took the graph code from there
Comments.UserDisplayName: Can't Tell
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-11T15:11:42.957Z
Comments.Text: do you do measures agains over-fitting or maybe reduce leraning rate with the time?
Comments.UserDisplayName: user8426627
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-12T11:02:50.373Z
Comments.Text: @user8426627 I use Adam optimizer and I use Dropout
Comments.UserDisplayName: Can't Tell
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-12T11:08:07.937Z
Comments.Text: thats weird because even train error dont converge. Make learning rate smaller and try to run over small amount of epochs
Comments.UserDisplayName: user8426627
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-12T11:21:30.993Z
Comments.Text: @user8426627 thanks for the suggestion. I will try that
Comments.UserDisplayName: Can't Tell
Tags: <deep-learning><keras>
Title: How can I interpret the following error graph?
UserDisplayName: Can't Tell
ThreadText: # How can I interpret the following error graph?
  
  (Asked by: username_0 on 2019-06-09)
  
  I am training a neural network which produces the following errors (epoch number on the x axis). I have some questions regrading interpreting it.
  
  * When I say `model.predict` is it giving me the result based on the final state (that is epoch 5000)?
  * Towards the end (and some places in the middle) there are places where the training error and validation error are farther apart. Does this mean that the model was over-fitting on those epochs?
  * Based on the graph, can one determine that the model was `best` at a certain epoch?
  * Does Keras have API methods to retrieve the model at a specific epoch so that I can retrieve the `best` model?
  
  [![enter image description here](https://i.sstatic.net/jKlil.png)](https://i.sstatic.net/jKlil.png)
  
  **Tags:** deep-learning, keras
  > **Comments:**
  > **username_0**: @DuttaA thank you for your comment. Can you please elaborate? Also looking at the train error,there is a wave like pattern. Would you not say that the wave like pattern indicates that the model is learning (that is it was performing best when the wave was at a minimum?)  
  > **username_0**: @DuttaA loss, as drawn in this example https://www.tensorflow.org/tutorials/keras/basic_regression, although the data is not the same. I just took the graph code from there  
  > **username_1**: do you do measures agains over-fitting or maybe reduce leraning rate with the time?  
  > **username_0**: @user8426627 I use Adam optimizer and I use Dropout  
  > **username_1**: thats weird because even train error dont converge. Make learning rate smaller and try to run over small amount of epochs  
  > **username_0**: @user8426627 thanks for the suggestion. I will try that  
  
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  ## 0 answers
  
  