Answers.Body: <p>Simply <code>model 1</code> is a better fit compared to <code>model 2</code>.</p>
  
  <ul>
  <li><p>Graph for model 1</p>
  
  <p>We notice that the training loss and validation loss aren't correlated. This means the as the training loss is decreasing, the validation loss remains the same of increases over the iterations. This means that the model is not exactly improving, but is instead overfitting the training data. This isn't what we are looking for.</p></li>
  <li><p>Graph for model 2</p>
  
  <p>In this case, there is clearly a health correlation between training loss and the validation loss. They both seem to reduce and stay at a constant value. This means that the model is well trained and is equally good on the training data as well as the hidden data.</p></li>
  </ul>
  
  <p>You should stick with model 2. In case you're going ahead with model 1, make sure to use the chechpoint where both the losses are at a similar value (at around 100 -150 epochs)</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-06-24T05:26:51.697Z
Answers.Text: In graph 2, is there any chance of overfitting?
Answers.UserDisplayName: Majo_Jose
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-06-21T20:36:21.117Z
Answers.LastActivityDate: 2019-06-21T20:36:21.117Z
Answers.UserDisplayName: skillsmuggler
Body: <p>Here I am Showing Two Loss graphs of an Artificial Neural Network.</p>
  
  <p><strong>Model 1</strong> </p>
  
  <p><a href="https://i.sstatic.net/WgBhA.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/WgBhA.png" alt="enter image description here"></a></p>
  
  <p><strong>Model 2</strong></p>
  
  <p><a href="https://i.sstatic.net/oghdm.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/oghdm.png" alt="enter image description here"></a></p>
  
  <p><strong>Blue</strong> -training loss</p>
  
  <p><strong>Red</strong>  -val training loss</p>
  
  <p>Can you help me to analyse these graphs? I read some articles and post but doesn't give me any sense.</p>
  
Tags: <neural-networks><deep-learning><objective-functions>
Title: Analysis of Training Loss and Validation Loss Graph
UserDisplayName: Majo_Jose
ThreadText: # Analysis of Training Loss and Validation Loss Graph
  
  (Asked by: username_0 on 2019-06-21)
  
  Here I am Showing Two Loss graphs of an Artificial Neural Network.
  
  **Model 1**
  
  [![enter image description here](https://i.sstatic.net/WgBhA.png)](https://i.sstatic.net/WgBhA.png)
  
  **Model 2**
  
  [![enter image description here](https://i.sstatic.net/oghdm.png)](https://i.sstatic.net/oghdm.png)
  
  **Blue** -training loss
  
  **Red** -val training loss
  
  Can you help me to analyse these graphs? I read some articles and post but doesn't give me any sense.
  
  **Tags:** neural-networks, deep-learning, objective-functions
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-06-21. Score: 3 | Accepted answer
  
  Simply `model 1` is a better fit compared to `model 2`.
  
  * Graph for model 1
  
    We notice that the training loss and validation loss aren't correlated. This means the as the training loss is decreasing, the validation loss remains the same of increases over the iterations. This means that the model is not exactly improving, but is instead overfitting the training data. This isn't what we are looking for.
  * Graph for model 2
  
    In this case, there is clearly a health correlation between training loss and the validation loss. They both seem to reduce and stay at a constant value. This means that the model is well trained and is equally good on the training data as well as the hidden data.
  
  You should stick with model 2. In case you're going ahead with model 1, make sure to use the chechpoint where both the losses are at a similar value (at around 100 -150 epochs)
  
  > **Comments:**
  > **username_0**: In graph 2, is there any chance of overfitting?  
  
  ---
  
  