Body: <p>I am training a modified VGG16 network for classification (adding 0.5 dropout after each of the last FC layers). In the following plot I am training for a small number of epochs as an example, and it shows the accuracy and loss curves of training process on both training and validation datasets. My training set size is <span class="math-container">$1725$</span>, and <span class="math-container">$429$</span> for validation. Also I am training with <code>weights=None</code></p>
  
  <p><a href="https://i.sstatic.net/ZZGbM.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ZZGbM.png" alt="enter image description here"></a></p>
  
  <p>My question is about the validation curves, why do not they appear to be as smooth as the training ones? Is this normal during the training stage?</p>
  
Tags: <neural-networks><convolutional-neural-networks><training>
Title: Why are not validation accuracy and loss as smooth as train accuracy and loss?
UserDisplayName: norahik
ThreadText: # Why are not validation accuracy and loss as smooth as train accuracy and loss?
  
  (Asked by: username_0 on 2019-04-10)
  
  I am training a modified VGG16 network for classification (adding 0.5 dropout after each of the last FC layers). In the following plot I am training for a small number of epochs as an example, and it shows the accuracy and loss curves of training process on both training and validation datasets. My training set size is $1725$, and $429$ for validation. Also I am training with `weights=None`
  
  [![enter image description here](https://i.sstatic.net/ZZGbM.png)](https://i.sstatic.net/ZZGbM.png)
  
  My question is about the validation curves, why do not they appear to be as smooth as the training ones? Is this normal during the training stage?
  
  **Tags:** neural-networks, convolutional-neural-networks, training
  
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  ## 0 answers
  
  