What is the meaning of this type of loss and accuracy graph?

username: sampa
cooked: <p>Here sometimes validation loss is less thain training loss.<br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/c/c/ccc3b67492e56b5373cf243549d113cc46e09da4.png" data-download-href="https://discuss.pytorch.org/uploads/default/ccc3b67492e56b5373cf243549d113cc46e09da4" title="loss_plotAlex"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/c/c/ccc3b67492e56b5373cf243549d113cc46e09da4_2_690x462.png" alt="loss_plotAlex" data-base62-sha1="tdqIqlJanBGZgNQeFQCMNr1TK2U" width="690" height="462" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/c/c/ccc3b67492e56b5373cf243549d113cc46e09da4_2_690x462.png, https://discuss.pytorch.org/uploads/default/optimized/3X/c/c/ccc3b67492e56b5373cf243549d113cc46e09da4_2_1035x693.png 1.5x, https://discuss.pytorch.org/uploads/default/original/3X/c/c/ccc3b67492e56b5373cf243549d113cc46e09da4.png 2x" data-dominant-color="F6F0EA"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">loss_plotAlex</span><span class="informations">1175×788 116 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div> <div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/4/5/4572286a1ffae2ff1f9729c7dcc4584b03b97020.png" data-download-href="https://discuss.pytorch.org/uploads/default/4572286a1ffae2ff1f9729c7dcc4584b03b97020" title="accplotAlex"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/4/5/4572286a1ffae2ff1f9729c7dcc4584b03b97020_2_624x500.png" alt="accplotAlex" data-base62-sha1="9UlwHYeCQ855OUspIfJY8AVXChO" width="624" height="500" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/4/5/4572286a1ffae2ff1f9729c7dcc4584b03b97020_2_624x500.png, https://discuss.pytorch.org/uploads/default/original/3X/4/5/4572286a1ffae2ff1f9729c7dcc4584b03b97020.png 1.5x, https://discuss.pytorch.org/uploads/default/original/3X/4/5/4572286a1ffae2ff1f9729c7dcc4584b03b97020.png 2x" data-dominant-color="F4EEE8"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">accplotAlex</span><span class="informations">846×677 72.5 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>

username: mailcorahul
cooked: <p>From the graph, it seems your model is training without any issues. Both train and val accuracy is constantly increasing except after epoch &gt; 1400. You can pretty much use the weights near epoch 1400.</p>

username: ptrblck
cooked: <p>Additionally to what <a class="mention" href="/u/mailcorahul">@mailcorahul</a> explained:<br>
the validation loss might be lower than the training loss, e.g. if:</p>
<ul>
<li>dropout is used and the training model has less capacity than the validation model</li>
<li>your data split is wrong and you have more “simple” examples in your validation set</li>
</ul>

username: sampa
cooked: <p>Thank u for your reply.</p>

username: sampa
cooked: <p><a class="mention" href="/u/ptrblck">@ptrblck</a> <a class="mention" href="/u/mailcorahul">@mailcorahul</a> I am using Alexnet model. How to solve the problem (validation loss is lower than training loss)?</p>

username: ptrblck
cooked: <p>What happens if you disable the <code>Dropout</code> layers and have you checked the data distribution of your train and validation set?</p>

username: sampa
cooked: <p><a class="mention" href="/u/ptrblck">@ptrblck</a> I created 3 folder for train, val and test and randomly select 60% of image and keep it in train, 20% for val and 20% test.<br>
How to disable dropout layers?<br>
This is my code</p>
<pre><code class="lang-auto">feature_extract = True
sm = nn.Softmax(dim = 1)
def set_parameter_requires_grad(model, feature_extracting):
    if feature_extracting:
        for param in model.parameters():
            param.requires_grad = True    
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
    if model_name == "alexnet":
        """ Alexnet
        """
        model= models.alexnet(pretrained=use_pretrained)
        set_parameter_requires_grad(model, feature_extract)
        num_ftrs = model.classifier[6].in_features
        model.classifier[6] = nn.Linear(num_ftrs,num_classes)
        input_size = 224
        

    return model
model = initialize_model(model_name, num_classes, feature_extract, use_pretrained=True)
</code></pre>