# Why are not validation accuracy and loss as smooth as train accuracy and loss?

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

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?