It is my understanding that you want to know whether your model is underfit or if it is not, then why training and validation loss are not converging.
“Underfitting occurs when the model is not able to obtain a sufficiently low error value on the training set.” – Deep Learning, by Ian Goodfellow
On seeing the graph, training and validation loss curves have low values. So, we can say model is not underfit.
In graph, validation loss is less than training loss because of the following reasons:
- Validation dataset is easier to learn as compared to training dataset. So, check whether validation dataset follows same distribution as training dataset.
- Regularization: Dropout is applied during training only. It helps in achieving better generalization on unseen datasets.
The reason for both validation and training never converge and remain offset could be that the model is not learning after certain epochs. You could try to experiment with hyperparameters like learning rate, no. of layers, dropout layer probability etc.