Nishanth_Sasankan:

I am not sure how I can visualize training, validation loss and accuracy during training. Also, how do I implement validation and validation loss during the training epoch?


Usually you would create a train function to train the model for a complete epoch using the training dataset. Afterwards you could run the evaluation on the validation dataset.
Have a look at the ImageNet exmple 2 to see how train and validate are defined.
The same code uses this code 1 to print the progress.



 Nishanth_Sasankan:

How do I know if the model is overfitting for one of the class? Also, how do I correct for it?


One signal of overfitting is when you observe a decreasing training loss and a static or increasing validation loss.
Further training at this point usually won’t give you any benefits and you would stop at this point.
You would counter it by e.g.

using (or collecting) more data
remove “capacity” of the model, e.g. by reducing the number of parameters
add regularization (e.g. dropout)

You can find way more information and a better explanation in Chapter 5, DeepLearningBook 1.