Append epoch rather than best val. loss to val_loss

**Problem**
Currently, we're val_loss.append(best_val_loss) in each epoch. This is misleading because we're appending the corresponding epoch (not best across epochs) quantities in train_loss, train_prep, and val_prep. This is also inconvenient, as one often would like to plot both train and validation losses as a function of the epochs to look for overfitting.

**Solution**
val_loss.append(eval_epoch_loss)