recurrent neural networks #define optimizerloss function optimizer torch optim adam(model parameters()lr= - criterion nn bcewithlogitsloss(#transfer components to gpuif available model model to(devicecriterion criterion to(devicefinallyin listing - we train the model instantiated above with the define loss functions and optimizer in loop for five epochs we define here for illustration purposes onlyfor practical examples we recommend increasing the number of epochs based on the size of data and complexity of the network listing - training the model for five epochs n_epochs for epoch in range(n_epochs)#train and evaluate train_losstrain_acc,train_num train(modeltrain_ iteratoroptimizercriterionvalid_lossvalid_acc,val_num evaluate(modelval_ iterator,criterionprint("epoch-",epochprint( '\ttrain loss{train_loss ftrain predicted correct {train_acctrain denom{train_numpercaccuracy{train_acc/train_num}'print( '\tvalid loss{valid_loss fvalid predicted correct{valid_accval denom{val_num}percaccuracy{train_acc/train_num}'