recurrent neural networks #finally lets train our model for epochs n_epochs for epoch in range(n_epochs)train_losstrain_acc,train_num train(modeltrain_ iteratoroptimizercriterionvalid_lossvalid_acc,val_num evaluate(modelval_ iteratorcriterionprint("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}'output[train loss train predicted correct train denom percaccuracy valid loss valid predicted correct val denom percaccuracy epoch train loss train predicted correct train denom percaccuracy valid loss valid predicted correct val denom percaccuracy