Perform CapsNet training

def do_training(num_epoch, optimizer, kvstore, learning_rate, model_prefix, decay):
    """Perform CapsNet training"""
    summary_writer = SummaryWriter(args.tblog_dir)
    lr_scheduler = SimpleLRScheduler(learning_rate)
    optimizer_params = {'lr_scheduler': lr_scheduler}
    module.init_params()
    module.init_optimizer(kvstore=kvstore,
                          optimizer=optimizer,
                          optimizer_params=optimizer_params)
    n_epoch = 0
    while True:
        if n_epoch >= num_epoch:
            break
        train_iter.reset()
        val_iter.reset()
        loss_metric.reset()
        for n_batch, data_batch in enumerate(train_iter):
            module.forward_backward(data_batch)
            module.update()
            module.update_metric(loss_metric, data_batch.label)
            loss_metric.get_batch_log(n_batch)
        train_acc, train_loss, train_recon_err = loss_metric.get_name_value()
        loss_metric.reset()
        for n_batch, data_batch in enumerate(val_iter):
            module.forward(data_batch)
            module.update_metric(loss_metric, data_batch.label)
            loss_metric.get_batch_log(n_batch)
        val_acc, val_loss, val_recon_err = loss_metric.get_name_value()

        summary_writer.add_scalar('train_acc', train_acc, n_epoch)
        summary_writer.add_scalar('train_loss', train_loss, n_epoch)
        summary_writer.add_scalar('train_recon_err', train_recon_err, n_epoch)
        summary_writer.add_scalar('val_acc', val_acc, n_epoch)
        summary_writer.add_scalar('val_loss', val_loss, n_epoch)
        summary_writer.add_scalar('val_recon_err', val_recon_err, n_epoch)

        print('Epoch[%d] train acc: %.4f loss: %.6f recon_err: %.6f' % (n_epoch, train_acc, train_loss,
                                                                        train_recon_err))
        print('Epoch[%d] val acc: %.4f loss: %.6f recon_err: %.6f' % (n_epoch, val_acc, val_loss, val_recon_err))
        print('SAVE CHECKPOINT')

        module.save_checkpoint(prefix=model_prefix, epoch=n_epoch)
        n_epoch += 1
        lr_scheduler.learning_rate = learning_rate * (decay ** n_epoch)