demo_grec_duvenaud.py 文件源码

def validate(val_loader, model, criterion, evaluation, logger=None):
    losses = AverageMeter()
    accuracies = AverageMeter()

    # switch to evaluate mode
    model.eval()

    for i, (g, h, e, target) in enumerate(val_loader):

        # Prepare input data
        target = torch.squeeze(target).type(torch.LongTensor)
        if args.cuda:
            g, h, e, target = g.cuda(), h.cuda(), e.cuda(), target.cuda()
        g, h, e, target = Variable(g), Variable(h), Variable(e), Variable(target)

        # Compute output
        output = model(g, h, e)

        # Logs
        test_loss = criterion(output, target)
        acc = Variable(evaluation(output.data, target.data, topk=(1,))[0])

        losses.update(test_loss.data[0], g.size(0))
        accuracies.update(acc.data[0], g.size(0))

    print(' * Average Accuracy {acc.avg:.3f}; Average Loss {loss.avg:.3f}'
          .format(acc=accuracies, loss=losses))

    if logger is not None:
        logger.log_value('test_epoch_loss', losses.avg)
        logger.log_value('test_epoch_accuracy', accuracies.avg)

    return accuracies.avg