ladder.py 文件源码

def evaluate_performance(ladder, valid_loader, e, agg_cost_scaled, agg_supervised_cost_scaled,
                         agg_unsupervised_cost_scaled, args):
    correct = 0.
    total = 0.
    for batch_idx, (data, target) in enumerate(valid_loader):
        if args.cuda:
            data = data.cuda()
        data, target = Variable(data), Variable(target)
        output = ladder.forward_encoders_clean(data)
        # TODO: Do away with the below hack for GPU tensors.
        if args.cuda:
            output = output.cpu()
            target = target.cpu()
        output = output.data.numpy()
        preds = np.argmax(output, axis=1)
        target = target.data.numpy()
        correct += np.sum(target == preds)
        total += target.shape[0]

    print("Epoch:", e + 1, "\t",
          "Total Cost:", "{:.4f}".format(agg_cost_scaled), "\t",
          "Supervised Cost:", "{:.4f}".format(agg_supervised_cost_scaled), "\t",
          "Unsupervised Cost:", "{:.4f}".format(agg_unsupervised_cost_scaled), "\t",
          "Validation Accuracy:", correct / total)