train.py 文件源码

def validate(models, dataset, arg, cuda=False):
    criterion = nn.MSELoss()
    losses = []
    batcher = dataset.get_batcher(shuffle=True, augment=False)
    for b, (x, y) in enumerate(batcher, 1):
        x = V(th.from_numpy(x).float()).cuda()
        y = V(th.from_numpy(y).float()).cuda()
        # Ensemble average
        logit = None
        for model, _ in models:
            model.eval()
            logit = model(x) if logit is None else logit + model(x)
        logit = th.div(logit, len(models))
        loss = criterion(logit, y)
        losses.append(loss.data[0])
    return np.mean(losses)