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How to use the d2l.base.Accumulator function in d2l

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dsgiitr / d2l-pytorch / d2l / train.py View on Github

def evaluate_loss(net, data_iter, loss):
    """Evaluate the loss of a model on the given dataset"""
    metric = Accumulator(2)  # sum_loss, num_examples
    for X, y in data_iter:
        metric.add(loss(net(X), y).sum().detach().numpy().item(), list(y.shape)[0])
    return metric[0] / metric[1]