ss_vae_M2.py 文件源码

def get_accuracy(data_loader, classifier_fn, batch_size):
    """
    compute the accuracy over the supervised training set or the testing set
    """
    predictions, actuals = [], []

    # use the appropriate data loader
    for (xs, ys) in data_loader:
        # use classification function to compute all predictions for each batch
        xs, ys = Variable(xs), Variable(ys)
        predictions.append(classifier_fn(xs))
        actuals.append(ys)

    # compute the number of accurate predictions
    accurate_preds = 0
    for pred, act in zip(predictions, actuals):
        for i in range(pred.size(0)):
            v = torch.sum(pred[i] == act[i])
            accurate_preds += (v.data[0] == 10)

    # calculate the accuracy between 0 and 1
    accuracy = (accurate_preds * 1.0) / (len(predictions) * batch_size)
    return accuracy