def update()

in domainbed/algorithms.py [0:0]

• 20 lines of code
• 8 McCabe index (conditional complexity)

    def update(self, minibatches, unlabeled=None):
        objective = 0
        penalty = 0
        nmb = len(minibatches)

        features = [self.featurizer(xi) for xi, _ in minibatches]
        classifs = [self.classifier(fi) for fi in features]
        targets = [yi for _, yi in minibatches]

        for i in range(nmb):
            objective += F.cross_entropy(classifs[i], targets[i])
            for j in range(i + 1, nmb):
                penalty += self.mmd(features[i], features[j])

        objective /= nmb
        if nmb > 1:
            penalty /= (nmb * (nmb - 1) / 2)

        self.optimizer.zero_grad()
        (objective + (self.hparams['mmd_gamma']*penalty)).backward()
        self.optimizer.step()

        if torch.is_tensor(penalty):
            penalty = penalty.item()

        return {'loss': objective.item(), 'penalty': penalty}