609a73ddcee8d3a12a9ae2c7f6e06aac6013c422,cnn_text_classification.py,CNNClassifier,__eval,#CNNClassifier#Any#,57

Before Change

            F.cross_entropy(logit, target, reduction="sum")

            predictions = torch.max(logit, 1)[1].view(target.size())
            corrects += (predictions.data == target.data).sum()

        return 100.0 * corrects / len(data_iter.dataset)

    def fit(self, X, y, sample_weight=None):
        start = time() if self.verbose > 0 else None
        train_iter, dev_iter = self.__preprocess(X, y, sample_weight)

After Change

        self.__model.eval()

        preds = []
        targets = []

        for batch in data_iter:
            feature, target = batch.text, batch.label

            feature.data.t_()
            target.data.sub_(1)

            if self.cuda and torch.cuda.is_available():
                feature, target = feature.cuda(), target.cuda()

            logit = self.__model(feature)

            F.cross_entropy(logit, target, reduction="sum")

            preds += torch.max(logit, 1)[1].view(target.size()).data.tolist()
            targets += target.data.tolist()

        return self.scoring(targets, preds)

    def fit(self, X, y, sample_weight=None):
        start = time() if self.verbose > 0 else None
        train_iter, dev_iter = self.__preprocess(X, y, sample_weight)

Instances