5c177f3d9a29fc7737bd4734315820d1c11c7e87,cnn_text_classification.py,CNNClassifier,predict,#CNNClassifier#Any#,156

Before Change

            self.__model.eval()

            text = [[self.__text_field.vocab.stoi[x] for x in text]]
            x = Variable(torch.tensor(text))
            x = x.cuda() if self.cuda and torch.cuda.is_available() else x
            _, predicted = torch.max(self.__model(x), 1)

            y_pred.append(self.__label_field.vocab.itos[predicted.data[0] + 1])

        torch.cuda.empty_cache()

After Change

        return y_output

    def predict(self, X):
        y_pred = [torch.argmax(yi, 1) for yi in self.__predict(X)]
        return [self.__label_field.vocab.itos[yi.data[0] + 1] for yi in y_pred]

    def predict_proba(self, X):
        softmax = nn.Softmax(dim=1)

Instances