for text in data_loader: out = model(text) pred.append(out) pred = torch.stack(pred) pred = list(np.array(pred.detach())) for i in range(len(pred)): str1 = str(int(pred[i][0])) + ' ' + str(d[i]) f.write(str1) f.write('\n') if __name__ == "__main__": epochs = 10 d = DataLoader(batch_size=64) text_tr, labels_tr, maxLen_tr = d.load_data('train') v = d.createVocab(text_tr) enc_data_tr = d.padText(text_tr, maxLen_tr, v) train_loader = d.createTensor(enc_data_tr, labels_tr) train_acc, model = train_main(train_loader, v, epochs) text_ts, labels_ts, maxLen_ts = d.load_data('test') enc_data_ts = d.padText(text_ts, maxLen_ts, v) test_loader = d.createTensor(enc_data_ts, labels_ts, batch_size=1) test_acc, pred, test_labels = evaluate(test_loader, model) print("The training accuracy is:") print(train_acc) print("The testing accuracy is:") print(test_acc)