from sklearn.model_selection import train_test_split ppd = PrepareData() data = ppd.get_data() lstm = LSTMClassifier() X = lstm.get_matrix(data) Y = pd.get_dummies(data['label']).values X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42) print(X_train.shape, Y_train.shape) print(X_test.shape, Y_test.shape) model = lstm.get_model(X.shape[1]) history = lstm.fit_model(model, X_train, Y_train) validation_size = 1500 X_validate = X_test[-validation_size:] Y_validate = Y_test[-validation_size:] X_test = X_test[:-validation_size] Y_test = Y_test[:-validation_size] score, acc = model.evaluate(X_test, Y_test, verbose=2, batch_size=batch_size) print("score: %.2f" % (score)) print("acc: %.2f" % (acc)) pos_cnt, neg_cnt, pos_correct, neg_correct = 0, 0, 0, 0 for x in range(len(X_validate)): result = model.predict(X_validate[x].reshape(1, X_test.shape[1]),