CONST_TRAINTING_SEQUENCE_LENGTH = 12CONST_TESTING_CASES = 5def dataNormalization(data):    return [(datum - data[0]) / data[0] for datum in data]def dataDeNormalization(data, base):    return [(datum + 1) * base for datum in data]def getDeepLearningData(ticker):    # Step 1. Load data    data = pandas.read_csv('/Users/yindeyong/Desktop/Django_Projects/pythonstock/data/Intraday/' + ticker + '.csv')[        'close'].tolist()    # Step 2. Building Training data    dataTraining = []    for i in range(len(data) - CONST_TESTING_CASES * CONST_TRAINTING_SEQUENCE_LENGTH):        dataSegment = data[i:i + CONST_TRAINTING_SEQUENCE_LENGTH + 1]        dataTraining.append(dataNormalization(dataSegment))    dataTraining = numpy.array(dataTraining)    numpy.random.shuffle(dataTraining)    X_Training = dataTraining[:, :-1]    Y_Training = dataTraining[:, -1]    # Step 3. Building Testing data    X_Testing = []    Y_Testing_Base = []    for i in range(CONST_TESTING_CASES, 0, -1):        dataSegment = data[-(i + 1) * CONST_TRAINTING_SEQUENCE_LENGTH:-i * CONST_TRAINTING_SEQUENCE_LENGTH]        Y_Testing_Base.append(dataSegment[0])        X_Testing.append(dataNormalization(dataSegment))    Y_Testing = data[-CONST_TESTING_CASES * CONST_TRAINTING_SEQUENCE_LENGTH:]    X_Testing = numpy.array(X_Testing)    Y_Testing = numpy.array(Y_Testing)    # Step 4. Reshape for deep learning    X_Training = numpy.reshape(X_Training, (X_Training.shape[0], X_Training.shape[1], 1))我有一個(gè)錯(cuò)誤：文件“/Users/yindeyong/Desktop/Django_Projects/envs/stockenv/lib/python3.6/site-packages/keras/engine/base_layer.py”，第147行，在init batch_size中，) + tuple(kwargs['input_shape' ]) TypeError: 'int' 對(duì)象不可迭代我試圖將input_shape=1更改 為input_shape=(1,)，然后又出現(xiàn)了另一個(gè)錯(cuò)誤：ValueError：輸入 0 與層 lstm_1 不兼容：預(yù)期 ndim=3，發(fā)現(xiàn) ndim=2