LSTM class, got error: TypeError: init() got an unexpected keyword argument 'input_shape'

Question

class LSTM:

    scaler_y_train= MinMaxScaler(feature_range=(0,1))
    scaler_x_train= MinMaxScaler(feature_range=(0,1))
    scaler_forecast= MinMaxScaler(feature_range=(0,1))

    def __init__(self, filename, seq_len, forecast_size):
        self._filename= filename
        self._seq_len= seq_len
        self._forecast_size= forecast_size
        self.model= Sequential()


    def load_y_train(self):
        raw_data_values= self._filename.values
        raw_data= self._filename
        print('Initializing the data loading...')
        forecast_out_variable= math.ceil(len(raw_data_values)* self._forecast_size)
        forecast_out_fixed= None

        print('Creating y_train...')
        y= raw_data.iloc[forecast_out_variable:].values
        len_y= len(y)
        data_windows_y= []
        for i in range(len_y - self._seq_len):
            data_windows_y.append(y[i: i + self._seq_len])
        data_windows_y= np.array(data_windows_y).astype(float)
        y_train= data_windows_y[:, -1, [0]]
        return print(y_train.shape), y_train

    def load_x_train(self):
        raw_data_values= self._filename.values
        raw_data= self._filename
        print('Initializing the data loading...')
        forecast_out_variable= math.ceil(len(raw_data_values)* self._forecast_size)
        forecast_out_fixed= None

        print('Creating x_train...')
        x= raw_data.iloc[:-forecast_out_variable].values
        len_x= len(x)
        data_windows_x= []
        for i in range(len_x - self._seq_len):
            data_windows_x.append(x[i: i + self._seq_len])
        data_windows_x= np.array(data_windows_x).astype(float)
        x_train= data_windows_x[:, :-1]
        return x_train

    def load_x_forecast(self):
        raw_data_values= self._filename.values
        raw_data= self._filename
        print('Initializing the data loading...')
        forecast_out_variable= math.ceil(len(raw_data_values)* self._forecast_size)
        forecast_out_fixed= None

        print('Creating x_train...')
        x_forecast = raw_data.iloc[:-forecast_out_variable].values
        len_x_forecast= len(x_forecast)
        data_windows_x= []
        for i in range(len_x_forecast - self._seq_len):
            data_windows_x.append(x_forecast[i: i + self._seq_len])
        data_windows_x= np.array(data_windows_x).astype(float)
        xx_forecast= data_windows_x[:, :-1]
        return print(xx_forecast.shape), xx_forecast

    def build_model(self):
        x_train= self.load_x_train()
        x_train= np.array(x_train).reshape(1050,49,2).astype(float)
        print(x_train.shape)
        print('Model starting compiling...')
        start= time.time()

        self.model.add(LSTM(50, input_shape=(x_train.shape[1], x_train.shape[-1]), 
        return_sequences=True))
        self.model.add(Dropout(0.2))
        self.model.add(LSTM(100, return_sequences=False))
        self.model.add(Dropout(0.2))
        self.model.add(Dense(1, activation = "linear"))
        self.model.compile(loss='mse', optimizer='adam')
        end= time.time()
        print ('model compiled in: ' +str((end-start)*1000)+ ' ms')
        return model, print(model.summary())




lstm_01= LSTM(ms_unreal, 50, 0.1)
print(lstm_01.load_x_forecast())
print(lstm_01.load_y_train())
print(lstm_01.load_x_train())
print(lstm_01.build_model(model))

类型错误是 init() 得到了一个意外的关键字参数 'input_shape'，我不知道如何让它工作，因为我正在正确调用库。我在网上搜索了这个问题，但我无法理解这个问题。该代码是一个名为 LSTM 的 class，其中基本上第一个函数已完成以获取训练和测试数据，而最后一个函数（存在问题）是初始化 LSTM keras 模型。

Answer 1

您的 class 称为 LSTM，您从 Keras 调用一个名为 LSTM 的函数。尝试重命名您的 class My_LSTM 或某些变体。否则，您将无法在不覆盖 Keras 实现的情况下调用 class。

LSTM class, got error: TypeError: __init__() got an unexpected keyword argument 'input_shape'

LSTM class, got error: TypeError: __init__() got an unexpected keyword argument 'input_shape'

class

init

keyword

lstm

LSTM class, got error: TypeError: init() got an unexpected keyword argument 'input_shape'

LSTM class, got error: TypeError: init() got an unexpected keyword argument 'input_shape'