数据挖掘 - 我应该只在 Keras LSTM 中使用标量标签吗？ - 吾爱随笔录

我有一个数组X_train = (1110,25,2)和一个y_train = (1110,5,2). 这意味着我使用长度为 25 的数组作为输入，长度为 5 的数组作为标签。但是当我使用：

model = Sequential()
model.add(LSTM(units = 25, return_sequences = True, input_shape = (25, 2))) 
model.add(Dropout(0.2))
model.add(Dense(units = 2))
model.compile(optimizer = 'adam', loss = 'mean_squared_error')
model.fit(X_train, y_train, epochs = 100 , batch_size = 25)

它在代码的最后一行给了我这个错误：

ValueError：检查目标时出错：预期dense_1有2维，但得到了形状为（1110、5、2）的数组[在5.1秒内完成，退出代码为1]

如果我将长度更改为y_train1，则该代码有效，但我喜欢测试更长的 y 标签来训练。有什么问题，我该如何解决？

编辑：我用这段代码创建X_train和y_train数组：

for i in range((len(training_set)%30) + 30 , len(training_set) - days ):
    X_train.append(training_set_scaled[i-30:i-5])
    y_train.append(training_set_scaled[i-5:i])
X_train, y_train = np.array(X_train), np.array(y_train)

这是结果model.summary()

model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_45 (LSTM)               (None, 25, 25)            2800      
_________________________________________________________________
dropout_45 (Dropout)         (None, 25, 25)            0         
_________________________________________________________________
lstm_46 (LSTM)               (None, 25, 25)            5100      
_________________________________________________________________
dropout_46 (Dropout)         (None, 25, 25)            0         
_________________________________________________________________
lstm_47 (LSTM)               (None, 25, 25)            5100      
_________________________________________________________________
dropout_47 (Dropout)         (None, 25, 25)            0         
_________________________________________________________________
lstm_48 (LSTM)               (None, 25)                5100      
_________________________________________________________________
dropout_48 (Dropout)         (None, 25)                0         
_________________________________________________________________
dense_12 (Dense)             (None, 2)                 52        
=================================================================
Total params: 18,152
Trainable params: 18,152
Non-trainable params: 0
_________________________________________________________________

EDIT2：我尝试使用以下代码解决我的RepeatVector()功能问题：encoder-decoder

model = Sequential()
model.add(LSTM(units = 25, return_sequences = True, input_shape = (25, 2))) 
model.add(Dropout(0.2))
model.add(LSTM(units = 25, return_sequences = True))
model.add(Dropout(0.2))
model.add(LSTM(units = 25)) #, return_sequences = True))
model.add(Dropout(0.2))
model.add(RepeatVector(5))
model.add(LSTM(units = 5 ,return_sequences = True)) 
model.add(Dropout(0.2))
model.add(LSTM(units = 5 ,return_sequences = True )) 
model.add(Dropout(0.2))
model.add(Dense(units = 2))

但我得到了这个愚蠢的结果：