我应该只对Keras使用标量标签吗？

Question

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，对标签使用长度为5的数组。但当我用：

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中给出了这个错误：

ValueError:检查目标时出错:期望dense_1具有二维，但得到形状为(1110，5，2) 在5.1中完成，退出代码为1的数组

如果我将y_train的长度更改为1，则代码可以工作，但我喜欢测试较长的y标签以进行训练。有什么问题，我怎么解决呢？

编辑:我使用以下代码创建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model.summary()的结果

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:我尝试用以下代码解决RepeatVector()函数在encoder-decoder方法中的问题：

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))

但我得到了一个愚蠢的结果：

Answer 1

您能否使用以下方法发布模型摘要：

model.summary()

此外，详细说明Y_train数据集与X_train的具体工作方式？目前尚不清楚来自X_train数据的25个时间步骤如何与Y_train 5输出相对应。