vdef create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1],1,1, 5))) # cnn1d Layers model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True)) model.add(Flatten()) model.add(Dropout(0.5)) model.add(RepeatVector(1)) # 添加lstm层 model.add(LSTM(64, activation = 'relu', return_sequences=True)) model.add(Dropout(0.5)) #添加注意力层 model.add(LSTM(64, activation = 'relu', return_sequences=False)) # 添加dropout model.add(Dropout(0.5)) model.add(Dense(128)) # 输出层 model.add(Dense(1, name='Output')) # 编译模型 model.compile(optimizer='adam', loss='mse', metrics=['mae']) return model # lstm network model = create_LSTM_model() # summary print(model.summary())修改该代码，解决ValueError: Input 0 of layer "sequential_90" is incompatible with the layer: expected shape=(None, 10, 1, 1, 5), found shape=(None, 10, 5)问题

根据报错信息，可以看到模型期望的输入维度是`(None, 10, 1, 1, 5)`，但实际输入的维度是`(None, 10, 5)`。因此，需要修改输入层的维度。

可以使用`Reshape`层将输入数据的维度从`(None, 10, 5)`转换为`(None, 10, 1, 1, 5)`，代码如下：

def create_LSTM_model():     
    # instantiate the model           
    model = Sequential()     
    model.add(Input(shape=(X_train.shape[1], 5))) 
    model.add(Reshape((X_train.shape[1], 1, 1, 5)))  # 添加Reshape层，将输入数据的维度转换为(10, 1, 1, 5)
    
    # cnn1d Layers     
    model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True))     
    model.add(Flatten())     
    model.add(Dropout(0.5))     
    model.add(RepeatVector(1))       
    
    # 添加lstm层     
    model.add(LSTM(64, activation = 'relu', return_sequences=True))     
    model.add(Dropout(0.5))    
    
    #添加注意力层      
    model.add(LSTM(64, activation = 'relu', return_sequences=False))     
    
    # 添加dropout     
    model.add(Dropout(0.5))     
    model.add(Dense(128))            
    
    # 输出层     
    model.add(Dense(1, name='Output'))     
    
    # 编译模型     
    model.compile(optimizer='adam', loss='mse', metrics=['mae'])     
    return model 

# lstm network 
model = create_LSTM_model() 

# summary 
print(model.summary())

修改后，卷积层的输入维度为`(None, 10, 1, 1, 5)`，与模型期望的输入维度相符合。