def create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) # cnn1d Layers model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True)) model.add(Dropout(0.5)) # 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 "conv_lstm2d_14" is incompatible with the layer: expected ndim=5, found ndim=3. Full shape received: (None, 10, 1)问题

该错误是由于输入数据的维度不匹配导致的。ConvLSTM2D层期望输入的数据维度为5，而当前的输入数据维度为3。因此，需要将输入数据的维度转换为正确的形状。

可以通过在输入层之前添加一个Reshape层来实现这一点，将原始输入从（None，10，1）转换为（None，10，1，1，1）。

修改代码如下：

def create_LSTM_model(): 
    # instantiate the model 
    model = Sequential() 
    model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) 
    # reshape the input to match the expected input shape of the ConvLSTM2D layer 
    model.add(Reshape((X_train.shape[1], 1, X_train.shape[2], 1))) 
    # cnn1d Layers 
    model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True)) 
    model.add(Dropout(0.5)) 
    # 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())

这样就可以避免ValueError错误了。