def create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1], X_train.shape[2]*5))) model.add(Reshape((X_train.shape[1], 1,X_train.shape[2], 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())修改代码，解决ValueError: in user code: File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1284, in train_function * return step_function(self, iterator) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1268, in step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1249, in run_step ** outputs = model.train_step(data) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1050, in train_step y_pred = self(x, training=True) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\input_spec.py", line 298, in assert_input_compatibility raise ValueError( ValueError: Input 0 of layer "sequential_71" is incompatible with the layer: expected shape=(None, 10, 5), found shape=(None, 10, 1, 1, 5)

根据报错信息，可以看出是输入数据的shape出现了问题。在模型中，第一层输入的shape是(X_train.shape[1], X_train.shape[2]*5)，即(None, 10, 5)，但是实际传入的数据的shape为(None, 10, 1, 1, 5)，其中多了两维，因此需要修改代码中的Reshape层的参数，将其改为Reshape((X_train.shape[1], 1, X_train.shape[2]*5))，这样就可以将输入的数据reshape成正确的shape了。修改后的代码如下：

def create_LSTM_model():
    # instantiate the model
    model = Sequential()
    model.add(Input(shape=(X_train.shape[1], X_train.shape[2]*5)))
    model.add(Reshape((X_train.shape[1], 1, X_train.shape[2]*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())