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())修改该代码，解决ValueError: Input 0 of layer "sequential_76" is incompatible with the layer: expected shape=(None, 10, 5), found shape=(None, 10, 1, 1, 5)问题

可以尝试将代码中的以下两行：

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)))

修改为：

model.add(Input(shape=(X_train.shape[1], X_train.shape[2], 5)))

这样就不需要加入 Reshape 层了，因为输入的数据已经是 (batch_size, timesteps, features) 的形状，可以直接作为 LSTM 层的输入。