def create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) 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(Flatten()) model.add(Dropout(0.5)) model.add(Reshape((X_train.shape[1], 64))) # 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，解决后续训练时 ValueError: Exception encountered when calling layer 'reshape_13' (type Reshape). total size of new array must be unchanged, input_shape = [10, 1, 1, 5], output_shape = [10, 1, 1, 1] Call arguments received by layer 'reshape_13' (type Reshape): • inputs=tf.Tensor(shape=(None, 10, 1, 1, 5), dtype=float32)问题

这个错误提示是由于在模型中的 `Reshape` 层输入和输出的形状不匹配导致的。具体来说，输出形状为 `[10, 1, 1, 1]`，但是输入形状为 `[10, 1, 1, 5]`，因此需要修改 `Reshape` 层的输出形状。

你可以尝试将第一次 `Reshape` 层的输出形状修改为 `[X_train.shape[1], 1, X_train.shape[2] * 64]`，即将前两个维度保持不变，将第三个维度变成 `X_train.shape[2] * 64`。这样做的目的是将 `ConvLSTM2D` 层的输出展平后，再将最后一个维度与 `LSTM` 层的输入维度相匹配。

修改后的代码如下所示：

def create_LSTM_model():
    # instantiate the model
    model = Sequential()
    model.add(Input(shape=(X_train.shape[1], X_train.shape[2])))
    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(Flatten())
    model.add(Dropout(0.5))
    model.add(Reshape((X_train.shape[1], 1, X_train.shape[2] * 64)))

    # 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())

你可以尝试运行修改后的代码，看看是否解决了问题。