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                # 使用 Keras 的 LSTM 創建 LSTM 模型只需添加 LSTM 層而不是 SimpleRNN 層,如下所示: ```py model.add(LSTM(units=4, input_shape=(X_train.shape[1], X_train.shape[2]))) ``` 模型結構如下所示: ```py _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= lstm_1 (LSTM) (None, 4) 96 _________________________________________________________________ dense_1 (Dense) (None, 1) 5 ================================================================= Total params: 101 Trainable params: 101 Non-trainable params: 0 _________________________________________________________________ ``` 筆記本 `ch-07b_RNN_TimeSeries_Keras` 中提供了 LSTM 模型的完整代碼。 由于 LSTM 模型具有更多需要訓練的參數,對于相同數量的迭代(20 個周期),我們得到更高的誤差分數。我們留給您探索周期和其他超參數的各種值,以獲得更好的結果: ```py Train Score: 32.21 RMSE Test Score: 84.68 RMSE ``` ![](https://img.kancloud.cn/f8/5a/f85a12af2372baafadb51b7479f6f7bf_923x610.png)
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