python - 使用 Keras 进行 LSTM 优化

问题描述

更新：感谢 Catalina 抽出宝贵时间

我按照您的建议做了 1- 我将数据拆分为训练和验证，并将其添加到合适的位置

history = model_final.fit(x_train_multi,
                      y_train_multi,
                      batch_size=BATCH_SIZE,
                      epochs=EPOCHS,
                      validation_data=(x_val_multi, y_val_multi),
                      callbacks=[tensorboard_callback,model_checkpoint_callback]
                      )

我还创建了检查点并添加到回调中

checkpoint_filepath = 'checkpoint_model/'
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_filepath,
    save_weights_only=True,
    monitor='mse',
    mode='min',
    save_best_only=True)

3-我还尝试添加 dropout，添加更多 LSTM 层并增加每层的单元数

    ________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_50 (LSTM)               (None, 128)               67072     
_________________________________________________________________
activation_19 (Activation)   (None, 128)               0         
_________________________________________________________________
dense_48 (Dense)             (None, 256)               33024     
_________________________________________________________________
dense_49 (Dense)             (None, 7)                 1799      
=================================================================
val_mse: 0.036


_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_51 (LSTM)               (None, 128)               67072     
_________________________________________________________________
batch_normalization_11 (Batc (None, 128)               512       
_________________________________________________________________
activation_20 (Activation)   (None, 128)               0         
_________________________________________________________________
dense_50 (Dense)             (None, 256)               33024     
_________________________________________________________________
dense_51 (Dense)             (None, 7)                 1799      
=================================================================
val_mse: 0.91


_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_54 (LSTM)               (None, 128)               67072     
_________________________________________________________________
activation_23 (Activation)   (None, 128)               0         
_________________________________________________________________
dropout_17 (Dropout)         (None, 128)               0         
_________________________________________________________________
dense_54 (Dense)             (None, 256)               33024     
_________________________________________________________________
dropout_18 (Dropout)         (None, 256)               0         
_________________________________________________________________
dense_55 (Dense)             (None, 7)                 1799      
=================================================================
val_mse: 0.0281


_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_55 (LSTM)               (None, 30, 128)           67072     
_________________________________________________________________
activation_24 (Activation)   (None, 30, 128)           0         
_________________________________________________________________
lstm_56 (LSTM)               (None, 128)               131584    
_________________________________________________________________
activation_25 (Activation)   (None, 128)               0         
_________________________________________________________________
dense_56 (Dense)             (None, 256)               33024     
_________________________________________________________________
dense_57 (Dense)             (None, 7)                 1799      
=================================================================
val_mse: 0.0822


_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_57 (LSTM)               (None, 30, 128)           67072     
_________________________________________________________________
activation_26 (Activation)   (None, 30, 128)           0         
_________________________________________________________________
lstm_58 (LSTM)               (None, 128)               131584    
_________________________________________________________________
activation_27 (Activation)   (None, 128)               0         
_________________________________________________________________
dense_58 (Dense)             (None, 256)               33024     
_________________________________________________________________
dense_59 (Dense)             (None, 256)               65792     
_________________________________________________________________
dense_60 (Dense)             (None, 7)                 1799      
=================================================================
val_mse: 0.0541

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_61 (LSTM)               (None, 30, 128)           67072     
_________________________________________________________________
activation_30 (Activation)   (None, 30, 128)           0         
_________________________________________________________________
lstm_62 (LSTM)               (None, 128)               131584    
_________________________________________________________________
activation_31 (Activation)   (None, 128)               0         
_________________________________________________________________
dense_62 (Dense)             (None, 7)                 903       
=================================================================
val_mse: 0.0713

4-我也尝试添加 BatchNormalization

似乎没有什么有助于准确预测未来 5 天。

您认为我可以尝试的其他任何事情吗？

谢谢！！

几周前我开始在 python 中使用 keras，现在我正在尝试解决预测问题。基本上我有活跃的学生，每天有多少学生协助上课。我想做的是预测接下来 X 天的援助，其中 x 现在是 10 天。为此，我有从 1 月 1 日到 9 月 28 日的信息我正在做的是将学生人数和每天协助的学生人数以 30 天为一组进行分组，我正在为 RNN 提供这些信息并获得作为输出一个包含 10 个值的数组

这是我的模型模型：“sequential_1”

层（类型）输出形状参数#

lstm_2 (LSTM)（无，256）265216

dense_1（密集）（无，128）32896

dense_2（密集）（无，10）1290

总参数：299,402 可训练参数：299,402 不可训练参数：0

我正在尝试改进我的模型以获得尽可能精确的预测，但我很难做到这一点。

我会很感激你的意见和建议

这是我的谷歌 colab 笔记本： https ://colab.research.google.com/drive/15RhjFwzjpUAhdXFWkn1dmRgFLoDQcuiU?usp=sharing

和数据集： https ://drive.google.com/file/d/1-HhaXSs4Bf0a0626bhAUCobFZRTasOGM/view?usp=sharing

提前致谢

标签： pythontensorflowkeras

python - 使用 Keras 进行 LSTM 优化

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