Keras LSTM模型始终欠拟合的问题求助

Hey Andy, I feel your pain—when you throw more layers and neurons at a model and it still just flatlines on loss, it’s super frustrating. Let’s walk through some actionable fixes tailored to your setup, since you’ve already ruled out common quick wins like adding capacity or removing dropout.

1. First: Fix Data Preprocessing (You Might Be Missing Normalization) #

LSTMs are extremely sensitive to the scale of input data. If your 30 features have wildly different ranges (e.g., one feature is 0-1 and another is 1000-10000), the model will struggle to learn meaningful patterns. You didn’t mention normalization in your code, so this is likely a big one.

Try scaling your input and output data to a small range like (0,1) using MinMaxScaler:

from sklearn.preprocessing import MinMaxScaler

# Reshape to 2D for scaling, then reshape back to 3D
scaler_x = MinMaxScaler(feature_range=(0, 1))
x_train_scaled = scaler_x.fit_transform(x_train.reshape(-1, data_dim)).reshape(x_train.shape)

scaler_y = MinMaxScaler(feature_range=(0, 1))
y_train_scaled = scaler_y.fit_transform(y_train.reshape(-1, out_dim)).reshape(y_train.shape)

Train on the scaled data, and invert the scaling when making predictions to get real-world values.

2. Tweak Your Sequence Length (Timesteps) #

Your current timesteps=200 might be too long or too short for your data’s temporal patterns:

• If the key dependencies in your data happen over shorter windows (e.g., 50-100 steps), a 200-step sequence forces the model to learn irrelevant noise alongside signal.
• If dependencies are longer, 200 might not capture enough context.

Test different values (50, 100, 300) and see if the loss curves start to show more learning.

3. Adjust Optimizer and Learning Rate Strategy #

Your RMSprop setup with decay=0.9 might be dropping the learning rate too fast, making the model stop learning early. Try these changes:

• Switch to Adam optimizer (it’s adaptive and often works better for sequence tasks):

  adam_opt = keras.optimizers.Adam(learning_rate=0.001)
  

• Add a learning rate scheduler to automatically lower the rate when validation loss plateaus:

  from tensorflow.keras.callbacks import ReduceLROnPlateau
  reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=10, min_lr=1e-6)
  

  Pass this to your fit() call alongside your existing callbacks.

4. Refine the Network Structure #

Your current stack of LSTMs is symmetric, but adding small stabilizers or changing the architecture could help:

• Add Layer Normalization: Place LayerNormalization() after each LSTM layer to stabilize training and help the model learn more effectively:

  from tensorflow.keras.layers import LayerNormalization
  
  model = Sequential()
  model.add(LSTM(h1, return_sequences=True, input_shape=(timesteps, data_dim)))
  model.add(LayerNormalization())
  model.add(LSTM(h2, return_sequences=True))
  model.add(LayerNormalization())
  # Repeat for remaining LSTM layers
  

• Try Bidirectional LSTMs: If your data has meaningful patterns in both past and future directions (even though you’re predicting forward), Bidirectional(LSTM(...)) can capture more context:

  from tensorflow.keras.layers import Bidirectional
  model.add(Bidirectional(LSTM(h1, return_sequences=True), input_shape=(timesteps, data_dim)))
  

• Consider Encoder-Decoder Architecture: Since you’re doing sequence-to-sequence prediction (outputting a full sequence of 200 steps), an encoder-decoder setup might be more effective than a straight stack of LSTMs. The encoder compresses the input sequence into a context vector, and the decoder uses that to generate the output sequence.

5. Verify Input-Label Alignment #

Double-check that your x_train and y_train are correctly aligned. For example:

• If x_train is the sequence from time t to t+199, does y_train correspond to t+1 to t+200 (predicting the next step for each input step)? A misalignment here would mean the model is trying to learn a meaningless mapping, leading to flat loss curves.

6. Experiment with Loss Functions #

While MSE is standard for regression tasks, try alternatives to see if they help the model learn:

• MAE (Mean Absolute Error): More robust to outliers in your data.
• Huber Loss: Combines the best of MSE and MAE—smooth like MSE for small errors, robust like MAE for large errors.

model.compile(loss=keras.losses.Huber(), optimizer=adam_opt, metrics=['mse'])

7. Light Data Augmentation #

Even with 4 hours of data, adding small variations can help the model generalize better while improving fitting:

• Add tiny Gaussian noise to your input features (e.g., x_train += np.random.normal(0, 0.01, x_train.shape)).
• Use slightly shifted sliding windows when creating your sequences to generate more training samples.

Give these steps a try—start with normalization and sequence length tweaks first, since those are often the quickest wins. Let me know how it goes!

内容的提问来源于stack exchange，提问作者Andy