Keras Seq2Seq模型predict的argmax始终输出0问题求助

Hey there, let's break down why your Seq2Seq model is only outputting zeros after argmax. Looking at your code snippets and prediction outputs, here are actionable troubleshooting steps to diagnose and fix the issue:

1. First, Check Training Sufficiency #

You're only training for 3 epochs—that's way too few for a Seq2Seq model to learn meaningful patterns. Looking at your prediction probabilities:

preds: [[[9.1350e-02 4.3054e-04 ...], [5.9895e-01 3.5628e-05 ...], [7.2249e-01 1.6146e-05 ...]]

The probability of class 0 climbs to ~60% then 72% across time steps, which means the model hasn't had enough time to learn anything beyond predicting the most frequent class (likely padding).

• Fix: Crank up the epochs to 20+ (start with 20, monitor validation loss/acc) and check if the predictions start to vary. Also, if your dataset is small, reduce the batch size from 128 to something smaller like 32 to get more frequent gradient updates.

2. Add Masking for Padding Tokens #

Your real labels have a lot of trailing zeros (padding), but your Embedding layer doesn't use mask_zero=True. This means the model treats padding tokens as valid input/output and is penalized for not predicting zeros in those positions—over time, it learns to just predict zero everywhere to minimize loss.

• Fix: Modify your Embedding layer to include masking:

  x = Embedding(input_vocab_size, 60, mask_zero=True)(inp)
  

  This tells the model to ignore padding positions during training, so it focuses on learning to predict non-zero tokens for meaningful positions.

3. Check Class Distribution in Labels #

If padding zeros make up the vast majority of your label data (which your example suggests), the model will naturally predict zero to maximize accuracy and minimize loss—this is a classic class imbalance issue.

• Fix:
  • Calculate the frequency of each class in your labels. If zero dominates, use class_weight in model.fit() to assign higher weights to non-zero classes:

    # Example: assign 10x weight to non-zero classes
    class_weights = {0: 1.0}
    for cls in range(1, output_vocab_size):
        class_weights[cls] = 10.0
    hist = model.fit(..., class_weight=class_weights)
    

  • Combine this with mask_zero=True to double down on ignoring padding loss.

4. Validate Model Architecture for Seq2Seq #

Your current architecture uses RepeatVector to feed the encoder's final output into the decoder—this is a simpler, older Seq2Seq setup that can struggle with longer sequences. A more robust approach is to pass the encoder's state to the decoder as initial state instead of repeating the vector:

• Adjustment Example:

  # Encoder: Capture hidden states instead of just output
  encoder_bi = Bidirectional(get_rnn(return_sequences=False))(x)
  # For Bidirectional LSTM, combine forward/backward states
  encoder_state_h = Concatenate()([encoder_bi[0][0], encoder_bi[1][0]])
  encoder_state_c = Concatenate()([encoder_bi[0][1], encoder_bi[1][1]])
  decoder_initial_state = [encoder_state_h, encoder_state_c]
  
  # Decoder: Use initial state from encoder
  x = RepeatVector(maxlen)(encoder_bi)
  x = get_rnn()(x, initial_state=decoder_initial_state)
  x = get_rnn()(x)
  

  While this might not fix the immediate all-zero issue, it will improve the model's ability to learn sequence dependencies long-term.

5. Verify Loss Function and Label Shape #

You're using sparse_categorical_crossentropy, which is correct for integer labels (not one-hot encoded). Your np.expand_dims(labels_train,-1) is okay, but double-check that:

• labels_train has shape (batch_size, maxlen) before expanding (so after expanding it's (batch_size, maxlen, 1) which matches the loss's expectations).
• output_vocab_size matches the number of unique classes in your labels (including the zero padding token).

6. Tweak Optimizer Learning Rate #

Adam's default learning rate (0.001) might be too high for your task, causing the model to oscillate or converge to a bad local minimum (like predicting all zeros).

• Fix: Try lowering the learning rate:

  from tensorflow.keras.optimizers import Adam
  model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam(learning_rate=0.0001), metrics=['acc'])
  

Start with the first two steps (more epochs + mask_zero) since they're the quickest to test and likely the root cause here. Let me know how it goes!

内容的提问来源于stack exchange，提问作者agata