Keras Word2Vec Skip-Gram模型训练损失持续上升问题求助

Hey there, let's work through why your Skip-Gram model's loss is climbing instead of trending down—this is a super common issue with custom Keras implementations, so let's break down the most likely culprits step by step:

• Double-check your loss function and label setup
  Skip-Gram’s core task is predicting context words from a center word. If your labels are integer word indices (not one-hot encoded), you must use sparse_categorical_crossentropy instead of categorical_crossentropy. Using the wrong loss here is one of the top reasons for exploding loss. Also, make sure you didn’t flip your input-output pairs: Skip-Gram takes a center word as input and predicts its surrounding context words, not the other way around.

• Fix your sampling and batch logic
  Since you’re feeding sentences one by one, I bet you might be missing two critical parts of Word2Vec: proper context sampling and negative sampling. For each center word, you need to generate samples for every word in its window (e.g., left/right 2 words). Without negative sampling, the model has to learn to distinguish every possible word in your vocab, which is computationally overwhelming and leads to unstable loss. Also, training on single sentences (batch size 1) creates massive gradient noise—pack your samples into batches of 256 or 512 to smooth out updates.

• Tune your optimizer and learning rate
  A learning rate that’s too high will make your model "bounce around" the parameter space instead of converging. Try dropping your learning rate from the default 0.001 to 0.0001 or even 0.00005, especially if you’re using SGD. Adam is generally more stable for these tasks than vanilla SGD, so switching optimizers might help too. Adding a small weight decay (e.g., weight_decay=1e-6) to your optimizer can also prevent weights from blowing up.

• Validate your vocabulary and index mapping
  Make sure your mapping uses continuous indices that match your vocab size. For example, if your vocab has 10,000 unique words, indices should range from 0 to 9999 (plus an extra index for out-of-vocabulary words if you have them). If there are indices outside this range, or if you didn’t filter low-frequency words (which can clutter the model), your training will get confused. Double-check that every index in mapping corresponds to a valid entry in your embedding layer.

• Verify your model architecture
  Confirm your embedding layer is configured correctly: Embedding(input_dim=total_vocab_size, output_dim=embedding_dim)—input_dim must exactly match the number of unique words in your vocab (including OOV if applicable). Your output layer should be Dense(total_vocab_size) (if using sparse categorical loss, since softmax is handled by the loss function) or Dense(total_vocab_size, activation='softmax') (for categorical loss). A mismatch between output layer size and vocab size will break loss calculation.

• Check your training loop for data shuffling
  If you’re feeding sentences in the same order every epoch, the model will start memorizing the sequence instead of learning generalizable word embeddings. Make sure to shuffle your sample batches (not just the sentences) between epochs to keep the model from overfitting to the data order.

Here’s a quick example of a correctly compiled model for integer labels:

from tensorflow.keras.optimizers import Adam

model.compile(
    optimizer=Adam(learning_rate=0.0001, weight_decay=1e-6),
    loss='sparse_categorical_crossentropy'
)

Start with these checks—odds are one of these is the root cause of your rising loss!

内容的提问来源于stack exchange，提问作者sist