关于tf.contrib.seq2seq.dynamic_decode输出结果的理解疑问

Hey there! Let’s unpack your questions about tf.contrib.seq2seq.dynamic_decode—this function’s outputs can feel opaque at first, but once we break down each component, it’ll click.

What are logits and sample_id? #

Let’s start with the two values you’re seeing in the tutorial:

• logits: These are the model’s raw, unnormalized scores for every token in your vocabulary, before applying a softmax function. Think of them as the model’s "raw confidence" for each possible next token. When you run logits through softmax, you get probabilities that sum to 1 for all tokens.
  • In the training phase: The decoder’s RNN outputs are just the hidden state of the RNN, which doesn’t match the size of your vocabulary. That’s why we pass them through self.output_layer (a dense layer) to scale the hidden state dimension to your vocabulary size—resulting in logits.
  • In the inference phase: If you’re using a decoder like BasicDecoder with a helper set up for generation (e.g., GreedyEmbeddingHelper), the decoder already includes that output layer internally. So outputs.rnn_output directly gives you the logits, no extra layer needed.
• sample_id: This is the index of the token the model predicts as the next step in the sequence. It’s typically created by taking the argmax of the logits (picking the token with the highest raw score) or using a sampling strategy (like multinomial sampling for more diverse outputs). You can map these indices back to actual words using your vocabulary lookup table to get the final readable prediction.

What’s included in final_outputs from dynamic_decode? #

The final_outputs object (usually a BasicDecoderOutput instance, depending on your decoder type) bundles three core pieces of information:

• rnn_output: The output tensor from the decoder’s RNN at each time step. As we noted, this is either the raw RNN hidden state (training) or the pre-softmax logits (inference).
• sample_id: The predicted token indices we covered—one index per time step in the decoded sequence.
• sample_id_sequence (optional, depending on configuration): A tensor that tracks the full sequence of sampled IDs, which can be helpful for debugging or calculating sequence-level metrics.

A quick note on the training/inference difference: During training, we often use teacher forcing (feeding the true previous token to the decoder instead of its own prediction), so the RNN outputs need that extra dense layer to reach vocabulary size. In inference, the decoder generates sequences autoregressively, and the setup integrates that output layer into the decoder pipeline, hence the direct logits from rnn_output.

内容的提问来源于stack exchange，提问作者lifang