📄 中文摘要
在前一篇文章中，重点分析了编码器和解码器中的嵌入值。解码器的输出是 Seq2Seq 模型的重要组成部分，它负责生成目标序列中的每个词。解码器通过接收编码器传递的上下文向量，结合自身的输入信息，逐步生成输出。全连接层在此过程中起到关键作用，它将解码器的隐藏状态映射到目标词汇表中，从而实现词的选择。理解这一过程对于优化 Seq2Seq 模型的性能至关重要。
理解 Seq2Seq 神经网络 - 第六部分：解码器输出与全连接层
📄 English Summary
Understanding Seq2Seq Neural Networks – Part 6: Decoder Outputs and the Fully Connected Layer
The previous article focused on the embedding values in both the encoder and decoder. The decoder's outputs are a crucial part of the Seq2Seq model, responsible for generating each word in the target sequence. It receives the context vector from the encoder and combines it with its own input information to sequentially produce outputs. The fully connected layer plays a key role in this process, mapping the decoder's hidden states to the target vocabulary, enabling word selection. Understanding this process is essential for optimizing the performance of Seq2Seq models.