深度学习seq2seq模型的相关内容

动手学深度学习(十四) NLP注意力机制和Seq2seq模型（下）

引入注意力机制的Seq2seq模型本节中将注意机制添加到sequence to sequence 模型中，以显式地使用权重聚合states。下图展示encoding 和decoding的模型结构，在时间步为t的时候。此刻attention layer保存着encodering看到的所有信息——即en...

动手学深度学习(十四) NLP注意力机制和Seq2seq模型（上）

注意力机制在“编码器—解码器（seq2seq）”⼀节⾥，解码器在各个时间步依赖相同的背景变量（context vector）来获取输⼊序列信息。当编码器为循环神经⽹络时，背景变量来⾃它最终时间步的隐藏状态。将源序列输入信息以循环单位状态编码，然后将其传递给解码器以生成目标序列。然而这种结构存在着问题...

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深度学习的seq2seq模型

从rnn结构说起根据输出和输入序列不同数量rnn可以有多种不同的结构，不同结构自然就有不同的引用场合。如下图， one to one 结构，仅仅只是简单的给一个输入得到一个输出，此处并未体现序列的特征，例如图像分类场景。 one to many 结构，给一个输入得到一系列输出，这种结构可用于生产图...

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更新时间 2023-02-09 12:26:48

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