什么是Keras中的嵌入? [英] What is an Embedding in Keras?

问题描述

Keras文档尚不清楚这到底是什么.我知道我们可以使用它来将输入要素空间压缩为较小的空间.但是，从神经设计的角度来看，这是怎么做的呢?它是自动编码器吗，RBM?

Keras documentation isn't clear what this actually is. I understand we can use this to compress the input feature space into a smaller one. But how is this done from a neural design perspective? Is it an autoenocder, RBM?

推荐答案

据我所知，嵌入层是一个简单的矩阵乘法，可将单词转换为相应的单词嵌入.

As far as I know, the Embedding layer is a simple matrix multiplication that transforms words into their corresponding word embeddings.

嵌入层的权重具有形状(vocabulary_size，embedding_dimension).对于每个训练样本，其输入都是整数，表示某些单词.整数在词汇量范围内.嵌入层将每个整数i转换为嵌入权重矩阵的第i行.

The weights of the Embedding layer are of the shape (vocabulary_size, embedding_dimension). For each training sample, its input are integers, which represent certain words. The integers are in the range of the vocabulary size. The Embedding layer transforms each integer i into the ith line of the embedding weights matrix.

为了快速进行矩阵乘法，输入整数不存储为整数列表，而是存储为单矩阵.因此，输入形状为(nb_words，vocabulary_size)，每行一个非零值.如果将其乘以嵌入权重，则会得到形状为

In order to quickly do this as a matrix multiplication, the input integers are not stored as a list of integers but as a one-hot matrix. Therefore the input shape is (nb_words, vocabulary_size) with one non-zero value per line. If you multiply this by the embedding weights, you get the output in the shape

(nb_words, vocab_size) x (vocab_size, embedding_dim) = (nb_words, embedding_dim)

因此，通过简单的矩阵乘法，您就可以将样本中的所有单词转换为相应的单词嵌入.

So with a simple matrix multiplication you transform all the words in a sample into the corresponding word embeddings.

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