Keras-嵌入层 [英] Keras- Embedding layer

问题描述

input_dim，output_dim和input_length的含义是什么

Embedding(input_dim, output_dim, input_length)

根据我了解的文档:

• input_dim:int>0.词汇量
• output_dim:int> =0.密集嵌入的尺寸.
• input_length:输入序列的长度

• input_dim: int > 0. Size of the vocabulary
• output_dim: int >= 0. Dimension of the dense embedding.
• input_length: Length of input sequences

因此，当我输入的词是google.com之类的单词时，每个字符都由整数[5, 2, 2, 5, 8, 3, 4, 1, 2, 9]表示，并且最大可能的单词长度为75.最多可以输入38个字符.我应该如何确定input_dim，output_dim和input_length?

So, when my input is a word like google.com each character represented by an integer [5, 2, 2, 5, 8, 3, 4, 1, 2, 9] and maximum word length possible is 75. Maximum characters possible is 38. How should I decide the input_dim, output_dim and input_length?

推荐答案

为了将单词用于自然语言处理或机器学习任务，必须首先将它们映射到连续的向量空间，从而创建 word向量或单词嵌入. Keras嵌入层可用于构建此类单词向量.

It order to use words for natural language processing or machine learning tasks, it is necessary to first map them onto a continuous vector space, thus creating word vectors or word embeddings. The Keras Embedding layer is useful for constructing such word vectors.

input_dim :词汇量.这是您的语料库中代表多少个独特的单词.

input_dim : the vocabulary size. This is how many unique words are represented in your corpus.

output_dim :单词向量的期望尺寸.例如，如果output_dim = 100，则每个单词将映射到具有100个元素的向量上，而如果output_dim = 300，则每个单词将映射到具有300个元素的向量上.

output_dim : the desired dimension of the word vector. For example, if output_dim = 100, then every word will be mapped onto a vector with 100 elements, whereas if output_dim = 300, then every word will be mapped onto a vector with 300 elements.

input_length :序列的长度.例如，如果您的数据由句子组成，则此变量表示一个句子中有多少个单词.由于不同的句子通常包含不同数量的单词，因此通常需要填充序列以使所有句子的长度相等.可以为此使用keras.preprocessing.pad_sequence方法( https://keras.io/preprocessing/sequence/).

input_length : the length of your sequences. For example, if your data consists of sentences, then this variable represents how many words there are in a sentence. As disparate sentences typically contain different number of words, it is usually required to pad your sequences such that all sentences are of equal length. The keras.preprocessing.pad_sequence method can be used for this (https://keras.io/preprocessing/sequence/).

在Keras中，可以1)使用预先训练的单词向量(例如GloVe或word2vec表示形式)，或2)在训练过程中学习单词向量.这篇博客文章( https://blog .keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html )提供了有关如何使用GloVe预训练单词向量的教程.对于选项2，Keras将随机初始化矢量作为默认选项，然后在训练过程中学习最佳单词矢量.

In Keras, it is possible to either 1) use pretrained word vectors such as GloVe or word2vec representations, or 2) learn the word vectors as part of the training process. This blog post (https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html) offers a tutorial on how to use GloVe pretrained word vectors. For option 2, Keras will randomly initialize vectors as the default option, and then learn optimal word vectors during the training process.

这篇关于Keras-嵌入层的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！