如何从 Keras 嵌入层获取词向量

Question

我目前正在使用 Keras 模型，该模型的第一层是嵌入层。为了可视化单词之间的关系和相似性，我需要一个函数 returns 词汇表中每个元素的单词和向量的映射（例如 'love' - [0.21, 0.56, .. ., 0.65, 0.10]).

有什么办法吗？

Answer 1

你可以使用嵌入层的get_weights()方法得到词嵌入（即嵌入层的权重本质上是嵌入向量）：

# if you have access to the embedding layer explicitly
embeddings = emebdding_layer.get_weights()[0]

# or access the embedding layer through the constructed model 
# first `0` refers to the position of embedding layer in the `model`
embeddings = model.layers[0].get_weights()[0]

# `embeddings` has a shape of (num_vocab, embedding_dim) 

# `word_to_index` is a mapping (i.e. dict) from words to their index, e.g. `love`: 69
words_embeddings = {w:embeddings[idx] for w, idx in word_to_index.items()}

# now you can use it like this for example
print(words_embeddings['love'])  # possible output: [0.21, 0.56, ..., 0.65, 0.10]

如何从 Keras 嵌入层获取词向量

How to get word vectors from Keras Embedding Layer

python

dictionary

keras

keras-layer

word-embedding