如何从Keras嵌入层获取单词向量

Question

我目前正在使用Keras模型，该模型具有嵌入层作为第一层。为了可视化彼此之间的单词的关系和相似性，我需要一个函数来返回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

您可以使用嵌入层的get_weights()方法获得嵌入一词（即嵌入层的权重基本上是嵌入向量）：

# 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]