keras中的多个嵌入层 [英] Multiple embedding layers in keras

问题描述

使用预训练的嵌入，我们可以在keras的嵌入层中将它们指定为权重.要使用多个嵌入，指定多个嵌入层是否合适?即

With pretrained embeddings, we can specify them as weights in keras' embedding layer. To use multiple embeddings, would specifying multiple embedding layer be suitable? i.e.

embedding_layer1 = Embedding(len(word_index) + 1,
                        EMBEDDING_DIM,
                        weights=[embedding_matrix_1],
                        input_length=MAX_SEQUENCE_LENGTH,
                        trainable=False)

 embedding_layer2 = Embedding(len(word_index) + 1,
                        EMBEDDING_DIM,
                        weights=[embedding_matrix_2],
                        input_length=MAX_SEQUENCE_LENGTH,
                        trainable=False)

 model.add(embedding_layer1)
 model.add(embedding_layer2)

这建议将它们汇总并表示为单个层，这不是我想要的.

This suggests to sum them up and represent them into a single layer, which is not what I am after.

推荐答案

下面是利用Keras的功能API 通过多个输入使用多个嵌入层的示例.这是针对Kaggle竞赛的，因此您必须通读代码.他们向网络提供字典，字典中包含每个数据输入的键.这非常聪明，我能够使用性能良好的框架构建一个单独的模型.

Here is an example of using multiple embedding layers through multiple inputs by leveraging Keras' functional API. This is for a Kaggle competition so you'll have to read through the code. They feed the network a dictionary with a key for each data input. It's quite clever and I was able to build a separate model using this framework that performed well.

deep-learning-support-9663

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