Tensorflow:Word2vec CBOW 模型 [英] Tensorflow: Word2vec CBOW model

问题描述

我是 tensorflow 和 word2vec 的新手.我刚刚研究了

要实现 CBOW，您必须编写一个新的 generate_batch 生成器函数，并在应用逻辑回归之前对周围单词的向量求和.我写了一个例子，你可以参考:https://github.com/wangz10/tensorflow-playground/blob/master/word2vec.py#L105

I am new to tensorflow and to word2vec. I just studied the word2vec_basic.py which trains the model using Skip-Gram algorithm. Now I want to train using CBOW algorithm. Is it true that this can be achieved if I simply reverse the train_inputs and train_labels?

解决方案

I think CBOW model can not simply be achieved by flipping the train_inputs and the train_labels in Skip-gram because CBOW model architecture uses the sum of the vectors of surrounding words as one single instance for the classifier to predict. E.g., you should use [the, brown] together to predict quick rather than using the to predict quick.

To implement CBOW, you'll have to write a new generate_batch generator function and sum up the vectors of surrounding words before applying logistic regression. I wrote an example you can refer to: https://github.com/wangz10/tensorflow-playground/blob/master/word2vec.py#L105

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