Tensorflow: Word2vec CBOW model

Question

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 C

Answer 1

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