Keras Embedding Layer main paper I need to explain the word embedding layer of Keras in my paper, mathematically. I know that keras initialize the embedding vectors randomly and then update the parameters using the optimizer specified by the programmer. I want to explain my architecture in an academic paper, therefore I need to explain each layer with a formal formula. 
Is there a paper that explains the method in details to reference it? or can you please guide me to how to compose the method formally?

Thanks a lot
I think I found the answer. The embedding layer in keras is nothing more than a set of vectors for distinct words. The keras embedding layer initializes the word embedding with some random values (the default values from a uniform distribution)and then updates the values when train the whole network. Therefore, there is no need to compose the model in details, because the backpropagation do it perfectly :)