I am working on sentiment analysis of text. I am using keras word embedding. If my embedding has a vocabulary of 50 and an input length of 4 and I choose an embedding space of 8 dimensions, how will be the output from the embedding layer?
As noted in Keras Documentation, your embedding layer output will be of dimension:
(<batch_size>, <input_length>, <embedding dimension>)
i.e. in your case it is
(None, 4, 8), where
None stands for your batch size. The vocabulary size doesn't affect the output dimension, but you need to input it while constructing your