Hi Chris, thanks for reply
I have built a image captioning model based on Tanti’s paper
I used 2 inputs for image and text data and concatenated them before the last dense layer. I do not use a softmax, i take the maximum logit from dense layer’s output to get the next word predicted

I had several experiments by changing embedding layer’s output dimension to 128, 256 and 512
As I have observed from my captions, As the dimension is increased, generated captions became more complex. But i am trying to understand the logic behind it