facenet triplet loss with keras

Question

I am trying to implement facenet in Keras with Thensorflow backend and I have some problem with the triplet loss.

I call the fit function with 3*n number of images and t

Answer 1

Are you constraining your embeddings to "be on a d-dimensional hypersphere"? Try running tf.nn.l2_normalize on your embeddings right after they come out of the CNN.

The problem could be that the embeddings are sort of being smart-alecs. One easy way to reduce the loss is to just set everything to zero. l2_normalize forces them to be unit length.

It looks you'll want to add the normalizing right after the last average pool.