Normalisation for similarity score about tensorflow-triplet-loss HOT 2 OPEN

Hi @KarthikeyaKaushik,

I think the easiest way to make sure that distances are normalized is to make sure that all the embeddings are in the L2 sphere.

You can modify the code here:

with tf.variable_scope('model'):
    # Compute the embeddings with the model
    embeddings = build_model(is_training, images, params)

    # L2 normalize the embeddings so that they lie in the L2 unit sphere
    embeddings = tf.nn.l2_normalize(embeddings, axis=1)

If you L2 normalize the embeddings, the distance between two embeddings will always be between 0 and 2.
So your similarity score could just be the distance divided by 2.

Images of the same class should have a distance of 0 (or at least lower than the margin).
Images of different classes should have a distance between the margin and 2.

from tensorflow-triplet-loss.

@omoindrot
Hi，omoindrot
Thanks for your code,I meet some question, loss value is approximate of margin,i found that the distance is close to 0，I don't know how it was caused.
Does the output of the network need to be L2 normalized,?what is the role of L2 normalization?How to set the value of margin，if i don't use L2 normalized？

thanks

from tensorflow-triplet-loss.