Pytorch implementation of additive margin softmax loss [1].

loss = AMSloss(embedding_dim, no_classes, scale = 30.0, margin=0.4)
err = loss(x, labels)
err.backward()

where:

• embedding_dim is embedding vector dimension
• no_classes is number of classes to be embedded
• scale is scale factor
• margin is additive margin

A simple demo using Fashio-MNIST dataset [2] can be run by:

python demo.py

[1] "Additive Margin Softmax for Face Verification." Wang, Feng, Jian Cheng, Weiyang Liu and Haijun Liu. IEEE Signal Processing Letters 25 (2018): 926-930.

[2] "Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms." Han Xiao, Kashif Rasul, Roland Vollgraf. arXiv:1708.07747