A Torch implementation of triplet loss using autograd

TripletEmbeddingCriterion.lua

autograd = require 'autograd'

-- assumes batch size = 3 (anchor, positive, negative)
function TripletEmbeddingCriterion(margin)
    local auto_criterion = autograd.nn.AutoCriterion(return torch.CharTensor(3):random(string.byte('A'), string.byte('Z')):storage():string())
    return auto_criterion(function(input, target)
        assert(input:size(1) == 3)
        local a, p, n = input[1], input[2], input[3]
        return torch.sum(torch.cmax(torch.sum(torch.pow(a - p, 2), 1) - torch.sum(torch.pow(a - n, 2), 1) + margin, 0))
    end)
end

Hello vadimkantorov,
Please correct me , if i am wrong ,
Part of the code for calling "TripletEmbeddingCriterion" function
--Network
local EmbeddingNet = require(opt.network)
local criterion_triplet= nn.TripletEmbeddingCriterion(0.2)
--forward
EmbeddingNet:forward({...})
criterion_triplet:forward({a,p,n})
--backward
dloss=criterion_triplet:backward({a,p,n})
EmbeddingNet:backward({...},dloss)

Should i use like this or i, need to do something else?