From the source code:
Tensor cosine_embedding_loss(const Tensor& input1, const Tensor& input2, const Tensor& target, double margin, int64_t reduction) {
auto prod_sum = (input1 * input2).sum(1);
auto mag_square1 = (input1 * input1).sum(1) + EPSILON;
auto mag_square2 = (input2 * input2).sum(1) + EPSILON;
auto denom = (mag_square1 * mag_square2).sqrt_();
auto cos = prod_sum / denom;
auto zeros = at::zeros_like(cos, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
auto pos = 1 - cos;
auto neg = (cos - margin).clamp_min_(0);
auto output_pos = at::where(target == 1, pos, zeros);
auto output_neg = at::where(target == -1, neg, zeros);
auto output = output_pos + output_neg;
return apply_loss_reduction(output, reduction);
}
I have two questions:
- Is it deprecate for the three steps of calculation for
prod_sum,mag_square1,mag_square2? They all calculate(input1 * input2).sum(1), or it’s because of some mechanisms like object referencing? - It doesn’t check the range of
targetat all. I inputed a target of 0.5, but it didn’t raise an error.