NegativeLogLikelihood

/++ equivalent torch v0.4 code
 >>> x = torch.FloatTensor([[0.2, 0.4, 0.4], [0.1,0.5,0.4]])
 >>> x.requires_grad = True
 >>> t = torch.LongTensor([1, 0])
 >>> l = torch.nn.functional.nll_loss(x, t)
 >>> print(l)
 tensor(-0.2500)

 >>> l.backward()
 >>> print(x.grad)
 tensor([[0.0, -0.5, 0.0], [-0.5, 0.0, 0.0]])
 +/
import std.typecons;
import grain.testing;

NegativeLogLikelihood!(float, int) func;
auto hx = [[0.2f, 0.4f, 0.4f], [0.1f, 0.5f, 0.4f], [0.1f, 0.5f, 0.4f]]
    .variable;
auto ht = [1, 0, func.ignoreIndex].variable;
auto hl = func.forward(hx, ht);
assert(func._normalize == 0.5);
assert(hl.sliced == [-(0.4f + 0.1f + 0.0f) / 2]);
auto hgx = func.backward(1.0f.variable);
assert(hgx[0].sliced == [[0.0, -0.5, 0.0], [-0.5, 0.0, 0.0], [0.0, 0.0, 0.0]]);
assert(!hgx[1].defined);
gradCheck(func, tuple(hx, ht), 1.0f.variable);

version (grain_cuda) {
    auto dx = hx.to!DeviceStorage;
    auto dt = ht.to!DeviceStorage;
    auto dl = func.forward(dx, dt);
    assert(func._normalize == 0.5);
    assert(dl.to!HostStorage.sliced == [-(0.4f + 0.1f + 0.0f) / 2]);
    auto dgx = func.backward(1.0f.variable.to!DeviceStorage);
    assert(dgx[0].to!HostStorage.sliced ==
           [[0.0, -0.5, 0.0],
            [-0.5, 0.0, 0.0],
            [0.0, 0.0, 0.0]]);
    assert(!dgx[1].defined);
}

import std.typecons;
import grain.testing;
import mir.ndslice;
static import grain.config;

grain.config.backprop = true;

NegativeLogLikelihood!(float, int) func;
auto hx = [[0.2f, 0.4f, 0.4f], [0.1f, 0.5f, 0.4f], [0.1f, 0.5f, 0.4f]]
    .variable;
hx.requiresGrad = true;
auto ht = [1, 0, func.ignoreIndex].variable;
auto hl = func.applyForward(hx, ht);

assert(func._normalize == 0.5);
assert(hl.sliced == [-(0.4f + 0.1f + 0.0f) / 2]);
auto u = UntypedVariable(1.0f.variable);
hl.backward(&u);

assert(hx.grad[].sliced(3, 3) == [[0.0, -0.5, 0.0], [-0.5, 0.0, 0.0], [0.0, 0.0,
        0.0]]);
// TODO assert(!ht.grad.defined);