def test_multiclass_hinge_loss():
from lasagne.objectives import mutliclass_hinge_loss
from lasagne.nonlinearities import rectify
p = theano.tensor.matrix('p')
t = theano.tensor.ivector('t')
c = mutliclass_hinge_loss(p, t)
# numeric version
floatX = theano.config.floatX
predictions = np.random.rand(10, 20).astype(floatX)
targets = np.random.random_integers(0, 19, (10,)).astype("int8")
one_hot = np.zeros((10, 20))
one_hot[np.arange(10), targets] = 1
correct = predictions[one_hot > 0]
rest = predictions[one_hot < 1].reshape((10, 19))
rest = np.max(rest, axis=1)
hinge = rectify(1 + rest - correct)
# compare
assert np.allclose(hinge, c.eval({p: predictions, t: targets}))
def test_multiclass_hinge_loss_invalid():
from lasagne.objectives import mutliclass_hinge_loss
with pytest.raises(TypeError) as exc:
mutliclass_hinge_loss(theano.tensor.vector(),
theano.tensor.matrix())
assert 'rank mismatch' in exc.value.args[0]
