def test_bidaf_trilinear_similarity(self):
linear = LinearMatrixAttention(2, 2, combination="x,y,x*y")
linear._weight_vector = Parameter(
torch.FloatTensor([-0.3, 0.5, 2.0, -1.0, 1, 1]))
linear._bias = Parameter(torch.FloatTensor([0.0]))
output = linear(
torch.FloatTensor([[[0, 0], [4, 5]], [[-7, -8], [10, 11]]]),
torch.FloatTensor([[[1, 2], [4, 5]], [[7, 8], [10, 11]]]),
)
assert_almost_equal(
output.data.numpy(),
numpy.array([
[
[0 + 0 + 2 + -2 + 0 + 0, 0 + 0 + 8 + -5 + 0 + 0],
[
-1.2 + 2.5 + 2 + -2 + 4 + 10,
-1.2 + 2.5 + 8 + -5 + 16 + 25
],
],
[
[
2.1 + -4 + 14 + -8 + -49 + -64,
2.1 + -4 + 20 + -11 + -70 + -88
],
[
-3 + 5.5 + 14 + -8 + 70 + 88,
-3 + 5.5 + 20 + -11 + 100 + 121
],
],
]),
decimal=2,
)
def test_linear_similarity(self):
linear = LinearMatrixAttention(3, 3)
linear._weight_vector = Parameter(torch.FloatTensor([-.3, .5, 2.0, -1.0, 1, 1]))
linear._bias = Parameter(torch.FloatTensor([.1]))
output = linear(Variable(torch.FloatTensor([[[0, 0, 0], [4, 5, 6]], [[-7, -8, -9], [10, 11, 12]]])),
Variable(torch.FloatTensor([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])))
assert_almost_equal(output.data.numpy(), numpy.array([[[4.1000, 7.1000], [17.4000, 20.4000]],
[[-9.8000, -6.8000], [36.6000, 39.6000]]]),
decimal=2)
def test_linear_similarity(self):
linear = LinearMatrixAttention(3, 3)
linear._weight_vector = Parameter(torch.FloatTensor([-.3, .5, 2.0, -1.0, 1, 1]))
linear._bias = Parameter(torch.FloatTensor([.1]))
output = linear(Variable(torch.FloatTensor([[[0, 0, 0], [4, 5, 6]], [[-7, -8, -9], [10, 11, 12]]])),
Variable(torch.FloatTensor([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])))
assert_almost_equal(output.data.numpy(), numpy.array([[[4.1000, 7.1000], [17.4000, 20.4000]],
[[-9.8000, -6.8000], [36.6000, 39.6000]]]),
decimal=2)