def test_forward_does_a_bilinear_product(self):
# pylint: disable=protected-access
bilinear = BilinearSimilarity(2, 2)
bilinear._weight_matrix = Parameter(torch.FloatTensor([[-.3, .5], [2.0, -1.0]]))
bilinear._bias = Parameter(torch.FloatTensor([.1]))
a_vectors = Variable(torch.FloatTensor([[1, 1], [-1, -1]]))
b_vectors = Variable(torch.FloatTensor([[1, 0], [0, 1]]))
result = bilinear(a_vectors, b_vectors).data.numpy()
assert result.shape == (2,)
assert_almost_equal(result, [1.8, .6])
def test_forward_does_a_bilinear_product(self):
# pylint: disable=protected-access
bilinear = BilinearSimilarity(2, 2)
bilinear._weight_matrix = Parameter(
torch.FloatTensor([[-.3, .5], [2.0, -1.0]]))
bilinear._bias = Parameter(torch.FloatTensor([.1]))
a_vectors = torch.FloatTensor([[1, 1], [-1, -1]])
b_vectors = torch.FloatTensor([[1, 0], [0, 1]])
result = bilinear(a_vectors, b_vectors).data.numpy()
assert result.shape == (2, )
assert_almost_equal(result, [1.8, .6])
def test_forward_works_with_higher_order_tensors(self):
# pylint: disable=protected-access
bilinear = BilinearSimilarity(4, 7)
weights = numpy.random.rand(4, 7)
bilinear._weight_matrix = Parameter(torch.from_numpy(weights).float())
bilinear._bias = Parameter(torch.from_numpy(numpy.asarray([0])).float())
a_vectors = numpy.random.rand(5, 4, 3, 6, 4)
b_vectors = numpy.random.rand(5, 4, 3, 6, 7)
a_variables = Variable(torch.from_numpy(a_vectors).float())
b_variables = Variable(torch.from_numpy(b_vectors).float())
result = bilinear(a_variables, b_variables).data.numpy()
assert result.shape == (5, 4, 3, 6)
expected_result = numpy.dot(numpy.dot(numpy.transpose(a_vectors[3, 2, 1, 3]), weights),
b_vectors[3, 2, 1, 3])
assert_almost_equal(result[3, 2, 1, 3], expected_result, decimal=5)
def test_forward_works_with_higher_order_tensors(self):
# pylint: disable=protected-access
bilinear = BilinearSimilarity(4, 7)
weights = numpy.random.rand(4, 7)
bilinear._weight_matrix = Parameter(torch.from_numpy(weights).float())
bilinear._bias = Parameter(
torch.from_numpy(numpy.asarray([0])).float())
a_vectors = numpy.random.rand(5, 4, 3, 6, 4)
b_vectors = numpy.random.rand(5, 4, 3, 6, 7)
a_variables = torch.from_numpy(a_vectors).float()
b_variables = torch.from_numpy(b_vectors).float()
result = bilinear(a_variables, b_variables).data.numpy()
assert result.shape == (5, 4, 3, 6)
expected_result = numpy.dot(
numpy.dot(numpy.transpose(a_vectors[3, 2, 1, 3]), weights),
b_vectors[3, 2, 1, 3])
assert_almost_equal(result[3, 2, 1, 3], expected_result, decimal=5)