def test_forward_does_a_bilinear_product(self):
params = Params({"matrix_1_dim": 2, "matrix_2_dim": 2})
bilinear = BilinearMatrixAttention.from_params(params)
bilinear._weight_matrix = Parameter(torch.FloatTensor([[-0.3, 0.5], [2.0, -1.0]]))
bilinear._bias = Parameter(torch.FloatTensor([0.1]))
a_vectors = torch.FloatTensor([[[1, 1], [2, 2]]])
b_vectors = torch.FloatTensor([[[1, 0], [0, 1]]])
result = bilinear(a_vectors, b_vectors).detach().numpy()
assert result.shape == (1, 2, 2)
assert_almost_equal(result, [[[1.8, -0.4], [3.5, -0.9]]])
def test_forward_does_a_bilinear_product(self):
params = Params({
'matrix_1_dim': 2,
'matrix_2_dim': 2,
})
bilinear = BilinearMatrixAttention.from_params(params)
bilinear._weight_matrix = Parameter(torch.FloatTensor([[-.3, .5], [2.0, -1.0]]))
bilinear._bias = Parameter(torch.FloatTensor([.1]))
a_vectors = torch.FloatTensor([[[1, 1], [2, 2]]])
b_vectors = torch.FloatTensor([[[1, 0], [0, 1]]])
result = bilinear(a_vectors, b_vectors).detach().numpy()
assert result.shape == (1, 2, 2)
assert_almost_equal(result, [[[1.8, -.4], [3.5, -.9]]])
def test_forward_does_a_bilinear_product_when_using_biases(self):
params = Params({
u'matrix_1_dim': 2,
u'matrix_2_dim': 2,
u'use_input_biases': True
})
bilinear = BilinearMatrixAttention.from_params(params)
bilinear._weight_matrix = Parameter(torch.FloatTensor([[-.3, .5, 1.0],
[2.0, -1.0, -1.0],
[1.0, 0.5, 1.0]]))
bilinear._bias = Parameter(torch.FloatTensor([.1]))
a_vectors = torch.FloatTensor([[[1, 1], [2, 2]]])
b_vectors = torch.FloatTensor([[[1, 0], [0, 1]]])
result = bilinear(a_vectors, b_vectors).detach().numpy()
assert result.shape == (1, 2, 2)
assert_almost_equal(result, [[[3.8, 1.1], [5.5, 0.6]]])
def test_forward_does_a_bilinear_product_when_using_biases(self):
params = Params({
'matrix_1_dim': 2,
'matrix_2_dim': 2,
'use_input_biases': True
})
bilinear = BilinearMatrixAttention.from_params(params)
bilinear._weight_matrix = Parameter(torch.FloatTensor([[-.3, .5, 1.0],
[2.0, -1.0, -1.0],
[1.0, 0.5, 1.0]]))
bilinear._bias = Parameter(torch.FloatTensor([.1]))
a_vectors = torch.FloatTensor([[[1, 1], [2, 2]]])
b_vectors = torch.FloatTensor([[[1, 0], [0, 1]]])
result = bilinear(a_vectors, b_vectors).detach().numpy()
assert result.shape == (1, 2, 2)
assert_almost_equal(result, [[[3.8, 1.1], [5.5, 0.6]]])