Python BilinearMatrixAttention.from_params示例

示例#1

文件： bilinear_matrix_attention_test.py 项目： ha-lins/medical_dialog

 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]]])

示例#2

文件： bilinear_matrix_attention_test.py 项目： apmoore1/allennlp

 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]]])

示例#3

 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]]])

示例#4

文件： bilinear_matrix_attention_test.py 项目： apmoore1/allennlp

 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]]])