示例#1
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 def test_initialize_weights_returns_correct_weight_sizes(self):
     linear = Linear(name='linear', combination='x,y')
     weights = linear.initialize_weights(input_shape=(2, 4, 3))
     assert isinstance(weights, list) and len(weights) == 2
     weight_vector, bias = weights
     assert K.int_shape(weight_vector) == (3 * 2, 1)
     assert K.int_shape(bias) == (1, )
示例#2
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 def test_compute_similarity_works_with_subtract_combinations(self):
     linear = Linear(name='linear', combination='x-y')
     linear.weight_vector = K.variable(numpy.asarray([[-.3], [.5]]))
     linear.bias = K.variable(numpy.asarray([0]))
     a_vectors = numpy.asarray([[1, 1], [-1, -1]])
     b_vectors = numpy.asarray([[1, 0], [0, 1]])
     result = K.eval(
         linear.compute_similarity(K.variable(a_vectors),
                                   K.variable(b_vectors)))
     assert result.shape == (2, )
     assert_almost_equal(result, [.5, -.7])
示例#3
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 def test_compute_similarity_works_with_higher_order_tensors(self):
     linear = Linear(name='linear', combination='x,y')
     weights = numpy.random.rand(14, 1)
     linear.weight_vector = K.variable(weights)
     linear.bias = K.variable(numpy.asarray([0]))
     a_vectors = numpy.random.rand(5, 4, 3, 6, 7)
     b_vectors = numpy.random.rand(5, 4, 3, 6, 7)
     result = K.eval(
         linear.compute_similarity(K.variable(a_vectors),
                                   K.variable(b_vectors)))
     assert result.shape == (5, 4, 3, 6)
     combined_vectors = numpy.concatenate(
         [a_vectors[3, 2, 1, 3, :], b_vectors[3, 2, 1, 3, :]])
     expected_result = numpy.dot(combined_vectors, weights)
     assert_almost_equal(result[3, 2, 1, 3], expected_result, decimal=6)
示例#4
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 def test_compute_similarity_does_a_weighted_product(self):
     linear = Linear(name='linear', combination='x,y')
     linear.weight_vector = K.variable(
         numpy.asarray([[-.3], [.5], [2.0], [-1.0]]))
     linear.bias = K.variable(numpy.asarray([.1]))
     a_vectors = numpy.asarray([[[1, 1], [-1, -1]]])
     b_vectors = numpy.asarray([[[1, 0], [0, 1]]])
     result = K.eval(
         linear.compute_similarity(K.variable(a_vectors),
                                   K.variable(b_vectors)))
     assert result.shape == (
         1,
         2,
     )
     assert_almost_equal(result, [[2.3, -1.1]])