hermitian.py 文件源码

def hermitian_tuple_scorer(tuples_var, rank=None, n_emb=None, emb0=None, symmetry_coef=(1.0, 1.0),
                           learn_symmetry_coef=True):
    """
    The Hermitian Scorer can learn embeddings for non-symmetric relations
    :param tuples_var: TensorFlow variable that encodes the tuples as inputs
    :param rank: size of the embeddings, including real and imaginary parts. The complex rank is half of it.
    not needed if emb0 is given
    :param n_emb: number of embeddings (not needed if initial embeddings are given)
    :param emb0: initial embeddings (optional)
    :param symmetry_coef: symmetry coefficient that equals np.inf for symmetric matrices, -np.inf for anti-symmetric
    matrices and a real scalar for other cases.
    :param learn_symmetry_coef: False if the symmetry coefficient is not learned [True by default]
    :return: a pair (scoring TensorFlow graph, parameters). The parameters have the form
     ([n_emd*rank] float matrix, symmetry coef)

    >>> embeddings = [[1., 1, 0, 3], [0, 1, 0, 1], [-1, 1, 1, 5]]
    >>> tuples_var = tf.Variable([[0, 1], [1, 0], [0, 2], [2, 0], [1, 2], [2, 1]])
    >>> (g, params) = hermitian_tuple_scorer(tuples_var, emb0=embeddings, symmetry_coef=(1.0, 0.0))
    >>> print(tf_eval(g))  # symmetric form
    [  4.   4.  15.  15.   6.   6.]
    >>> (g, params) = hermitian_tuple_scorer(tuples_var, emb0=embeddings, symmetry_coef=(0.0, 1.0))
    >>> print(tf_eval(g))  # skewed (anti-symmetric) form
    [-2.  2.  3. -3.  4. -4.]
    >>> (g, params) = hermitian_tuple_scorer(tuples_var, emb0=embeddings, symmetry_coef=(1.0, 1.0))
    >>> print(tf_eval(g))  # combination of the previous two forms
    [  2.   6.  18.  12.  10.   2.]
    >>> (g, params) = hermitian_tuple_scorer(tuples_var, emb0=embeddings, symmetry_coef=(0.9, 0.1))
    >>> print(tf_eval(g))  # close to symmetric
    [  3.39999986   3.79999995  13.80000019  13.19999981   5.79999971
       4.99999952]
    """
    emb0 = emb0 if emb0 is not None else np.random.normal(size=(n_emb, rank))
    embeddings = tf.Variable(tf.cast(emb0, 'float32'), 'embeddings')
    symmetry_coef = tf.Variable(symmetry_coef, name='symmetry_coef', trainable=learn_symmetry_coef)
    params = (embeddings, symmetry_coef)
    return sparse_hermitian_scoring(params, tuples_var), params