ortho_gru_cell.py 文件源码

def orthogonal_initializer():
    """Return an orthogonal initializer.

    Random orthogonal matrix is byproduct of singular value decomposition
    applied on a matrix initialized with normal distribution.

    The initializer works with 2D square matrices and matrices that can be
    splitted along axis 1 to several 2D matrices. In the latter case, each
    submatrix is initialized independently and the resulting orthogonal
    matrices are concatenated along axis 1.

    Note this is a higher order function in order to mimic the tensorflow
    initializer API.
    """

    # pylint: disable=unused-argument
    def func(shape, dtype, partition_info=None):
        if len(shape) != 2:
            raise ValueError(
                "Orthogonal initializer only works with 2D matrices.")

        if shape[1] % shape[0] != 0:
            raise ValueError("Shape {} is not compatible with orthogonal "
                             "initializer.".format(str(shape)))

        mult = int(shape[1] / shape[0])
        dim = shape[0]

        orthogonals = []
        for _ in range(mult):
            matrix = tf.random_normal([dim, dim], dtype=dtype)
            orthogonals.append(tf.svd(matrix)[1])

        return tf.concat(orthogonals, 1)
    # pylint: enable=unused-argument

    return func


# pylint: disable=too-few-public-methods