bilinear.py 文件源码

def __init__(self, left_size, right_size, out_size, nobias=False,
                 initialW=None, initial_bias=None):
        super(Bilinear, self).__init__(W=(left_size, right_size, out_size))
        self.in_sizes = (left_size, right_size)
        self.nobias = nobias

        # TODO(Kenta OONO): I do not know appropriate way of
        # initializing weights in tensor network.
        # This initialization is a modification of
        # that of Linear function.

        if isinstance(initialW, (numpy.ndarray, cuda.ndarray)):
            assert initialW.shape == self.W.data.shape
        initializers.init_weight(self.W.data, initialW)

        if not self.nobias:
            self.add_param('V1', (left_size, out_size))
            self.add_param('V2', (right_size, out_size))
            self.add_param('b', out_size)

            if isinstance(initial_bias, tuple):
                V1, V2, b = initial_bias
            elif initial_bias is None:
                V1 = V2 = None
                b = 0
            else:
                raise ValueError('initial_bias must be tuple or None')

            if isinstance(V1, (numpy.ndarray, cuda.ndarray)):
                assert V1.shape == self.V1.data.shape
            if isinstance(V2, (numpy.ndarray, cuda.ndarray)):
                assert V2.shape == self.V2.data.shape
            if isinstance(b, (numpy.ndarray, cuda.ndarray)):
                assert b.shape == self.b.data.shape
            initializers.init_weight(self.V1.data, V1)
            initializers.init_weight(self.V2.data, V2)
            initializers.init_weight(self.b.data, b)