Source code for chainer.links.connection.bias

import chainer
from chainer.functions.math import bias
from chainer import link


[docs]class Bias(link.Link): """Broadcasted elementwise summation with learnable parameters. Computes a elementwise summation as :func:`~chainer.functions.bias` function does except that its second input is a learnable bias parameter :math:`b` the link has. Args: axis (int): The first axis of the first input of :func:`~chainer.functions.bias` function along which its second input is applied. shape (tuple of ints): Shape of the learnable bias parameter. If ``None``, this link does not have learnable parameters so an explicit bias needs to be given to its ``__call__`` method's second input. .. seealso:: See :func:`~chainer.functions.bias` for details. Attributes: b (~chainer.Variable): Bias parameter if ``shape`` is given. Otherwise, no attributes. """ def __init__(self, axis=1, shape=None): super(Bias, self).__init__() # Add b parameter if given. if shape is not None: self.add_param('b', shape) self.b.data.fill(0) self.axis = axis def __call__(self, *xs): """Applies broadcasted elementwise summation. Args: xs (list of Variables): Input variables whose length should be one if the link has a learnable bias parameter, otherwise should be two. """ axis = self.axis # Case of only one argument where b is a learnt parameter. if hasattr(self, 'b'): if chainer.is_debug(): assert len(xs) == 1 x, = xs b = self.b return bias.bias(x, b, axis) # Case of two arguments where b is given as an argument. else: if chainer.is_debug(): assert len(xs) == 2 x, y = xs return bias.bias(x, y, axis)