def __call__(self, *xs):
"""Applies broadcasted elementwise summation.
Args:
xs (list of ~chainer.Variable): 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)
def __call__(self, x):
"""Apply layer normalization to given input.
Args:
x (~chainer.Variable): Batch vectors.
Shape of this value must be `(batch_size, unit_size)`,
e.g., the output of :func:`~chainer.functions.linear`.
Returns:
~chainer.Variable: Output of the layer normalization.
"""
if self.gamma.data is None:
self._initialize_params(x.size // x.shape[0])
normalized = self._normalize(x)
return bias.bias(scale.scale(normalized, self.gamma), self.beta)
def __call__(self, x):
"""Apply layer normalization to given input.
Args:
x (~chainer.Variable): Batch vectors.
Shape of this value must be `(batch_size, unit_size)`,
e.g., the output of :func:`~chainer.functions.linear`.
Returns:
~chainer.Variable: Output of the layer normalization.
"""
if self.has_uninitialized_params:
with cuda.get_device_from_id(self._device_id):
self._initialize_params(x.size // x.shape[0])
normalized = self._normalize(x)
return bias.bias(scale.scale(normalized, self.gamma), self.beta)