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from chainer import backend
from chainer import function
from chainer import utils
from chainer.utils import type_check
class DeCov(function.Function):
"""DeCov loss ("""
def __init__(self, reduce='half_squared_sum'):
self.h_centered = None
self.covariance = None
if reduce not in ('half_squared_sum', 'no'):
raise ValueError(
'only \'half_squared_sum\' and \'no\' are valid '
'for \'reduce\', but \'%s\' is given' % reduce)
self.reduce = reduce
def check_type_forward(self, in_types):
type_check._argname(in_types, ('h',))
h_type, = in_types
h_type.dtype.kind == 'f',
h_type.ndim == 2,
def forward(self, inputs):
xp = backend.get_array_module(*inputs)
h, = inputs
self.h_centered = h - h.mean(axis=0, keepdims=True)
self.covariance =
xp.fill_diagonal(self.covariance, 0.0)
self.covariance /= len(h)
if self.reduce == 'half_squared_sum':
cost = xp.vdot(self.covariance, self.covariance)
cost *= h.dtype.type(0.5)
return utils.force_array(cost),
return self.covariance,
def backward(self, inputs, grad_outputs):
xp = backend.get_array_module(*inputs)
h, = inputs
gcost, = grad_outputs
gcost_div_n = gcost / gcost.dtype.type(len(h))
if self.reduce == 'half_squared_sum':
gh = 2.0 *
gh *= gcost_div_n
xp.fill_diagonal(gcost_div_n, 0.0)
gh = + gcost_div_n.T)
return gh,
def decov(h, reduce='half_squared_sum'):
"""Computes the DeCov loss of ``h``
The output is a variable whose value depends on the value of
the option ``reduce``. If it is ``'no'``, it holds a matrix
whose size is same as the number of columns of ``y``.
If it is ``'half_squared_sum'``, it holds the half of the
squared Frobenius norm (i.e. squared of the L2 norm of a matrix flattened
to a vector) of the matrix.
h (:class:`~chainer.Variable` or :ref:`ndarray`):
Variable holding a matrix where the first dimension
corresponds to the batches.
recude (str): Reduction option. Its value must be either
``'half_squared_sum'`` or ``'no'``.
Otherwise, :class:`ValueError` is raised.
A variable holding a scalar of the DeCov loss.
If ``reduce`` is ``'no'``, the output variable holds
2-dimensional array matrix of shape ``(N, N)`` where
``N`` is the number of columns of ``y``.
If it is ``'half_squared_sum'``, the output variable
holds a scalar value.
.. note::
See for details.
return DeCov(reduce)(h)
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