import numpy
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer.utils import type_check
class MeanAbsoluteError(function_node.FunctionNode):
"""Mean absolute error function."""
def __init__(self, ignore_nan=False):
# TODO(mottodora): implement task weight calculation
self.ignore_nan = ignore_nan
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 2)
type_check.expect(
in_types[0].dtype == numpy.float32,
in_types[1].dtype == numpy.float32,
in_types[0].shape == in_types[1].shape
)
def forward_cpu(self, inputs):
self.retain_inputs((0, 1))
x0, x1 = inputs
diff = (inputs[0] - inputs[1]).ravel()
# TODO(mottodora): add reduce option
if self.ignore_nan:
diff[numpy.isnan(diff)] = 0.
return numpy.array(abs(diff).sum() / diff.size, dtype=diff.dtype),
def forward_gpu(self, inputs):
self.retain_inputs((0, 1))
cupy = cuda.cupy
diff = (inputs[0] - inputs[1]).ravel()
if self.ignore_nan:
diff[cupy.isnan(diff)] = 0.
return abs(diff).sum() / diff.dtype.type(diff.size),
def backward(self, indexes, gy):
x0, x1 = self.get_retained_inputs()
xp = cuda.get_array_module(x0)
diff = x0 - x1
if self.ignore_nan:
diff = chainer.functions.where(xp.isnan(diff.array),
xp.zeros_like(diff.array), diff)
gy0 = chainer.functions.broadcast_to(gy[0], diff.shape)
gx0 = gy0 * chainer.functions.sign(diff) * 1. / diff.size
return gx0, -gx0
[docs]def mean_absolute_error(x0, x1, ignore_nan=False):
"""Mean absolute error function.
This function computes mean absolute error between two variables. The mean
is taken over the minibatch.
Args:
x0 (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`): Input variable.
x1 (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`): Input variable.
ignore_nan (bool): If `True`, this function compute mean absolute error
ignoring NaNs. The arithmetic mean is the sum of the non-NaN
elements along the axis divided by the number of whole elements.
Returns:
~chainer.Variable:
A variable holding an array representing the mean absolute
error of two inputs.
"""
return MeanAbsoluteError(ignore_nan).apply((x0, x1))[0]