/
mean_absolute_error.py
48 lines (36 loc) · 1.4 KB
/
mean_absolute_error.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
import numpy
import chainer
from chainer import backend
from chainer import function_node
from chainer.utils import type_check
class MeanAbsoluteError(function_node.FunctionNode):
"""Mean absolute error function."""
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x0', 'x1'))
type_check.expect(
in_types[0].dtype.kind == 'f',
in_types[0].dtype == in_types[1].dtype,
in_types[0].shape == in_types[1].shape
)
def forward_cpu(self, inputs):
x0, x1 = inputs
self.diff = x0 - x1
diff = self.diff.ravel()
return numpy.array(abs(diff).sum() / diff.size, dtype=diff.dtype),
def forward_gpu(self, inputs):
x0, x1 = inputs
self.diff = x0 - x1
diff = self.diff.ravel()
return abs(diff).sum() / diff.dtype.type(diff.size),
def backward(self, indexes, grad_outputs):
gy, = grad_outputs
coeff = gy * gy.data.dtype.type(1. / self.diff.size)
coeff = chainer.functions.broadcast_to(coeff, self.diff.shape)
gx0 = coeff * backend.get_array_module(gy.data).sign(self.diff)
return gx0, -gx0
def mean_absolute_error(x0, x1):
"""Mean absolute error function.
This function computes mean absolute error between two variables. The mean
is taken over the minibatch.
"""
return MeanAbsoluteError().apply((x0, x1))[0]