/
mean_absolute_error.py
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/
mean_absolute_error.py
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import numpy
import chainer
from chainer import backend
from chainer import function_node
from chainer.utils import type_check
def _get_intermediate_dtype(dtype):
# Returns the dtype for intermediate calculation.
# For float16 input, float32 is used.
# Otherwise the same dtype as the parameter is used.
if dtype == numpy.float16:
return numpy.float32
return dtype
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
orig_dtype = self.diff.dtype
dtype = _get_intermediate_dtype(orig_dtype)
diff = self.diff.ravel().astype(dtype, copy=False)
return numpy.array(abs(diff).sum() / diff.size, dtype=orig_dtype),
def forward_gpu(self, inputs):
x0, x1 = inputs
self.diff = x0 - x1
orig_dtype = self.diff.dtype
dtype = _get_intermediate_dtype(orig_dtype)
diff = self.diff.ravel().astype(dtype, copy=False)
return (abs(diff).sum() / diff.dtype.type(diff.size)).astype(
orig_dtype, copy=False),
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.
The function computes the mean absolute error between two variables. The
mean is taken over the minibatch. Args ``x0`` and ``x1`` must have the
same dimensions. This function first calculates the absolute value
differences between the corresponding elements in x0 and x1, and then
returns the mean of those differences.
Args:
x0 (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
x1 (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
Returns:
~chainer.Variable:
A variable holding an array representing the mean absolute
error of two inputs.
.. admonition:: Example
1D array examples:
>>> x = np.array([1, 2, 3]).astype(np.float32)
>>> y = np.array([0, 0, 0]).astype(np.float32)
>>> F.mean_absolute_error(x, y)
variable(2.)
>>> x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32)
>>> y = np.array([7, 8, 9, 10, 11, 12]).astype(np.float32)
>>> F.mean_absolute_error(x, y)
variable(6.)
2D array example:
In this example, there are 4 elements, and thus 4 errors
>>> x = np.array([[1, 2], [3, 4]]).astype(np.float32)
>>> y = np.array([[8, 8], [8, 8]]).astype(np.float32)
>>> F.mean_absolute_error(x, y)
variable(5.5)
3D array example:
In this example, there are 8 elements, and thus 8 errors
>>> x = np.reshape(np.array([1, 2, 3, 4, 5, 6, 7, 8]), (2, 2, 2))
>>> y = np.reshape(np.array([8, 8, 8, 8, 8, 8, 8, 8]), (2, 2, 2))
>>> x = x.astype(np.float32)
>>> y = y.astype(np.float32)
>>> F.mean_absolute_error(x, y)
variable(3.5)
"""
return MeanAbsoluteError().apply((x0, x1))[0]