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"""
# -*- coding: utf-8 -*-
#
# Copyright 2021 Michael Büsch <m@bues.ch>
#
# Licensed under the Apache License version 2.0
# or the MIT license, at your option.
# SPDX-License-Identifier: Apache-2.0 OR MIT
#
"""
__all__ = [
"Loss",
"MAE",
"MSE",
]
from abc import ABC, abstractmethod
import numpy as np
class Loss(ABC):
@abstractmethod
def fn(self, yh, y):
"""Forward loss function.
yh: predicted value
y: expected value
"""
@abstractmethod
def fn_d(self, yh, y):
"""Loss function derivative.
"""
class MAE(Loss):
"""MAE Mean Absolute Error (L1) loss.
"""
def fn(self, yh, y):
assert yh.size == y.size
if y.size:
return np.absolute(y - yh).mean()
return 0.0
def fn_d(self, yh, y):
assert yh.size == y.size
return ((yh > y).astype(y.dtype) * 2.0) - 1.0
class MSE(Loss):
"""MSE Mean Squared Error (L2) loss.
"""
def fn(self, yh, y):
assert yh.size == y.size
if y.size:
return np.square(y - yh).mean()
return 0.0
def fn_d(self, yh, y):
assert yh.size == y.size
return yh - y
# vim: ts=4 sw=4 expandtab
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