Numpy实现Loss函数

from __future__ import division
import numpy as np
from mlfromscratch.utils import accuracy_score
from mlfromscratch.deep_learning.activation_functions import Sigmoid

class Loss(object):
    def loss(self, y_true, y_pred):
        return NotImplementedError()

    def gradient(self, y, y_pred):
        raise NotImplementedError()

    def acc(self, y, y_pred):
        return 0

class SquareLoss(Loss):
    def __init__(self): pass

    def loss(self, y, y_pred):
        return 0.5 * np.power((y - y_pred), 2)

    def gradient(self, y, y_pred):
        return -(y - y_pred)

class CrossEntropy(Loss):
    def __init__(self): pass

    def loss(self, y, p):
        # Avoid division by zero
        p = np.clip(p, 1e-15, 1 - 1e-15)
        return - y * np.log(p) - (1 - y) * np.log(1 - p)

    def acc(self, y, p):
        return accuracy_score(np.argmax(y, axis=1), np.argmax(p, axis=1))

    def gradient(self, y, p):
        # Avoid division by zero
        p = np.clip(p, 1e-15, 1 - 1e-15)
        return - (y / p) + (1 - y) / (1 - p)




  

 

  
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文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。

原文链接：wanghao.blog.csdn.net/article/details/120321869