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【python实现卷积神经网络】损失函数的定义(均方误差损失、交叉熵损失)

代码来源:https://github.com/eriklindernoren/ML-From-Scratch

卷积神经网络中卷积层Conv2D(带stride、padding)的具体实现:https://www.cnblogs.com/xiximayou/p/12706576.html

激活函数的实现(sigmoid、softmax、tanh、relu、leakyrelu、elu、selu、softplus):https://www.cnblogs.com/xiximayou/p/12713081.html

 

这节讲解两个基础的损失函数的实现:

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)

其中y是真实值对应的标签,p是预测值对应的标签。

补充:

  • numpy.clip():看个例子

    import numpy as np
    x=np.array([1,2,3,5,6,7,8,9])
    np.clip(x,3,8)
    array([3, 3, 3, 5, 6, 7, 8, 8])

这里使用到了mlfromscrach/utils/data_operation.py中的:

def accuracy_score(y_true, y_pred):
    """ Compare y_true to y_pred and return the accuracy """
    accuracy = np.sum(y_true == y_pred, axis=0) / len(y_true)
    return accuracy

用于计算准确率。

 

posted @ 2020-04-16 15:29  西西嘛呦  阅读(2987)  评论(0编辑  收藏  举报