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://cloud.tencent.com/developer/article/1686529
激活函数的实现(sigmoid、softmax、tanh、relu、leakyrelu、elu、selu、softplus):https://cloud.tencent.com/developer/article/1686496
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:
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。