神经网络作业: NN LEARNING Coursera Machine Learning（Andrew Ng） WEEK 5
在WEEK 5中，作业要求完成通过神经网络(NN)实现多分类的逻辑回归(MULTI-CLASS LOGISTIC REGRESSION)的监督学习(SUOERVISED LEARNING)来识别阿拉伯数字。作业主要目的是感受如何在NN中求代价函数（COST FUNCTION）和其假设函数中各个参量(THETA)的求导值（GRADIENT DERIVATIVE)（利用BACKPROPAGGATION）。
难度不高，但问题是你要习惯使用MATLAB的矩阵QAQ，作为一名蒟蒻，我已经狗带了。以下代核心部分的代码希望给被作业卡住的同学一些帮助。但请不要照搬代码哦~不要~不要~
1 ty = zeros(m, num_labels); 2 3 for i=1:m 4 for j=1:num_labels 5 if y(i)==j 6 ty(i,j) = 1; 7 end 8 end 9 end 10 11 a1 = X; 12 a1 = [ones(size(a1,1),1) a1]; 13 z2 = a1 * Theta1'; 14 a2 = sigmoid(z2); 15 a2 = [ones(size(a2,1),1) a2]; 16 z3 = a2 * Theta2'; 17 a3 = sigmoid(z3); 18 19 20 for i=1:m 21 for j=1:num_labels 22 J = J - log(1-a3(i,j))*(1-ty(i,j))/m-log(a3(i,j))*ty(i,j)/m; 23 end 24 end 25 26 %size(J,1) 27 %size(J,2) 28 29 d3 = a3 - ty; 30 d2 = (d3 * Theta2(:,2:end)).*sigmoidGradient(z2); 31 Theta1_grad = Theta1_grad + d2'*a1/m; 32 Theta2_grad = Theta2_grad + d3'*a2/m; 33 34 % ------------------------------------------------------------- 35 JJ=0; 36 37 for i=1:size(Theta1,1) 38 for j=2:size(Theta1,2) 39 JJ = JJ + Theta1(i,j)*Theta1(i,j)*lambda/(m*2); 40 end 41 end 42 size(Theta1,1); 43 size(Theta1,2); 44 45 for i=1:size(Theta2,1) 46 for j=2:size(Theta2,2) 47 JJ = JJ + Theta2(i,j)*Theta2(i,j)*lambda/(2*m); 48 end 49 end 50 size(Theta2,1); 51 size(Theta2,2); 52 %J = J + (lambda/(2*m)) * (Theta1(:,2:end).*Theta1(:,2:end)+Theta2(2:end,:).*Theta2(2:end,:)); 53 J =J+JJ; 54 55 Theta1_gradd = zeros(size(Theta1)); 56 Theta2_gradd = zeros(size(Theta2)); 57 58 for i=2:size(Theta1,2) 59 for j=1:size(Theta1,1) 60 Theta1_gradd(j,i) = Theta1(j,i)*lambda/m; 61 end 62 end 63 64 for i=2:size(Theta2,2) 65 for j=1:size(Theta2,1) 66 Theta2_gradd(j,i) = Theta2(j,i)*lambda/m; 67 end 68 end 69 70 Theta1_grad = Theta1_gradd+Theta1_grad; 71 Theta2_grad = Theta2_gradd+Theta2_grad;
PS:博主蒟蒻强迫自己下次要写矩阵运算，不能再套循环啦！！！
