MATLAB 损失函数画图

损失函数画图

• Hinge loss function：

$$H(z) = max(0,1-z)$$

• $\psi$-learning loss function: 

$${\phi _s}(z) = \left\{ {\begin{array}{*{20}{c}} s&{z < 0}\\ 0&{z \ge 0} \end{array}} \right.$$

• Normalized Sigmoid loss:

$${P_t}(z) = 1 - \tanh (tz)$$

• Ramp loss function:

$${R_s}(z) = \left\{ {\begin{array}{*{20}{c}} 0&{z > 0}\\ {1 - z}&{0 \le z \le 1}\\ {1 - s}&{z > 1} \end{array}} \right.$$

 

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%plot loss function   %define the loss function H = @(z)max(0,1-z) ; %Hinge loss function P = @(z)(2*(z<0)+0*(z>=0)); %\psi-learning loss function S = @(z)(1-tanh(2*z)); %Normalized Sigmoid loss function R = @(z)(1*(z<0)+(1-z).*(z>=0&z<1)+0*(z>=1)); % ramp loss   z=-2:0.01:2; subplot(1,4,1) % plot the 1st figure of 1-4 plot(z,H(z),'-','linewidth',2); xlabel('z'); title('Hinge loss','fontweight','normal','fontsize',10); axis([-2,2 0 3])   subplot(1,4,2) plot(z,P(z),'g-','linewidth',2); xlabel('z'); title('\psi-learnig loss','fontweight','normal','fontsize',10); axis([-2,2 0 3])   subplot(1,4,3) plot(z,S(z),'r-','linewidth',2); xlabel('z'); title('Normalized Sigmoid loss','fontweight','normal','fontsize',10); axis([-2,2 0 3])   subplot(1,4,4) plot(z,R(z),'b-','linewidth',2); xlabel('z'); title('Ramp loss','fontweight','normal','fontsize',10); axis([-2,2 0 3])