介绍常见的Loss损失函数SSE(和方差)MSE(均方误差,mean_squared_error)二分类交叉熵损失（binary_crossentropy）多类交叉熵损失（categorical_crossentropy）多类交叉熵损失（sparse_categorical_crossentropy）KL散度（相对熵）Focal loss
假设有m个数据输入X:x1,x2...xmX:x^1,x^2...x^mX:x1,x2...xm模型预测值为Y:y1,y2...ymY:y^1,y^2...y^mY:y1,y2...ym模型真实值为Y^:y^1,y^2...y^m\hat{Y}:\hat{y}^1,\hat{y}^2...\hat{y}^mY^:y^1,y^2...y^mSSE(和方差)SSE(Y,Y^)=∑i=1m(y^i−yi)2SSE(Y,\hat{Y})=\sum_{i=1}^{m}(\hat{y}_i -y_i)
![[Matlab]Simulink中data(from Workspace)的设置[Matlab]Simulink中data(from Workspace)的设置](/uploads/reation/bcimg12.png)
