5,337
edits
Deep Learning: Difference between revisions
→Loss functions
No edit summary |
|||
| Line 16: | Line 16: | ||
<math>l(f_W(x), y) = \frac{1}{2}\Vert f_W(x)-y \Vert^2</math> | <math>l(f_W(x), y) = \frac{1}{2}\Vert f_W(x)-y \Vert^2</math> | ||
For classification, can use hinge-loss: | For 2-way classification, can use hinge-loss: | ||
<math>l(f_W(x), y) = \max(0, 1-yf_W(x))</math> | <math>l(f_W(x), y) = \max(0, 1-yf_W(x))</math> | ||
For multi-way classification, can use cross-entropy loss: | |||
<math>g(z)=\frac{1}{1+e^{-z}}</math> | |||
<math>-\sum_{i=1}^{N}\left[y_i\log(y(f_W(x)) + (1-y_i)\log(1-g(f_W(x))\right]</math> | |||
==Misc== | ==Misc== | ||