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| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| data_mining:neural_network:gradient_descent [2018/05/12 22:16] – [Adam] phreazer | data_mining:neural_network:gradient_descent [2018/05/12 22:21] (current) – [Learning rate decay] phreazer | ||
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| ===== Learning rate decay ===== | ===== Learning rate decay ===== | ||
| - | $\alpha = \frac{1}{1+ decay_rate * epoch_num} \alpha_0$ | + | $\alpha = \frac{1}{1+ |
| or | or | ||
| - | $\alpha = 0, | + | $\alpha = 0,95^{\text{epoch_num}} \alpha_0$ |
| + | ===== Saddle points ===== | ||
| + | In high-dimensional spaces it's more likely to end up at a saddle point (than in local optima). E.g. 20000 parameter, highly unlikely that it's a local minimum you get stuck. Plateus make learning slow. | ||