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| data_mining:neural_network:gradient_descent [2018/05/12 20:12] – [Adam] phreazer | data_mining:neural_network:gradient_descent [2018/05/12 20:21] (current) – [Learning rate decay] phreazer | ||
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| * $\beta_2$: 0.999 | * $\beta_2$: 0.999 | ||
| * $\sigma$: $10^{-8}$ | * $\sigma$: $10^{-8}$ | ||
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| + | ===== Learning rate decay ===== | ||
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| + | $\alpha = \frac{1}{1+ \text{decay_rate} * \text{epoch_num}} \alpha_0$ | ||
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| + | or | ||
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| + | $\alpha = 0, | ||
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| + | |||
| + | ===== Saddle points ===== | ||
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| + | 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. | ||