class nbla::RMSpropGraves
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template<typename T>
class RMSpropGraves : public nbla::Solver -
This is defined as
\[\begin{split} n_t \leftarrow \rho n_{t-1} + \left(1 - \rho \right) e^2\\ g_t \leftarrow \rho g_{t-1} + \left(1 - \rho \right) e\\ d_t \leftarrow \beta d_{t-1} - \eta \frac{e}{\sqrt{n_t - {g_t}^2 + \epsilon}}\\ w_{t+1} \leftarrow w_t + d_t \end{split}\]See also
See the paper linked below for more details. A. Graves Generating Sequences With Recurrent Neural Networks http://arxiv.org/pdf/1308.0850v5.pdf
- Param lr:
\(\eta\) Learning rate.
- Param decay:
\(\rho\) Decay rate.
- Param momentum:
\(\beta\) Momentum.
- Param eps:
\(\epsilon\) Tiny factor for avoiding 0-division.
Public Functions
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inline virtual float learning_rate()
Set learning rate.