AdamW¶
- class dragon.optimizers.AdamW(
 lr=0.001,
 beta1=0.9,
 beta2=0.999,
 eps=1e-08,
 weight_decay=0.01,
 **kwargs
 )[source]¶
- The optimizer to apply AdamW algorithm. [Loshchilov & Hutter, 2017]. - The AdamW update is defined as: \[\text{AdamW}(g, p) = \text{lr} * (\frac{\text{correction} * m_{t}} {\sqrt{v_{t}} + \epsilon} + \lambda p) \\ \quad \\ \text{where}\quad \begin{cases} \text{correction} = \sqrt{1 - \beta_{2}^{t}} / (1 - \beta_{1}^{t}) \\ m_{t} = \beta_{1} * m_{t-1} + (1 - \beta_{1}) * g \\ v_{t} = \beta_{2} * v_{t-1} + (1 - \beta_{2}) * g^{2} \\ \end{cases} \]
__init__¶
- AdamW.- __init__(
 lr=0.001,
 beta1=0.9,
 beta2=0.999,
 eps=1e-08,
 weight_decay=0.01,
 **kwargs
 )[source]¶
- Create an - AdamWupdater.- Parameters:
- lr (float, optional, default=0.001) – The initial value to \(\text{lr}\).
- beta1 (float, optional, default=0.9) – The initial value to \(\beta_{1}\).
- beta2 (float, optional, default=0.999) – The initial value to \(\beta_{2}\).
- eps (float, optional, default=1e-8) – The initial value to \(\epsilon\)
- weight_decay (float, optional, default=0.01) – The initial value to \(\lambda\).
 
 
Methods¶
apply_gradients¶
- Optimizer.- apply_gradients(grads_and_vars)[source]
- Apply the gradients on variables. - Parameters:
- grads_and_vars (Sequence[Sequence[dragon.Tensor]]) – The sequence of update pair.
 
 
