mindspore.experimental.optim.RAdam

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class mindspore.experimental.optim.RAdam(params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.0)[source]

Implements RAdam algorithm.

\[\begin{split}\begin{align*} &\rule{180mm}{0.4pt} \\ &\textbf{Input}: \gamma \text{ (lr)}, \: \beta_1, \beta_2 \text{ (betas)}, \: \theta_0 \text{ (params)}, \:f(\theta) \text{ (objective)}, \: \lambda \text{ (weightdecay)}, \: \epsilon \text{ (epsilon)} \\ &\textbf{Initialize}: \begin{cases} m_0 \leftarrow 0 \text{ (first moment)} \\ v_0 \leftarrow 0 \text{ (second moment)} \\ \rho_{\infty} \xleftarrow{\text{def}} \dfrac{2}{1 - \beta_2} - 1 \end{cases} \\ &\rule{180mm}{0.4pt} \\ &\textbf{For } t = 1 \text{ to } \ldots \text{ do}: \\ &\quad g_t \leftarrow \nabla_{\theta} f_t(\theta_{t - 1}) \\ &\quad \text{If } \lambda \neq 0: \\ &\quad\quad g_t \leftarrow g_t + \lambda \theta_{t - 1} \\ &\quad m_t \leftarrow \beta_1 m_{t - 1} + (1 - \beta_1) g_t \\ &\quad v_t \leftarrow \beta_2 v_{t - 1} + (1 - \beta_2) g_t^2 \\ &\quad \widehat{m_t} \leftarrow \dfrac{m_t}{1 - \beta_1^t} \\ &\quad \text{Let } \rho_t' = 2 t \beta_2^t /(1 - \beta_2^t) \quad \text{(auxiliary variable)} \\ &\quad \rho_t \leftarrow \rho_{\infty} - \rho_t' \\ &\quad \text{If } \rho_t > 5: \\ &\quad\quad l_t \leftarrow \dfrac{\sqrt{1 - \beta_2^t}}{\sqrt{v_t} + \epsilon} \\ &\quad\quad r_t \leftarrow \sqrt{\dfrac{(\rho_t - 4)(\rho_t - 2)\rho_{\infty}}{(\rho_{\infty} - 4) (\rho_{\infty} - 2) \rho_t}} \\ &\quad\quad \theta_t \leftarrow \theta_{t - 1} - \gamma \widehat{m_t} r_t l_t \\ &\quad \text{Else}: \\ &\quad\quad \theta_t \leftarrow \theta_{t - 1} - \gamma \widehat{m_t} \\ &\rule{180mm}{0.4pt} \\ &\bf{Return}: \theta_t \\ &\rule{180mm}{0.4pt} \end{align*}\end{split}\]

Warning

This is an experimental optimizer API that is subject to change. This module must be used with lr scheduler module in LRScheduler Class .

Parameters

  • params (Union[list(Parameter), list(dict)]) – list of parameters to optimize or dicts defining parameter groups.

  • lr (Union[int, float, Tensor], optional) – learning rate. Default: 1e-3.

  • betas (Tuple[float, float], optional) – coefficients used for computing running averages of gradient and its square. Default: (0.9, 0.999).

  • eps (float, optional) – term added to the denominator to improve numerical stability. Default: 1e-8.

  • weight_decay (float, optional) – weight decay (L2 penalty). Default: 0.0.

Inputs:

  • gradients (tuple[Tensor]) - The gradients of params.

Raises

  • ValueError – If the learning rate is not int, float or Tensor.

  • ValueError – If the learning rate is less than 0.

  • ValueError – If the eps is less than 0.0.

  • ValueError – If the weight_decay is less than 0.

  • ValueError – If elements of betas not in the range of [0, 1).

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import nn
>>> from mindspore.experimental import optim
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/master/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> optimizer = optim.RAdam(net.trainable_params(), lr=0.1)
>>> def forward_fn(data, label):
...     logits = net(data)
...     loss = loss_fn(logits, label)
...     return loss, logits
>>> grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
>>> def train_step(data, label):
...     (loss, _), grads = grad_fn(data, label)
...     optimizer(grads)
...     return loss