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mindspore.experimental.optim.Adamax

class mindspore.experimental.optim.Adamax(params, lr=0.002, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.0, *, maximize=False)

Implements Adamax algorithm (a variant of Adam based on infinity norm).

$$\begin{array}{r}\begin{array}{rl}& \\ & \mathbf{\text{input}}:\gamma \text{ (lr)},{\beta }_1,{\beta }_2\text{ (betas)},{\theta }_0\text{ (params)},f(\theta )\text{ (objective)},\lambda \text{ (weight decay)},\\ & ϵ\text{ (epsilon)}\\ & \mathbf{\text{initialize}}:m_0\leftarrow 0\text{ ( first moment)},u_0\leftarrow 0\text{ ( infinity norm)}\\ & \\ & \mathbf{\text{for}}t=1\mathbf{\text{to}}\dots \mathbf{\text{do}}\\ & g_t\leftarrow {\mathrm{\nabla }}_{\theta }{f}_t({\theta }_{t-1})\\ & if\lambda \ne 0\\ & g_t\leftarrow g_t+\lambda {\theta }_{t-1}\\ & m_t\leftarrow {\beta }_1{m}_{t-1}+(1-{\beta }_1)g_t\\ & u_t\leftarrow \max ({\beta }_2{u}_{t-1},|g_t|+ϵ)\\ & {\theta }_t\leftarrow {\theta }_{t-1}-\frac{\gamma m_t}{(1-{\beta }_1^t)u_t}\\ & \\ & \mathbf{r}\mathbf{e}\mathbf{t}\mathbf{u}\mathbf{r}\mathbf{n}{\theta }_{\mathbf{t}}\\ & \end{array}\end{array}$$

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: 2e-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..

Keyword Arguments

maximize (bool, optional) – maximize the params based on the objective, instead of minimizing. Default: False.

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 the 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/r2.3.0rc2/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> optimizer = optim.Adamax(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