mindspore.experimental.optim.NAdam

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class mindspore.experimental.optim.NAdam(params, lr=0.002, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.0, momentum_decay=0.004)[source]

Implements NAdam algorithm.

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..

  • momentum_decay (float, optional) – momentum momentum_decay. Default: 4e-3.

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 the momentum_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.NAdam(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