mindspore.experimental.optim.Rprop

class mindspore.experimental.optim.Rprop(params, lr=0.01, etas=(0.5, 1.2), step_sizes=(1e-06, 50), *, maximize=False)[source]

Implements Rprop 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: 1e-2.

  • etas (Tuple[float, float], optional) – pair of (etaminus, etaplus), that are multiplicative increase and decrease factors. Default:(0.5, 1.2)

  • step_sizes (Tuple[float, float], optional) – a pair of minimal and maximal allowed step sizes. Default:(1e-6, 50)

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 etas[1] is less than or equal to 1.0.

  • ValueError – If the etas[0] 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.Rprop(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