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/r2.3.0rc2/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