mindspore.experimental.optim.RMSprop
- class mindspore.experimental.optim.RMSprop(params, lr=0.01, alpha=0.99, eps=1e-08, weight_decay=0.0, momentum=0.0, centered=False, maximize=False)[source]
Implements RMSprop 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
.alpha (float, optional) – smoothing constant. Default:
0.99
.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 (float, optional) – momentum factor. Default:
0.
.centered (bool, optional) – if
True
, compute the centered RMSProp, the gradient is normalized by an estimation of its variance. Default:False
.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 momentum is less than 0.0.
ValueError – If the alpha is less than 0.0.
ValueError – If the eps is less than 0.0.
ValueError – If the weight_decay is less than 0.
- 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.RMSprop(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