mindspore.experimental.optim.Optimizer
- class mindspore.experimental.optim.Optimizer(params, defaults)[source]
Base class for all optimizers.
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
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> import mindspore >>> from mindspore import nn, Tensor, Parameter >>> from mindspore import ops >>> from mindspore.experimental import optim >>> >>> class MySGD(optim.Optimizer): ... def __init__(self, params, lr): ... defaults = dict(lr=lr) ... super(MySGD, self).__init__(params, defaults) ... ... def construct(self, gradients): ... for group_id, group in enumerate(self.param_groups): ... id = self.group_start_id[group_id] ... for i, param in enumerate(group["params"]): ... next_param = param + gradients[id+i] * group["lr"] ... ops.assign(param, next_param) >>> >>> net = nn.Dense(8, 2) >>> data = Tensor(np.random.rand(20, 8).astype(np.float32)) >>> label = Tensor(np.random.rand(20, 2).astype(np.float32)) >>> >>> optimizer = MySGD(net.trainable_params(), 0.01) >>> optimizer.add_param_group({"params": Parameter([0.01, 0.02])}) >>> >>> criterion = nn.MAELoss(reduction="mean") >>> >>> def forward_fn(data, label): ... logits = net(data) ... loss = criterion(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) ... print(loss) >>> >>> train_step(data, label)