Source code for mindspore.experimental.optim.rprop

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"""rprop"""
from __future__ import absolute_import

from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.common import Tensor, Parameter
import mindspore.common.dtype as mstype
from mindspore import _checkparam as validator
from mindspore.experimental.optim.optimizer import Optimizer, check_not_less_than_without_equal
from mindspore import ops
from mindspore import jit

_rprop_opt = C.MultitypeFuncGraph("rprop_opt")

op_sign = P.Sign()
op_fill = P.FillV2()
op_assign = P.Assign()
op_assignadd = P.AssignAdd()
op_cast = P.Cast()
op_select = P.Select()
op_oneslike = P.OnesLike()


@_rprop_opt.register("Tensor", "Tensor", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt(etaminus, etaplus, step_size_min, step_size_max, step, lr, param, prev, step_size, gradient):
    """Apply rprop optimizer to the weight parameter."""
    if step == 1:
        step_size_value = op_oneslike(step_size) * lr
    else:
        step_size_value = step_size.value()

    sign = op_sign(gradient * prev)

    sign[sign.gt(0)] = etaplus
    sign[sign.lt(0)] = etaminus
    sign[sign.eq(0)] = 1

    step_size_clip = ops.clip_by_value(step_size_value * sign, step_size_min, step_size_max)
    op_assign(step_size, step_size_clip)

    gradient_update = op_select(sign == etaminus, op_fill(sign.shape, op_cast(0., mstype.float32)), gradient)

    op_assign(prev, gradient_update)
    next_param = param - op_sign(gradient_update) * step_size_clip

    op_assign(param, next_param)

    return True


[docs]class Rprop(Optimizer): r""" 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 <https://www.mindspore.cn/docs/en/r2.3.0rc2/api_python/mindspore.experimental.html#lrscheduler-class>`_ . Args: 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 Args: 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 """ def __init__(self, params, lr=1e-2, etas=(0.5, 1.2), step_sizes=(1e-6, 50), *, maximize=False): check_not_less_than_without_equal(lr, "lr", self.cls_name) check_not_less_than_without_equal(etas[1], "etas[1]", self.cls_name, 1.) validator.check_float_range(etas[0], 0., 1., validator.INC_NEITHER, "etas[0]", self.cls_name) defaults = dict( lr=lr, etas=etas, step_sizes=step_sizes, maximize=maximize, ) super(Rprop, self).__init__(params, defaults) self.prev = self.parameters.clone(prefix="prev", init='zeros') self.step_size = self.parameters.clone(prefix="step_size", init='zeros') self.step_t = Parameter(Tensor(0, mstype.int32), "step_t") self.increase_tensor = Tensor(1, mstype.int32) self.op_cast = P.Cast() @jit def implementation(self, etaminus, etaplus, group_id, lr, gradients, maximize, step_size_min, step_size_max): """Extract the common computing part for acceleration""" etaminus, etaplus = op_cast(etaminus, mstype.float32), op_cast(etaplus, mstype.float32) start_id = self.group_start_id[group_id] end_id = self.group_start_id[group_id + 1] params = self.parameters[start_id: end_id] grads = tuple([grad if not maximize else F.neg(grad) for grad in gradients[start_id: end_id]]) prev = self.prev[start_id: end_id] step_size = self.step_size[start_id: end_id] self.hyper_map(F.partial(_rprop_opt, etaminus, etaplus, step_size_min, step_size_max, self.step_t, lr), params, prev, step_size, grads) return True def construct(self, gradients): op_assignadd(self.step_t, self.increase_tensor) for group_id, group in enumerate(self.param_groups): lr = self.lrs[group_id] if isinstance(group.get("lr"), float): lr = self.op_cast(group.get("lr"), mstype.float32) maximize = group.get("maximize") etaminus, etaplus = group["etas"] step_size_min, step_size_max = group["step_sizes"] self.implementation(etaminus, etaplus, group_id, lr, gradients, maximize, step_size_min, step_size_max) return True