# Source code for mindspore.nn.optim.momentum

# Copyright 2020 Huawei Technologies Co., Ltd
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"""momentum"""
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.ops import _selected_ops
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
from mindspore._checkparam import check_bool
from mindspore._checkparam import Validator as validator
from .optimizer import Optimizer

_momentum_opt = C.MultitypeFuncGraph("momentum_opt")

@_momentum_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Bool")
def _tensor_run_opt_ext(opt, momentum, learning_rate, gradient, weight, moment, ps_parameter):
"""Apply momentum optimizer to the weight parameter using Tensor."""
success = True
if ps_parameter:
op_shape = P.Shape()
_ps_pull = P.Pull()
_ps_push = P.Push("ApplyMomentum", [])
[docs]class Momentum(Optimizer): r""" Implements the Momentum algorithm. Refer to the paper on the importance of initialization and momentum in deep learning for more details. Note: When separating parameter groups, the weight decay in each group will be applied on the parameters if the weight decay is positive. When not separating parameter groups, the weight_decay in the API will be applied on the parameters without 'beta' or 'gamma' in their names if weight_decay is positive. To improve parameter groups performance, the customized order of parameters can be supported. .. math:: v_{t} = v_{t-1} \ast u + gradients If use_nesterov is True: .. math:: p_{t} = p_{t-1} - (grad \ast lr + v_{t} \ast u \ast lr) If use_nesterov is Flase: .. math:: p_{t} = p_{t-1} - lr \ast v_{t} Here: where grad, lr, p, v and u denote the gradients, learning_rate, params, moments, and momentum respectively. Args: params (Union[list[Parameter], list[dict]]): When the params is a list of Parameter which will be updated, the element in params should be class Parameter. When the params is a list of dict, the "params", "lr", "weight_decay" and "order_params" are the keys can be parsed. - params: Required. The value should be a list of Parameter. - lr: Optional. If "lr" in the keys, the value of corresponding learning rate will be used. If not, the learning_rate in the API will be used. - weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay will be used. If not, the weight_decay in the API will be used. - order_params: Optional. If "order_params" in the keys, the value should be the order of parameters and the order will be followed in optimizer. There are no other keys in the dict and the parameters which in the value of 'order_params' should be in one of group parameters. learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or a graph for the learning rate. When the learning_rate is an Iterable or a Tensor in a 1D dimension, use dynamic learning rate, then the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule, use dynamic learning rate, the i-th learning rate will be calculated during the process of training according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor in a zero dimension, use fixed learning rate. Other cases are not supported. The float learning rate should be equal to or greater than 0. If the type of learning_rate is int, it will be converted to float. momentum (float): Hyperparameter of type float, means momentum for the moving average. It should be at least 0.0. weight_decay (int, float): Weight decay (L2 penalty). It should be equal to or greater than 0.0. Default: 0.0. loss_scale (int, float): A floating point value for the loss scale. It should be greater than 0.0. Default: 1.0. use_nesterov (bool): Enable Nesterov momentum. Default: False. Inputs: - **gradients** (tuple[Tensor]) - The gradients of params, the shape is the same as params. Outputs: tuple[bool], all elements are True. Raises: ValueError: If the momentum is less than 0.0. Examples: >>> net = Net() >>> #1) All parameters use the same learning rate and weight decay >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) >>> >>> #2) Use parameter groups and set different values >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params())) >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params())) >>> group_params = [{'params': conv_params, 'weight_decay': 0.01}, >>> {'params': no_conv_params, 'lr': 0.01}, >>> {'order_params': net.trainable_params()}] >>> optim = nn.Momentum(group_params, learning_rate=0.1, momentum=0.9, weight_decay=0.0) >>> # The conv_params's parameters will use a learning rate of default value 0.1 and a weight decay of 0.01. >>> # The no_conv_params's parameters will use a learning rate of 0.01 and a weight decay of default value 0.0. >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'. >>> >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) """ def __init__(self, params, learning_rate, momentum, weight_decay=0.0, loss_scale=1.0, use_nesterov=False): super(Momentum, self).__init__(learning_rate, params, weight_decay, loss_scale) validator.check_value_type("momentum", momentum, [float], self.cls_name) if isinstance(momentum, float) and momentum < 0.0: raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum)) self.momentum = Parameter(Tensor(momentum, mstype.float32), name="momentum") self.params = self.parameters self.use_nesterov = check_bool(use_nesterov) self.moments = self.params.clone(prefix="moments", init='zeros') self.hyper_map = C.HyperMap() self.opt = _selected_ops.ApplyMomentum(use_nesterov=self.use_nesterov) def construct(self, gradients): params = self.params moments = self.moments gradients = self.decay_weight(gradients) gradients = self.scale_grad(gradients) lr = self.get_lr() if self.is_group_lr: success = self.hyper_map(F.partial(_momentum_opt, self.opt, self.momentum), lr, gradients, params, moments, self.ps_parameters) else: success = self.hyper_map(F.partial(_momentum_opt, self.opt, self.momentum, lr), gradients, params, moments, self.ps_parameters) return success