Source code for mindspore.nn.optim.sgd

# Copyright 2020 Huawei Technologies Co., Ltd
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# ============================================================================
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
from mindspore._checkparam import Validator as validator
from .optimizer import Optimizer

sgd_opt = C.MultitypeFuncGraph("sgd_opt")

@sgd_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt_ext(opt, momentum, learning_rate, gradient, weight, accum, stat):
    """Apply sgd optimizer to the weight parameter using Tensor."""
    success = True
    success = F.depend(success, opt(weight, gradient, learning_rate, accum, momentum, stat))
    return success

[docs]class SGD(Optimizer): """ Implements stochastic gradient descent (optionally with momentum). Introduction to SGD can be found at Nesterov momentum is based on the formula from paper `On the importance of initialization and momentum in deep learning <>`_. Note: The SGD optimizer supports separating parameter groups. Different parameter groups can set different `learning_rate` and `weight_decay`. When separating parameter groups, the weight decay in each group will be applied on the parameters if the value of weight_decay > 0. When not separating parameter groups, the `weight_decay` in the API will be applied on the parameters if `weight_decay` > 0 and the 'beta' and 'gamma' are not in the name of parameters. 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" and "weight_decay" 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. learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is Iterable or a Tensor and the dims of the Tensor is 1, use dynamic learning rate, then the i-th step will take the i-th value as the learning rate. When the learning_rate is float or learning_rate is a Tensor but the dims of the Tensor is 0, use fixed learning rate. Other cases are not supported. Default: 0.1. momentum (float): A floating point value the momentum. Default: 0.0. dampening (float): A floating point value of dampening for momentum. Default: 0.0. weight_decay (float): Weight decay (L2 penalty). Default: 0.0. nesterov (bool): Enables the Nesterov momentum. Default: False. loss_scale (float): A floating point value for the loss scale, which should be larger than 0.0. Default: 1.0. Inputs: - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`. Outputs: Tensor[bool], the value is True. Raises: ValueError: If the momentum, dampening or weight_decay value is less than 0.0. Examples: >>> net = Net() >>> #1) All parameters use the same learning rate and weight decay >>> optim = nn.SGD(params=net.trainable_params()) >>> >>> #2) Use parameter groups and set different values >>> conv_params = list(filter(lambda x: 'conv' in, net.trainable_params())) >>> no_conv_params = list(filter(lambda x: 'conv' not in, net.trainable_params())) >>> group_params = [{'params': conv_params, 'weight_decay': 0.01, 'lr': 0.01}, >>> {'params': no_conv_params}] >>> opt = nn.SGD(group_params, learning_rate=0.1, weight_decay=0.0) >>> # the conv_params's parameters will use a learning rate of 0.01 and a weight decay of 0.01 >>> # the no_cov_params's parameters don't set learning and weight decay. So they will use a >>> # learning rate of 0.1 and a weight decay of 0.0. >>> >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> model = Model(net, loss_fn=loss, optimizer=optim) """ def __init__(self, params, learning_rate=0.1, momentum=0.0, dampening=0.0, weight_decay=0.0, nesterov=False, loss_scale=1.0): super(SGD, self).__init__(learning_rate, params, weight_decay, loss_scale) if not isinstance(momentum, float): raise TypeError("momentum should be float number!") if isinstance(momentum, float) and momentum < 0.0: raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum)) if not isinstance(dampening, float): raise TypeError("dampening should be float number") if isinstance(dampening, int): dampening = float(dampening) if dampening < 0.0: raise ValueError("dampening should be at least 0.0, but got dampening {}".format(dampening)) self.dampening = dampening validator.check_value_type("nesterov", nesterov, [bool], self.cls_name) self.nesterov = nesterov self.opt = P.SGD(dampening, weight_decay, nesterov) self.momentum = Parameter(Tensor(momentum, mstype.float32), name="momentum") self.accum = self.parameters.clone(prefix="accum", init='zeros') self.stat = self.parameters.clone(prefix="stat", init='ones') self.hyper_map = C.HyperMap() def construct(self, gradients): params = self.parameters accum = self.accum stat = self.stat gradients = self.scale_grad(gradients) lr = self.get_lr() if self.is_group_lr: success = self.hyper_map(F.partial(sgd_opt, self.opt, self.momentum), lr, gradients, params, accum, stat) else: success = self.hyper_map(F.partial(sgd_opt, self.opt, self.momentum, lr), gradients, params, accum, stat) return success