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.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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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, learning_rate, momentum, 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 <>`_. Args: params (list[Parameter]): A list of parameter, which will be updated. The element in `params` should be class mindspore.Parameter. learning_rate (float): A floating point value for the learning rate. Default: 0.1. momentum (float): A floating point value the momentum. Default: 0. dampening (float): A floating point value of dampening for momentum. Default: 0. weight_decay (float): Weight decay (L2 penalty). Default: 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() >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> optim = nn.SGD(params=net.trainable_params()) >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) """ 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.decay_weight(gradients) gradients = self.scale_grad(gradients) lr = self.get_lr() success = self.hyper_map(F.partial(sgd_opt, self.opt, lr, self.momentum), gradients, params, accum, stat) return success