Source code for mindspore.nn.optim.momentum

# 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
# limitations under the License.
# ============================================================================
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 .optimizer import Optimizer

momentum_opt = C.MultitypeFuncGraph("momentum_opt")

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

[docs]class Momentum(Optimizer): """ Implements the Momentum algorithm. Refer to the paper on the importance of initialization and momentum in deep learning for more details. Args: params (list[Parameter]): A list of parameter, which will be updated. The element in `parameters` should be class mindspore.Parameter. 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. momentum (float): Hyperparameter of type float, means momentum for the moving average. weight_decay (float): Weight decay (L2 penalty). Default: 0.0. loss_scale (float): A floating point value for the loss scale. Default: 1.0. decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default: lambda x: 'beta' not in and 'gamma' not in 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() >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) >>> 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, decay_filter=lambda x: 'beta' not in and 'gamma' not in super(Momentum, self).__init__(learning_rate, params, weight_decay, loss_scale, decay_filter) 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.moments = self.params.clone(prefix="moments", init='zeros') self.hyper_map = C.HyperMap() self.opt = P.ApplyMomentum() def construct(self, gradients): params = self.params moments = self.moments gradients = self.decay_weight(gradients) gradients = self.scale_grad(gradients) lr = self.get_lr() success = self.hyper_map(F.partial(momentum_opt, self.opt, lr, self.momentum), gradients, params, moments) return success