# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ============================================================================
"""momentum"""
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 check_bool
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, momentum, learning_rate, 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.
Note:
The Momentum 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.
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.
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, 'lr': 0.01},
>>> {'params': no_conv_params}]
>>> opt = 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 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, 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)
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 = P.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)
else:
success = self.hyper_map(F.partial(momentum_opt, self.opt, self.momentum, lr), gradients, params, moments)
return success