# 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
# limitations under the License.
# ============================================================================
"""rmsprop"""
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from .optimizer import Optimizer
rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
@rmsprop_opt.register("Function", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _rmsprop_opt(opt, decay, epsilon, momentum, learning_rate, weight, ms, mom, grad):
"""Apply rmsprop optimizer to the weight parameter using dynamic learning rate."""
success = True
success = F.depend(success, opt(weight, ms, mom, learning_rate, grad, decay, momentum, epsilon))
return success
@centered_rmsprop_opt.register("Function", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor",
"Tensor", "Tensor")
def _centered_rmsprop_opt(opt, decay, epsilon, momentum, learning_rate, weight, mg, ms, mom, grad):
"""Apply centered rmsprop optimizer to the weight parameter using dynamic learning rate."""
success = True
success = F.depend(success, opt(weight, mg, ms, mom, grad, learning_rate, decay, momentum, epsilon))
return success
[docs]class RMSProp(Optimizer):
"""
Implements Root Mean Squared Propagation (RMSProp) algorithm.
Note:
The RMSProp 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.
Update `params` according to the RMSProp algorithm.
The equation is as follows:
.. math::
s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
.. math::
m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} + \\epsilon}} \\nabla Q_{i}(w)
.. math::
w = w - m_{t}
The first equation calculates moving average of the squared gradient for
each weight. Then dividing the gradient by :math:`\\sqrt{ms_{t} + \\epsilon}`.
if centered is True:
.. math::
g_{t} = \\rho g_{t-1} + (1 - \\rho)\\nabla Q_{i}(w)
.. math::
s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
.. math::
m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} - g_{t}^2 + \\epsilon}} \\nabla Q_{i}(w)
.. math::
w = w - m_{t}
where, :math:`w` represents `params`, which will be updated.
:math:`g_{t}` is mean gradients, :math:`g_{t-1}` is the last moment of :math:`g_{t}`.
:math:`s_{t}` is the mean square gradients, :math:`s_{t-1}` is the last moment of :math:`s_{t}`,
:math:`m_{t}` is moment, the delta of `w`, :math:`m_{t-1}` is the last moment of :math:`m_{t}`.
:math:`\\rho` represents `decay`. :math:`\\beta` is the momentum term, represents `momentum`.
:math:`\\epsilon` is a smoothing term to avoid division by zero, represents `epsilon`.
:math:`\\eta` is learning rate, represents `learning_rate`. :math:`\\nabla Q_{i}(w)` is gradientse,
represents `gradients`.
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.
decay (float): Decay rate. Should be equal to or greater than 0. Default: 0.9.
momentum (float): Hyperparameter of type float, means momentum for the moving average. Should be equal to or
greater than 0. Default: 0.0.
epsilon (float): Term added to the denominator to improve numerical stability. Should be greater than
0. Default: 1e-10.
use_locking (bool): Enable a lock to protect the update of variable and accumlation tensors. Default: False.
centered (bool): If True, gradients are normalized by the estimated variance of the gradient. Default: False.
loss_scale (float): A floating point value for the loss scale. Should be greater than 0. Default: 1.0.
weight_decay (float): Weight decay (L2 penalty). Should be equal to or greater than 0. Default: 0.0.
Inputs:
- **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
Outputs:
Tensor[bool], the value is True.
Examples:
>>> net = Net()
>>> #1) All parameters use the same learning rate and weight decay
>>> optim = nn.RMSProp(params=net.trainable_params(), learning_rate=lr)
>>>
>>> #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.RMSProp(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, decay=0.9, momentum=0.0, epsilon=1e-10,
use_locking=False, centered=False, loss_scale=1.0, weight_decay=0.0):
super(RMSProp, self).__init__(learning_rate, params, weight_decay, loss_scale)
validator.check_value_type("decay", decay, [float], self.cls_name)
validator.check_number_range("decay", decay, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
validator.check_value_type("momentum", momentum, [float], self.cls_name)
validator.check_number_range("momentum", momentum, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
validator.check_value_type("epsilon", epsilon, [float], self.cls_name)
validator.check_number_range("epsilon", epsilon, 0.0, float("inf"), Rel.INC_NEITHER, self.cls_name)
validator.check_value_type("use_locking", use_locking, [bool], self.cls_name)
validator.check_value_type("centered", centered, [bool], self.cls_name)
self.centered = centered
if centered:
self.opt = P.ApplyCenteredRMSProp(use_locking)
self.mg = self.parameters.clone(prefix="mean_grad", init='zeros')
else:
self.opt = P.ApplyRMSProp(use_locking)
self.momentum = momentum
self.ms = self.parameters.clone(prefix="mean_square", init='zeros')
self.moment = self.parameters.clone(prefix="moment", init='zeros')
self.hyper_map = C.HyperMap()
self.epsilon = epsilon
self.decay = decay
def construct(self, gradients):
params = self.parameters
gradients = self.decay_weight(gradients)
gradients = self.scale_grad(gradients)
lr = self.get_lr()
if self.centered:
if self.is_group_lr:
success = self.hyper_map(F.partial(centered_rmsprop_opt, self.opt, self.decay, self.epsilon,
self.momentum), lr, params, self.mg, self.ms, self.moment, gradients)
else:
success = self.hyper_map(F.partial(centered_rmsprop_opt, self.opt, self.decay, self.epsilon,
self.momentum, lr), params, self.mg, self.ms, self.moment, gradients)
else:
if self.is_group_lr:
success = self.hyper_map(F.partial(rmsprop_opt, self.opt, self.decay, self.epsilon,
self.momentum), lr, params, self.ms, self.moment, gradients)
else:
success = self.hyper_map(F.partial(rmsprop_opt, self.opt, self.decay, self.epsilon,
self.momentum, lr), params, self.ms, self.moment, gradients)
return success