forked from huawei/mindspore2022
199 lines
11 KiB
Python
199 lines
11 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""rmsprop"""
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from mindspore.ops import functional as F, composite as C, operations as P
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from mindspore._checkparam import Validator as validator
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from mindspore._checkparam import Rel
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from .optimizer import Optimizer
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_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
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_centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
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@_rmsprop_opt.register("Function", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
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def _rmsprop_opt_(opt, decay, epsilon, momentum, learning_rate, weight, ms, mom, grad):
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"""Apply rmsprop optimizer to the weight parameter using dynamic learning rate."""
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success = True
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success = F.depend(success, opt(weight, ms, mom, learning_rate, grad, decay, momentum, epsilon))
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return success
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@_centered_rmsprop_opt.register("Function", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor",
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"Tensor", "Tensor")
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def _centered_rmsprop_opt_(opt, decay, epsilon, momentum, learning_rate, weight, mg, ms, mom, grad):
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"""Apply centered rmsprop optimizer to the weight parameter using dynamic learning rate."""
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success = True
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success = F.depend(success, opt(weight, mg, ms, mom, grad, learning_rate, decay, momentum, epsilon))
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return success
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class RMSProp(Optimizer):
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"""
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Implements Root Mean Squared Propagation (RMSProp) algorithm.
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Note:
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When separating parameter groups, the weight decay in each group will be applied on the parameters if the
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weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied
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on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive.
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To improve parameter groups performance, the customized order of parameters can be supported.
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Update `params` according to the RMSProp algorithm.
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The equation is as follows:
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.. math::
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s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
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.. math::
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m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} + \\epsilon}} \\nabla Q_{i}(w)
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.. math::
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w = w - m_{t}
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The first equation calculates moving average of the squared gradient for
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each weight. Then dividing the gradient by :math:`\\sqrt{ms_{t} + \\epsilon}`.
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if centered is True:
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.. math::
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g_{t} = \\rho g_{t-1} + (1 - \\rho)\\nabla Q_{i}(w)
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.. math::
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s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
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.. math::
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m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} - g_{t}^2 + \\epsilon}} \\nabla Q_{i}(w)
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.. math::
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w = w - m_{t}
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where, :math:`w` represents `params`, which will be updated.
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:math:`g_{t}` is mean gradients, :math:`g_{t-1}` is the last moment of :math:`g_{t}`.
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:math:`s_{t}` is the mean square gradients, :math:`s_{t-1}` is the last moment of :math:`s_{t}`,
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:math:`m_{t}` is moment, the delta of `w`, :math:`m_{t-1}` is the last moment of :math:`m_{t}`.
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:math:`\\rho` represents `decay`. :math:`\\beta` is the momentum term, represents `momentum`.
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:math:`\\epsilon` is a smoothing term to avoid division by zero, represents `epsilon`.
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:math:`\\eta` is learning rate, represents `learning_rate`. :math:`\\nabla Q_{i}(w)` is gradientse,
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represents `gradients`.
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Args:
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params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated,
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the element in `params` should be class `Parameter`. When the `params` is a list of `dict`, the "params",
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"lr", "weight_decay" and "order_params" are the keys can be parsed.
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- params: Required. The value should be a list of `Parameter`.
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- lr: Optional. If "lr" in the keys, the value of corresponding learning rate will be used.
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If not, the `learning_rate` in the API will be used.
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- weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay
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will be used. If not, the `weight_decay` in the API will be used.
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- order_params: Optional. If "order_params" in the keys, the value should be the order of parameters and
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the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
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in the value of 'order_params' should be in one of group parameters.
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learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or graph for the learning rate.
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When the learning_rate is a Iterable or a Tensor with dimension of 1, use dynamic learning rate, then
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the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule,
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use dynamic learning rate, the i-th learning rate will be calculated during the process of training
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according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor with
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dimension of 0, use fixed learning rate. Other cases are not supported. The float learning rate should be
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equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
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Default: 0.1.
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decay (float): Decay rate. Should be equal to or greater than 0. Default: 0.9.
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momentum (float): Hyperparameter of type float, means momentum for the moving average. Should be equal to or
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greater than 0. Default: 0.0.
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epsilon (float): Term added to the denominator to improve numerical stability. Should be greater than
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0. Default: 1e-10.
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use_locking (bool): Enable a lock to protect the update of variable and accumlation tensors. Default: False.
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centered (bool): If True, gradients are normalized by the estimated variance of the gradient. Default: False.
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loss_scale (float): A floating point value for the loss scale. Should be not less than 1.0. Default: 1.0.
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weight_decay (float): Weight decay (L2 penalty). Should be in range [0.0, 1.0]. Default: 0.0.
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Inputs:
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- **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
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Outputs:
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Tensor[bool], the value is True.
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Examples:
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>>> net = Net()
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>>> #1) All parameters use the same learning rate and weight decay
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>>> optim = nn.RMSProp(params=net.trainable_params(), learning_rate=lr)
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>>>
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>>> #2) Use parameter groups and set different values
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>>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
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>>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
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>>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
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>>> {'params': no_conv_params, 'lr': 0.01},
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>>> {'order_params': net.trainable_params()}]
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>>> optim = nn.RMSProp(group_params, learning_rate=0.1, weight_decay=0.0)
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>>> # The conv_params's parameters will use a learning rate of default value 0.1 and a weight decay of 0.01.
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>>> # The no_conv_params's parameters will use a learning rate of 0.01 and a weight decay of default value 0.0.
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>>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
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>>>
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>>> loss = nn.SoftmaxCrossEntropyWithLogits()
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>>> model = Model(net, loss_fn=loss, optimizer=optim)
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"""
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def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,
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use_locking=False, centered=False, loss_scale=1.0, weight_decay=0.0):
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super(RMSProp, self).__init__(learning_rate, params, weight_decay, loss_scale)
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validator.check_value_type("decay", decay, [float], self.cls_name)
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validator.check_number_range("decay", decay, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
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validator.check_value_type("momentum", momentum, [float], self.cls_name)
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validator.check_number_range("momentum", momentum, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
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validator.check_value_type("epsilon", epsilon, [float], self.cls_name)
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validator.check_number_range("epsilon", epsilon, 0.0, float("inf"), Rel.INC_NEITHER, self.cls_name)
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validator.check_value_type("use_locking", use_locking, [bool], self.cls_name)
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validator.check_value_type("centered", centered, [bool], self.cls_name)
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self.centered = centered
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if centered:
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self.opt = P.ApplyCenteredRMSProp(use_locking)
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self.mg = self.parameters.clone(prefix="mean_grad", init='zeros')
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else:
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self.opt = P.ApplyRMSProp(use_locking)
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self.momentum = momentum
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self.ms = self.parameters.clone(prefix="mean_square", init='ones')
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self.moment = self.parameters.clone(prefix="moment", init='zeros')
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self.hyper_map = C.HyperMap()
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self.epsilon = epsilon
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self.decay = decay
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def construct(self, gradients):
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params = self.parameters
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gradients = self.decay_weight(gradients)
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gradients = self.scale_grad(gradients)
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lr = self.get_lr()
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if self.centered:
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if self.is_group_lr:
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success = self.hyper_map(F.partial(_centered_rmsprop_opt, self.opt, self.decay, self.epsilon,
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self.momentum), lr, params, self.mg, self.ms, self.moment, gradients)
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else:
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success = self.hyper_map(F.partial(_centered_rmsprop_opt, self.opt, self.decay, self.epsilon,
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self.momentum, lr), params, self.mg, self.ms, self.moment, gradients)
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else:
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if self.is_group_lr:
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success = self.hyper_map(F.partial(_rmsprop_opt, self.opt, self.decay, self.epsilon,
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self.momentum), lr, params, self.ms, self.moment, gradients)
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else:
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success = self.hyper_map(F.partial(_rmsprop_opt, self.opt, self.decay, self.epsilon,
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self.momentum, lr), params, self.ms, self.moment, gradients)
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return success
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