Source code for mindspore.nn.optim.rmsprop

# Copyright 2020 Huawei Technologies Co., Ltd
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"""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: When separating parameter groups, the weight decay in each group will be applied on the parameters if the weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive. To improve parameter groups performance, the customized order of parameters can be supported. 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", "weight_decay" and "order_params" 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. - order_params: Optional. If "order_params" in the keys, the value should be the order of parameters and the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which in the value of 'order_params' should be in one of group parameters. learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or graph for the learning rate. When the learning_rate is a Iterable or a Tensor with dimension of 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 LearningRateSchedule, use dynamic learning rate, the i-th learning rate will be calculated during the process of training according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor with dimension of 0, use fixed learning rate. Other cases are not supported. The float learning rate should be equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float. 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}, >>> {'params': no_conv_params, 'lr': 0.01}, >>> {'order_params': net.trainable_params()}] >>> optim = nn.RMSProp(group_params, learning_rate=0.1, weight_decay=0.0) >>> # The conv_params's parameters will use a learning rate of default value 0.1 and a weight decay of 0.01. >>> # The no_conv_params's parameters will use a learning rate of 0.01 and a weight decay of default value 0.0. >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'. >>> >>> 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='ones') 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