Source code for mindspore.nn.optim.rmsprop

# Copyright 2020 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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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 .optimizer import Optimizer

rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")


@rmsprop_opt.register("Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor")
def _rmsprop_opt(opt, learning_rate, decay, epsilon, momentum, 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, grad, learning_rate, decay, momentum, epsilon))
    return success


@centered_rmsprop_opt.register("Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor",
                               "Tensor", "Tensor")
def _centered_rmsprop_opt(opt, learning_rate, decay, epsilon, momentum, 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: 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 (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. decay (float): Decay rate. momentum (float): Hyperparameter of type float, means momentum for the moving average. epsilon (float): Term added to the denominator to improve numerical stability. Should be greater than 0. 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. Default: 1.0. weight_decay (float): Weight decay (L2 penalty). Default: 0.0. decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default: lambda x: 'beta' not in x.name and 'gamma' not in x.name. 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() >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> opt = nn.RMSProp(params=net.trainable_params(), learning_rate=lr) >>> model = Model(net, loss, opt) """ 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, decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name): super(RMSProp, 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)) if decay < 0.0: raise ValueError("decay should be at least 0.0, but got dampening {}".format(decay)) self.decay = decay self.epsilon = epsilon 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.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: success = self.hyper_map(F.partial(centered_rmsprop_opt, self.opt, lr, self.decay, self.epsilon, self.momentum), params, self.mg, self.ms, self.moment, gradients) else: success = self.hyper_map(F.partial(rmsprop_opt, self.opt, lr, self.decay, self.epsilon, self.momentum), params, self.ms, self.moment, gradients) return success