mindspore.experimental.optim.adadelta 源代码

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"""adadelta"""
from __future__ import absolute_import

from mindspore.ops import functional as F, composite as C, operations as P
import mindspore.common.dtype as mstype
from mindspore.experimental.optim.optimizer import Optimizer, check_not_less_than, check_not_less_than_without_equal
from mindspore import _checkparam as validator
from mindspore import jit

_adadelta_opt = C.MultitypeFuncGraph("adadelta_opt")


@_adadelta_opt.register("Function", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt(opt, rho, epsilon, learning_rate, weight, accum, accum_update, gradient):
    """Apply adadelta optimizer to the weight parameter."""
    success = True
    success = F.depend(success, opt(weight, accum, accum_update, learning_rate, rho, epsilon, gradient))
    return success


[文档]class Adadelta(Optimizer): r""" Implements Adadelta algorithm. .. math:: \begin{aligned} &\rule{150mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}, \: \rho \text{ (decay)}, \: \lambda \text{ (weight decay)} \\ &\textbf{initialize} : v_0 \leftarrow 0 \: \text{ (square avg)}, \: u_0 \leftarrow 0 \: \text{ (accumulate variables)} \\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm}if \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm} v_t \leftarrow v_{t-1} \rho + g^2_t (1 - \rho) \\ &\hspace{5mm}\Delta x_t \leftarrow \frac{\sqrt{u_{t-1} + \epsilon }}{ \sqrt{v_t + \epsilon} }g_t \hspace{21mm} \\ &\hspace{5mm} u_t \leftarrow u_{t-1} \rho + \Delta x^2_t (1 - \rho) \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \gamma \Delta x_t \\ &\rule{110mm}{0.4pt} \\[-1.ex] &\bf{return} \: \theta_t \\[-1.ex] &\rule{110mm}{0.4pt} \\[-1.ex] \end{aligned} .. warning:: This is an experimental optimizer API that is subject to change. This module must be used with lr scheduler module in `LRScheduler Class <https://www.mindspore.cn/docs/en/r2.3.1/api_python/mindspore.experimental.html#lrscheduler-class>`_ . Args: params (Union[list(Parameter), list(dict)]): list of parameters to optimize or dicts defining parameter groups. lr (Union[int, float, Tensor], optional): learning rate. Default: ``1.0``. rho (float, optional): coefficient used for computing a running average of squared gradients. :math:`\rho` in the formula above. Default: ``0.9``. eps (float, optional): term added to the denominator to improve numerical stability. :math:`\epsilon` in the formula above. Default: ``1e-6``. weight_decay (float, optional): weight decay (L2 penalty). Default: ``0.``. Keyword Args: maximize (bool, optional): maximize the params based on the objective, instead of minimizing. Default: ``False``. Inputs: - **gradients** (tuple[Tensor]) - The gradients of `params`. Raises: ValueError: If the learning rate is not int, float or Tensor. ValueError: If the learning rate is less than 0. ValueError: If the `eps` is less than or equal to 0.0. ValueError: If the `rho` is not in the range of [0, 1]. ValueError: If the `weight_decay` is less than 0. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore >>> from mindspore import nn >>> from mindspore.experimental import optim >>> # Define the network structure of LeNet5. Refer to >>> # https://gitee.com/mindspore/docs/blob/r2.3.1/docs/mindspore/code/lenet.py >>> net = LeNet5() >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True) >>> optimizer = optim.Adadelta(net.trainable_params(), lr=0.1) >>> def forward_fn(data, label): ... logits = net(data) ... loss = loss_fn(logits, label) ... return loss, logits >>> grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True) >>> def train_step(data, label): ... (loss, _), grads = grad_fn(data, label) ... optimizer(grads) ... return loss """ def __init__(self, params, lr=1.0, rho=0.9, eps=1e-6, weight_decay=0.0, *, maximize=False): check_not_less_than_without_equal(lr, "lr", self.cls_name) check_not_less_than_without_equal(eps, "eps", self.cls_name) check_not_less_than(weight_decay, "weight_decay", self.cls_name) validator.check_float_range(rho, 0., 1., validator.INC_BOTH, "rho", self.cls_name) defaults = dict( lr=lr, rho=rho, eps=eps, weight_decay=weight_decay, maximize=maximize, ) super(Adadelta, self).__init__(params, defaults) self.accum = self.parameters.clone(prefix="accum", init=0) self.accum_update = self.parameters.clone(prefix="accum_update", init=0) self.opt = P.ApplyAdadelta() self.op_cast = P.Cast() @jit def implementation(self, lr, rho, eps, maximize, weight_decay, start_id, end_id, gradients): """Extract the common computing part for acceleration""" params = self.parameters[start_id: end_id] grads = tuple([grad if not maximize else F.neg(grad) for grad in gradients[start_id: end_id]]) grads = self._decay_weight(weight_decay, params, grads) accum = self.accum[start_id: end_id] accum_update = self.accum_update[start_id: end_id] self.hyper_map(F.partial(_adadelta_opt, self.opt, rho, eps, lr), params, accum, accum_update, grads) return True def construct(self, gradients): for group_id, group in enumerate(self.param_groups): lr = self.lrs[group_id] if isinstance(group.get("lr"), float): lr = self.op_cast(group.get("lr"), mstype.float32) maximize = group.get("maximize") rho = group["rho"] eps = group["eps"] start_id = self.group_start_id[group_id] end_id = self.group_start_id[group_id + 1] weight_decay = group["weight_decay"] self.implementation(lr, rho, eps, maximize, weight_decay, start_id, end_id, gradients) return True