def __init__(self, G, max_iter=20): self._G = G self._n = len(G.nodes(False)) # 节点数目 self._max_iter = max_iter这段代码什么意思

这段代码是一个 Python 类的初始化方法（`__init__`）。

参数 `G` 是一个 NetworkX 图对象，表示图的结构。

参数 `max_iter` 是一个整数，表示 PageRank 算法的最大迭代次数，默认为 20。

`self._G` 将传入的图对象存储在类的成员变量 `_G` 中。

`self._n` 是图中节点的数量，存储在类的成员变量 `_n` 中。

`self._max_iter` 存储了最大迭代次数。

相关问题

给以下代码写注释，要求每行写一句：class CosineAnnealingWarmbootingLR: # cawb learning rate scheduler: given the warm booting steps, calculate the learning rate automatically def __init__(self, optimizer, epochs=0, eta_min=0.05, steps=[], step_scale=0.8, lf=None, batchs=0, warmup_epoch=0, epoch_scale=1.0): self.warmup_iters = batchs * warmup_epoch self.optimizer = optimizer self.eta_min = eta_min self.iters = -1 self.iters_batch = -1 self.base_lr = [group['lr'] for group in optimizer.param_groups] self.step_scale = step_scale steps.sort() self.steps = [warmup_epoch] + [i for i in steps if (i < epochs and i > warmup_epoch)] + [epochs] self.gap = 0 self.last_epoch = 0 self.lf = lf self.epoch_scale = epoch_scale # Initialize epochs and base learning rates for group in optimizer.param_groups: group.setdefault('initial_lr', group['lr']) def step(self, external_iter = None): self.iters += 1 if external_iter is not None: self.iters = external_iter # cos warm boot policy iters = self.iters + self.last_epoch scale = 1.0 for i in range(len(self.steps)-1): if (iters <= self.steps[i+1]): self.gap = self.steps[i+1] - self.steps[i] iters = iters - self.steps[i] if i != len(self.steps)-2: self.gap += self.epoch_scale break scale *= self.step_scale if self.lf is None: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * ((((1 + math.cos(iters * math.pi / self.gap)) / 2) ** 1.0) * (1.0 - self.eta_min) + self.eta_min) else: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * self.lf(iters, self.gap) return self.optimizer.param_groups[0]['lr'] def step_batch(self): self.iters_batch += 1 if self.iters_batch < self.warmup_iters: rate = self.iters_batch / self.warmup_iters for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = lr * rate return self.optimizer.param_groups[0]['lr'] else: return None

这是一个类的定义，类名为CosineAnnealingWarmbootingLR。

def __init__(self, optimizer, T_max, T_mult=1, eta_min=0, last_epoch=-1):

这是类的初始化函数，接受optimizer、T_max、T_mult、eta_min和last_epoch五个参数。

self.optimizer = optimizer
self.T_max = T_max
self.T_mult = T_mult
self.eta_min = eta_min
self.last_epoch = last_epoch

将传入的参数分别赋值给类的属性。

self.T_cur = float(self.last_epoch)

初始化当前的T值为last_epoch。

def get_lr(self):

定义一个名为get_lr的函数。

if self.T_cur == self.T_max:

如果当前T值等于T_max，则将T_cur重置为0，并将T_max乘以T_mult。

self.T_cur = 0
self.T_max *= self.T_mult

return self.eta_min + (1 + math.cos(math.pi * self.T_cur / self.T_max)) / 2 * (self.optimizer.param_groups[0]['lr'] - self.eta_min)

如果当前T值不等于T_max，则根据公式计算当前的学习率，并返回。

在paddle框架中实现下面的所有代码:class CosineAnnealingWarmbootingLR: # cawb learning rate scheduler: given the warm booting steps, calculate the learning rate automatically def __init__(self, optimizer, epochs=0, eta_min=0.05, steps=[], step_scale=0.8, lf=None, batchs=0, warmup_epoch=0, epoch_scale=1.0): self.warmup_iters = batchs * warmup_epoch self.optimizer = optimizer self.eta_min = eta_min self.iters = -1 self.iters_batch = -1 self.base_lr = [group['lr'] for group in optimizer.param_groups] self.step_scale = step_scale steps.sort() self.steps = [warmup_epoch] + [i for i in steps if (i < epochs and i > warmup_epoch)] + [epochs] self.gap = 0 self.last_epoch = 0 self.lf = lf self.epoch_scale = epoch_scale # Initialize epochs and base learning rates for group in optimizer.param_groups: group.setdefault('initial_lr', group['lr']) def step(self, external_iter = None): self.iters += 1 if external_iter is not None: self.iters = external_iter # cos warm boot policy iters = self.iters + self.last_epoch scale = 1.0 for i in range(len(self.steps)-1): if (iters <= self.steps[i+1]): self.gap = self.steps[i+1] - self.steps[i] iters = iters - self.steps[i] if i != len(self.steps)-2: self.gap += self.epoch_scale break scale *= self.step_scale if self.lf is None: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * ((((1 + math.cos(iters * math.pi / self.gap)) / 2) ** 1.0) * (1.0 - self.eta_min) + self.eta_min) else: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * self.lf(iters, self.gap) return self.optimizer.param_groups[0]['lr'] def step_batch(self): self.iters_batch += 1 if self.iters_batch < self.warmup_iters: rate = self.iters_batch / self.warmup_iters for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = lr * rate return self.optimizer.param_groups[0]['lr'] else: return None

在Paddle框架中实现下面的所有代码:class CosineAnnealingWarmbootingLR:

在Paddle框架中，可以通过继承paddle.optimizer.lr.LRScheduler类来实现CosineAnnealingWarmbootingLR类。具体实现代码如下：

import math
import paddle.optimizer.lr as lr

class CosineAnnealingWarmbootingLR(lr.LRScheduler):
    def __init__(self, T_max, T_warmup, eta_min=0, last_epoch=-1):
        self.T_max = T_max
        self.T_warmup = T_warmup
        self.eta_min = eta_min
        super(CosineAnnealingWarmbootingLR, self).__init__(last_epoch)

    def get_lr(self):
        if self.last_epoch < self.T_warmup:
            return self.eta_min + (self.base_lr - self.eta_min) * self.last_epoch / self.T_warmup
        else:
            return self.eta_min + (self.base_lr - self.eta_min) * (1 + math.cos(math.pi * (self.last_epoch - self.T_warmup) / (self.T_max - self.T_warmup))) / 2

其中，T_max表示学习率下降的总步数，T_warmup表示学习率从0逐渐增加到初始值的步数，eta_min表示学习率的最小值，last_epoch表示上一次更新学习率的步数。

在get_lr()方法中，首先判断当前步数是否小于T_warmup，如果是，则学习率从0逐渐增加到初始值；否则，学习率按照余弦退火的方式进行下降。具体来说，学习率的下降曲线为：

$$\eta_t = \eta_{min} + \frac{1}{2}(\eta_{max}-\eta_{min})(1+\cos(\frac{\pi(t-T_{warmup})}{T_{max}-T_{warmup}}))$$

其中，$\eta_t$表示第t步的学习率，$\eta_{min}$表示学习率的最小值，$\eta_{max}$表示学习率的初始值，$T_{max}$表示学习率下降的总步数，$T_{warmup}$表示学习率从0逐渐增加到初始值的步数。