滑动平均(exponential moving average),或者叫做指数加权平均(exponentially weighted moving average),可以用来估计变量的局部均值,使得变量的更新与一段时间内的历史取值有关。 变量v在t时刻记为 vt,θt 为变量 v 在t 时刻的取值,即在不使用滑动平均模型时 vt=θt,在使用滑动平均模型后,vt的更新公式如下: vt=β...
EMA(Exponential Moving Average)全称为指数移动平均,主要作用是平滑模型权重,平滑可以带来更好的泛化能力。 以下是来自wiki关于移动平均(Moving Average)的图片,对带有噪声的正弦函数使用Moving Average进行平滑后的效果。 蓝色为带噪声的正弦曲线,红色为平滑曲线。 image-20220817215348448 首先,来看下它的公式: wshadowt...
滑动平均(exponential moving average),或者叫做指数加权平均(exponentially weighted moving average),可以用来估计变量的局部均值,使得变量的更新与一段时间内的历史取值有关。 变量vv在tt时刻记为 vtvt,θtθt 为变量 vv 在tt 时刻的取值,即在不使用滑动平均模型时 vt=θtvt=θt,在使用滑动平均模型后,vtvt 的更...
python class EMA: def __init__(self, mu): self.mu = mu self.shadow = {} def register(self, name, val): self.shadow[name] = val.clone() def __call__(self, name, x): assert name in self.shadow new_average = (1.0 - self.mu) * x + self.mu * self.shadow[name] self.sh...
Exponential Moving Average from https://github.com/rwightman/pytorch-image-modelsKeep a moving average of everything in the model state_dict (parameters and buffers).This is intended to allow functionality likehttps://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverageA smoothed ...
This tutorial was a good starting point on how you can calculate the moving averages of your data and make sense of it. Try writing the cumulative and exponential moving average python code without using the pandas library. That will give you much more in-depth knowledge about how they are...
/usr/bin/python # -*- coding: utf-8 -*- """ when apply average, the new value is a value between initial value and updated value """ importtensorflowastf # v1 is the variable to apply exponential moving average v1=tf.Variable(0.0,tf.float32)...
1.Moving Average in Stable Diffusion (SMA&EMA) 1.Moving average 2.移动平均值 3.How We Trained Stable Diffusion for Less than $50k (Part 3) Moving Average 在统计学中,移动平均是通过创建整个数据集中不同选择的一系列平均值来分析数据点的计算。
/usr/bin/env python2#-*- coding: utf-8 -*-3importtensorflow as tf45"""6滑动平均方法的运用,通常用于训练模型参数时,对参数的更新操作,利用7shadow_variable = decay * shadow_variable + (1 - decay) * variable8构造类tf.train.ExponentialMovingAverage()的对象ema9调用ema.apply() 返回用于对参数...
问尝试使用未初始化的值变量/ExponentialMovingAverageEN昨天写的今日问题，有小伙伴给我反馈，觉得挺有用...