Ranger优化器是在RAdam与Lookahead优化器基础上进行融合得到的。 RAdam:带有整流器的Adam,能够利用方差的潜在散度动态地打开或关闭自适应学习率。 Lookahead:通过迭代更新两组权重的方法,提前观察另一个优化器生成的序列,以选择搜索方向。 Ranger优化器将RAdam与Lookahead优化器组合到一起,并兼顾了二者的优点。 PyTorch中...
update def train(epoch): for i, data in enumerate(train_loader, 0): inputs, labels = data y_pred = model(inputs) loss = criterion(y_pred, labels) optimizer.zero_grad() loss.backward() optimizer.step() return loss
optimizer = RAdam(filter(lambda p: p.requires_grad, model.parameters()), lr=0.01, betas=(0.90, 0.999), eps=1e-08, weight_decay=1e-4) 1. 2. 3. ranger code import torch from .Ranger import Ranger optimizer = Ranger(filter(lambda p: p.requires_grad, model.parameters()), lr=0.01,...
optimizer = torch.optim.AdamW((param for param in net.parameters() if param.requires_grad), lr=lr, #param.requires_grad:是否需要梯度 weight_decay=0) elif optim == 'ranger': #ranger优化器 optimizer = Ranger((param for param in net.parameters() if param.requires_grad), lr=lr,#param.r...
optimizer = torch.optim.Adam(model.parameters(),lr = lreaning_rate) 2.3.2 查看优化器的参数结构 Pytorch中的每个优化器类中均有param_groups属性,该属性包括每个待优化权重的配置参数,是一个列表对象 list(optimizer.param_group[0].keys())#返回: ['params','lr','eps','weight_deacy','amsgrad']#...
# 1.7 使用Ranger优化器训练模型import torch.optim as optim # 引入优化器库from functools import partial # 引入偏函数库from ranger import * # 载入Ranger优化器# 为Ranger优化器设置参数opt_func = partial(Ranger, betas=(.9, 0.99), eps=1e-6) # betas=(Momentum,alpha)optimizer = opt_func(model...
RAdam( m.parameters(), lr= 1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, ) optimizer.step() Paper: On the Variance of the Adaptive Learning Rate and Beyond (2019) [https://arxiv.org/abs/1908.03265] Reference Code: https://github.com/LiyuanLucasLiu/RAdam Ranger import ...
Ranger import torch_optimizer as optim # model = ... optimizer = optim.Ranger( m.parameters(), lr=1e-3, alpha=0.5, k=6, N_sma_threshhold=5, betas=(.95, 0.999), eps=1e-5, weight_decay=0 ) optimizer.step() Paper:Calibrating the Adaptive Learning Rate to Improve Convergence of AD...
N-BEATS model DeepAR model: Most popular baseline model for time-series forecasting. Ranger optimizer for faster model training. Hyperparameter tuning using optuna Installation First, install Pytorch as the forecasting library is inherited from Pytorch, install PyTorch with this command: ...
Reference Code:https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer RangerVA importtorch_optimizerasoptim# model = ...optimizer=optim.RangerVA(m.parameters(),lr=1e-3,alpha=0.5,k=6,n_sma_threshhold=5,betas=(.95,0.999),eps=1e-5,weight_decay=0,amsgrad=True,transformer='softplus',...