torch.optim.lr_scheduler.CosineAnnealingLR()是PyTorch中的一个学习率调整器。它根据余弦函数的形状动态调整学习率,可以帮助模型更好地收敛。具体而言,该调整器将学习率调整为: ηₜ=η_min+(η_max-η_min)*0.5*(1+cos(T_cur/T_max*π)) 1. 其中,ηₜ 是在第 t 个 epoch 的学习率,η_min 和...
type='OptimWrapper') param_scheduler = [ dict( begin=0, by_epoch=False, end=1000, start_factor=1e-05, type='LinearLR'), dict( T_max=40, begin=40, by_epoch=True, convert_to_iter_based=True, end=80, eta_min=0.00025, type='CosineAnnealingLR'), ] randomness = dict(seed=21) re...
def __init__(self, env_type, max_epochs, batch_size, device="cpu", val_every=1, num_gpus=1, logdir="./logs/", master_ip='localhost', master_port=17750, training_script="train.py",fold=0): super().__init__(env_type, max_epochs, batch_size, device, val_every, num_gpus,...
optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=30000, eta_min=1e-7) self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=1e-4) # self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=30000, eta_min=1e-6) self.scheduler = torch.optim...