zlheos/pytorch-cosine-annealing-with-warmup

Cosine Annealing with Warm up for PyTorch

Example

>> model = ...
>> optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-5) # lr is min lr
>> scheduler = CosineAnnealingWarmUpRestarts(optimizer, T_0=250, T_mult=2, eta_max=0.1, T_up=50)
>> for epoch in range(n_epoch):
>>     train()
>>     valid()
>>     scheduler.step()

• case1 : CosineAnnealingWarmUpRestarts(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)
• case2 : CosineAnnealingWarmUpRestarts(optimizer, T_0=50, T_mult=2, eta_max=0.1, T_up=10, gamma=0.5)
• case3 : CosineAnnealingWarmUpRestarts(optimizer, T_0=100, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)
• case4 : CosineAnnealingWarmUpRestarts(optimizer, T_0=250, T_mult=1, eta_max=0.1, T_up=50)
• case5 : CosineAnnealingWarmUpRestarts(optimizer, T_0=250, T_mult=2, eta_max=0.1, T_up=50)

引数

• T_0 : Cosine Annearingのステップ数
• T_multi : ステップの倍率
• eta_max : lrの最大値
• T_up : warmupのイテレーション数
• gamma : サイクル毎の最大学習率減少率