If you have ever worked on a Computer Vision project, you might know that using a learning rate scheduler might significantly increase your model training performance. On this page, we will:
Let’s jump in.
The Exponential Learning Rate scheduling technique divides the learning rate every epoch (or every evaluation period in the case of iteration trainer) by the same factor called gamma. Thus, the learning rate will decrease abruptly during the first several epochs and slow down later, with most epochs running with lower values. The learning rate aspires to zero but never reaches it.
At the last epoch, the algorithm sets the learning rate as the initial Base Learning Rate.
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import torch
model = [Parameter(torch.randn(2, 2, requires_grad=True))]
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=0.01, amsgrad=False)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.1, last_epoch=-1, verbose=False)
for epoch in range(20):
for input, target in dataset:
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
scheduler.step()
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