ExponentialLR

This scheduling technique reduces the learning rate every epoch (or every eval period in case of iteration trainer) by a factor "gamma".

At the last epoch, it sets the learning rate as the initial Base Learning Rate.

Gamma

The factor by which the learning rate is decayed every epoch.

Code Implementation

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()