PyTorch implementation of some learning rate schedulers for deep learning researcher.
- Visualize
- Example code
import torch
from lr_scheduler.warmup_reduce_lr_on_plateau_scheduler import WarmupReduceLROnPlateauScheduler
if __name__ == '__main__':
max_epochs, steps_in_epoch = 10, 10000
model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
optimizer = torch.optim.Adam(model, 1e-10)
scheduler = WarmupReduceLROnPlateauScheduler(
optimizer,
init_lr=1e-10,
peak_lr=1e-4,
warmup_steps=30000,
patience=1,
factor=0.3,
)
for epoch in range(max_epochs):
for timestep in range(steps_in_epoch):
...
...
if timestep < warmup_steps:
scheduler.step()
val_loss = validate()
scheduler.step(val_loss)- Visualize
- Example code
import torch
from lr_scheduler.transformer_lr_scheduler import TransformerLRScheduler
if __name__ == '__main__':
max_epochs, steps_in_epoch = 10, 10000
model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
optimizer = torch.optim.Adam(model, 1e-10)
scheduler = TransformerLRScheduler(
optimizer=optimizer,
init_lr=1e-10,
peak_lr=0.1,
final_lr=1e-4,
final_lr_scale=0.05,
warmup_steps=3000,
decay_steps=17000,
)
for epoch in range(max_epochs):
for timestep in range(steps_in_epoch):
...
...
scheduler.step()- Visualize
- Example code
import torch
from lr_scheduler.tri_stage_lr_scheduler import TriStageLRScheduler
if __name__ == '__main__':
max_epochs, steps_in_epoch = 10, 10000
model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
optimizer = torch.optim.Adam(model, 1e-10)
scheduler = TriStageLRScheduler(
optimizer,
init_lr=1e-10,
peak_lr=1e-4,
final_lr=1e-7,
init_lr_scale=0.01,
final_lr_scale=0.05,
warmup_steps=30000,
hold_steps=70000,
decay_steps=100000,
total_steps=200000,
)
for epoch in range(max_epochs):
for timestep in range(steps_in_epoch):
...
...
scheduler.step()- Visualize
- Example code
import torch
from lr_scheduler.reduce_lr_on_plateau_lr_scheduler import ReduceLROnPlateauScheduler
if __name__ == '__main__':
max_epochs, steps_in_epoch = 10, 10000
model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
optimizer = torch.optim.Adam(model, 1e-4)
scheduler = ReduceLROnPlateauScheduler(optimizer, patience=1, factor=0.3)
for epoch in range(max_epochs):
for timestep in range(steps_in_epoch):
...
...
val_loss = validate()
scheduler.step(val_loss)- Visualize
- Example code
import torch
from lr_scheduler.warmup_lr_scheduler import WarmupLRScheduler
if __name__ == '__main__':
max_epochs, steps_in_epoch = 10, 10000
model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
optimizer = torch.optim.Adam(model, 1e-10)
scheduler = WarmupLRScheduler(
optimizer,
init_lr=1e-10,
peak_lr=1e-4,
warmup_steps=4000,
)
for epoch in range(max_epochs):
for timestep in range(steps_in_epoch):
...
...
scheduler.step()git clone git@github.com:sooftware/pytorch-lr-scheduler.git
cd pytorch-lr-scheduler
pip install .If you have any questions, bug reports, and feature requests, please open an issue on Github.
I appreciate any kind of feedback or contribution. Feel free to proceed with small issues like bug fixes, documentation improvement. For major contributions and new features, please discuss with the collaborators in corresponding issues.
I follow PEP-8 for code style. Especially the style of docstrings is important to generate documentation.
This project is licensed under the MIT LICENSE - see the LICENSE.md file for details
