how MultiStepLR works in PyTorch

I’m new to PyTorch and am working on a toy example to understand how weight decay works in learning rate passed into the optimizer. When I use MultiStepLR , I was expecting to decrease the learning rate in given epoch numbers, however, it does not work as I intended. What am I doing wrong?

import random
import torch
import pandas as pd
import numpy as np
from torch import nn
from torch.utils.data import Dataset,DataLoader,TensorDataset
from torchvision import datasets, transforms

model = nn.Sequential(nn.Linear(n_input, n_hidden),
                      nn.ReLU(),
                      nn.Linear(n_hidden, n_out),
                      nn.ReLU())
loss_function = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.1)

scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[2,4], gamma=0.1)
for e in range(5):
    scheduler.step()
    print(e, ' : lr', scheduler.get_lr()[0],"\n")

0  : lr 0.1 

1  : lr 0.0010000000000000002 

2  : lr 0.010000000000000002 

3  : lr 0.00010000000000000003 

4  : lr 0.0010000000000000002 

The expected behavior in learning rate is [0.1, 0.1, 0.01, 0.01, 0.001]

>Solution :

When running your code I get the following warning:

/home/user/anaconda3/envs/eai/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:138: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`.
In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.
Failure to do this will result in PyTorch skipping the first value of the learning rate schedule.
See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
  warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "

/home/user/anaconda3/envs/eai/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:429: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.
  warnings.warn("To get the last learning rate computed by the scheduler, "

Your code can be fixed by following the warning message and using get_last_lr:

import random
import torch
import pandas as pd
import numpy as np
from torch import nn
from torch.utils.data import Dataset,DataLoader,TensorDataset
from torchvision import datasets, transforms

model = nn.Sequential(nn.Linear(4, 4),
                      nn.ReLU(),
                      nn.Linear(4, 4),
                      nn.ReLU())
loss_function = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.1)

scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[2,4], gamma=0.1)
for e in range(5):
    scheduler.step()
    print(e, ' : lr', scheduler.get_last_lr(),"\n")

With output:

0  : lr [0.1] 

1  : lr [0.010000000000000002] 

2  : lr [0.010000000000000002] 

3  : lr [0.0010000000000000002] 

4  : lr [0.0010000000000000002] 

If you want the learning rate to decrease each epoch instead you should remove the milestones parameter.