Improving Results with PyTorch LR Scheduler by Adjusting Learning Rate

In deep learning, a key aspect of training a neural network is tuning the learning rate. The learning rate determines how much the model’s weights are updated during training and can heavily impact the model’s performance. PyTorch offers a convenient way to adjust the learning rate during training using a learning rate scheduler. In this tutorial, we will discuss how to use the PyTorch learning rate scheduler to improve the training process.

1. Import necessary libraries
   First, make sure you have PyTorch installed. You can install it using pip installation:

   pip install torch

   Then, import the necessary libraries:

   import torch
   import torch.optim as optim
   from torch.optim.lr_scheduler import StepLR

2. Define your neural network model and optimizer
   Create your neural network model and define the optimizer that will update the weights of the model during training. Here is a simple example of defining a neural network model and its optimizer:

   
   class Model(nn.Module):
   def __init__(self):
       super(Model, self).__init__()
       self.fc1 = nn.Linear(10, 5)
       self.fc2 = nn.Linear(5, 1)
   
   def forward(self, x):
       x = F.relu(self.fc1(x))
       x = self.fc2(x)
       return x

model = Model()
optimizer = optim.SGD(model.parameters(), lr=0.1)

3. Define the learning rate scheduler
Next, define the learning rate scheduler. PyTorch offers various learning rate schedulers such as StepLR, MultiStepLR, ExponentialLR, etc. In this tutorial, we will focus on the StepLR scheduler, which decays the learning rate at a specific interval. Here is an example of defining the StepLR scheduler:
```python
scheduler = StepLR(optimizer, step_size=10, gamma=0.5)

In this example, the learning rate will be decayed by a factor of 0.5 every 10 epochs.

1. Adjust the learning rate during training
   Now, you can incorporate the learning rate scheduler into your training loop. Here is an example of how to adjust the learning rate during training:

   epochs = 50
   for epoch in range(epochs):
   # Train your model
   for batch_idx, (data, target) in enumerate(train_loader):
       optimizer.zero_grad()
       output = model(data)
       loss = criterion(output, target)
       loss.backward()
       optimizer.step()
   
   # Adjust the learning rate
   scheduler.step()
   
   # Print the current learning rate
   print(f"Epoch {epoch}: Learning rate: {optimizer.param_groups[0]['lr']}")

2. Visualize the learning rate
   You can also visualize how the learning rate changes during training by plotting it. Here is an example of plotting the learning rate over epochs:

   
   import matplotlib.pyplot as plt

learning_rates = [optimizer.paramgroups[0][‘lr’] for in range(epochs)]
plt.plot(range(epochs), learning_rates)
plt.xlabel(‘Epoch’)
plt.ylabel(‘Learning Rate’)
plt.title(‘Learning Rate Schedule’)
plt.show()

By using the PyTorch learning rate scheduler, you can dynamically adjust the learning rate during training to improve the performance of your neural network. Experiment with different learning rate schedulers and parameters to find the best configuration for your model. Happy coding!