Optimizing Deep Learning: Exploring Alternatives to CosineAnnealingWarmRestarts.print_lr()

Purpose

• The print_lr() function is used for printing the current learning rate of each parameter group being managed by the CosineAnnealingWarmRestarts scheduler.

Functionality

• If group is specified, it only prints the learning rate for that particular parameter group.

• If is_verbose is True, it prints a message in the format:

  Adjusting learning rate of group {group} to {lr:.4e}.
  

  or:

  Epoch {epoch:5d}: adjusting learning rate of group {group} to {lr:.4e}.
  

  (The :.4e formatting specifies scientific notation with 4 decimal places.)

• It takes three optional arguments:

  • is_verbose (boolean): Controls whether to print the learning rate information. Defaults to False.
  • group (int): The index of the parameter group for which to print the learning rate. Defaults to None, meaning it prints for all groups.
  • lr (float): The learning rate value (usually internal for the scheduler). Not typically used for direct calls.
  • epoch (int, optional): The current epoch number (deprecated).

Context in PyTorch Optimization

• Printing the learning rate can be helpful for monitoring the training process and understanding how the learning rate is evolving.
• It gradually reduces the learning rate during training and then restarts the cycle with a higher minimum learning rate (warm restarts).
• The CosineAnnealingWarmRestarts scheduler implements a cyclical learning rate schedule that follows a cosine annealing pattern.

Usage

import torch
from torch.optim import SGD
from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts

# ... (create your model and optimizer)

scheduler = CosineAnnealingWarmRestarts(optimizer, T_max=10, eta_min=0.001)

# ... (training loop)

# Print learning rate information during training (optional)
if epoch % 10 == 0:  # Print every 10 epochs
    scheduler.print_lr(is_verbose=True)

# Update learning rate after each epoch
scheduler.step()

Key Points

• You can use it to track the learning rate behavior during training.
• It's not essential for the scheduler's functionality.
• print_lr() is primarily for informational purposes.

• The lr argument is usually for internal use by the scheduler and not intended for direct modification.
• The epoch argument is deprecated in newer PyTorch versions. It's recommended to use scheduler.step() to update the learning rate.

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Example 1: Printing Learning Rate Every Epoch

This code snippet prints the learning rate for all parameter groups after each training epoch:

import torch
from torch.optim import SGD
from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts

# ... (create your model and optimizer)

scheduler = CosineAnnealingWarmRestarts(optimizer, T_max=10, eta_min=0.001)

for epoch in range(1, num_epochs + 1):
    # ... (training loop)

    # Print learning rates after each epoch
    scheduler.print_lr(is_verbose=True)

    # Update learning rate after each epoch
    scheduler.step()

Example 2: Printing Learning Rate for Specific Group

This example demonstrates printing the learning rate only for a specific parameter group (index 1):

import torch
from torch.optim import SGD
from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts

# ... (create your model and optimizer, assuming multiple parameter groups)

scheduler = CosineAnnealingWarmRestarts(optimizer, T_max=10, eta_min=0.001)

for epoch in range(1, num_epochs + 1):
    # ... (training loop)

    # Print learning rate for group 1 only
    scheduler.print_lr(is_verbose=True, group=1)

    # Update learning rate after each epoch
    scheduler.step()

Example 3: Conditional Printing Based on Validation Loss

This code snippet prints the learning rate only if the validation loss improves:

import torch
from torch.optim import SGD
from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts

# ... (create your model, optimizer, and validation logic)

scheduler = CosineAnnealingWarmRestarts(optimizer, T_max=10, eta_min=0.001)
best_val_loss = float('inf')

for epoch in range(1, num_epochs + 1):
    # ... (training loop)

    # Check if validation loss improved
    if val_loss < best_val_loss:
        best_val_loss = val_loss
        scheduler.print_lr(is_verbose=True)  # Print only on improvement

    # Update learning rate after each epoch
    scheduler.step()

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Custom Logging

• During the training loop, after updating the learning rate with scheduler.step(), access the learning rate for each parameter group using the get_lr() method of the scheduler:
• Implement your own logging mechanism to track the learning rate.

learning_rates = scheduler.get_lr()  # List of learning rates for each group

• Use a logging library like logging or tqdm to record the epoch number, learning rates, and any other relevant training information. This gives you more control over the format and location of the logged data.

TensorBoard

• During training, add the learning rate values to a SummaryWriter object:
• If you're using TensorBoard for visualization, you can create a scalar plot to track the learning rate.

from torch.utils.tensorboard import SummaryWriter

# ... (create model, optimizer, scheduler, etc.)
writer = SummaryWriter('runs/experiment_name')  # Replace with your experiment name

for epoch in range(1, num_epochs + 1):
    # ... (training loop)

    # Add learning rate to TensorBoard
    learning_rates = scheduler.get_lr()
    for i, lr in enumerate(learning_rates):
        writer.add_scalar(f'Learning_Rate/Group_{i}', lr, epoch)

    # Update learning rate after each epoch
    scheduler.step()

This allows you to visualize the learning rate changes alongside other training metrics in TensorBoard.

Progress Bars (e.g., tqdm)

• Access the learning rates using scheduler.get_lr() within the training loop and update the progress bar message accordingly.
• If you're using a progress bar library like tqdm, you can customize the message displayed during training to include the learning rate.

• Progress bars provide a compact way to view learning rates during training.
• TensorBoard offers visualization alongside other metrics, ideal for interactive exploration.
• Custom logging is flexible and gives you full control, but requires manual implementation.