• March 20, 2025

Optimizer vs Scheduler

Both optimizers and schedulers play a role in training deep learning models, but they have different purposes.


1️⃣ Optimizer

🔹 Purpose:

  • Updates model weights to minimize the loss function.
  • Uses gradients from backpropagation to adjust parameters.
  • Controls how quickly the model learns (learning rate affects convergence).

🔹 Common Optimizers:

  • SGD (Stochastic Gradient Descent) → Basic optimizer.
  • Adam → Adaptive learning rate, widely used.
  • RMSprop → Good for recurrent neural networks (RNNs).
  • Adagrad → Adjusts learning rate per parameter.

🔹 Example in PyTorch:

import torch.optim as optim

model_params = [torch.tensor(1.0, requires_grad=True)] # Example parameter
optimizer = optim.Adam(model_params, lr=0.01)

# Training step
optimizer.zero_grad()
loss = model_params[0]**2 # Example loss
loss.backward()
optimizer.step()

2️⃣ Scheduler (Learning Rate Scheduler)

🔹 Purpose:

  • Adjusts the learning rate during training.
  • Helps prevent overshooting or speed up convergence.
  • Works alongside an optimizer (does not update weights directly).

🔹 Common Schedulers:

  • StepLR → Reduces learning rate every few epochs.
  • ExponentialLR → Decays learning rate exponentially.
  • ReduceLROnPlateau → Reduces learning rate if validation loss stops improving.

🔹 Example in PyTorch:

from torch.optim.lr_scheduler import StepLR

optimizer = optim.Adam(model_params, lr=0.01)
scheduler = StepLR(optimizer, step_size=10, gamma=0.1) # Reduce LR every 10 epochs

for epoch in range(20):
optimizer.step()
scheduler.step() # Adjust learning rate
print(f"Epoch {epoch}, Learning Rate: {scheduler.get_last_lr()}")

🔑 Key Differences

FeatureOptimizerScheduler
PurposeUpdates model weightsAdjusts learning rate dynamically
ControlsGradient updatesLearning rate over time
Directly AffectsModel parametersOptimizer settings
Common AlgorithmsAdam, SGD, RMSpropStepLR, ExponentialLR, ReduceLROnPlateau
UsageAlways needed for trainingOptional but useful for better convergence

🛠️ When to Use Each?

  • Always use an optimizer (like Adam or SGD) to train the model.
  • Use a scheduler when you need to fine-tune the learning rate over epochs for better performance.

🚀 Final Thought

An optimizer updates weights, while a scheduler adjusts the learning rate during training to improve convergence.

Let me know if you need further clarification! 🚀

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