Adam vs SGD in PyTorch: Key Differences and Usage Guide

In PyTorch, Adam is an adaptive optimizer that adjusts learning rates for each parameter, often leading to faster convergence, while SGD (Stochastic Gradient Descent) uses a fixed learning rate and is simpler but may require more tuning. Adam is preferred for complex models and noisy data, whereas SGD can perform better for large-scale or well-tuned tasks.

Quick Comparison

Here is a quick side-by-side comparison of Adam and SGD optimizers in PyTorch based on key factors.

Factor	Adam	SGD
Type	Adaptive learning rate optimizer	Fixed learning rate optimizer
Learning Rate	Automatically adjusted per parameter	Manually set and constant (can use momentum)
Convergence Speed	Usually faster on complex problems	Slower but can be stable with tuning
Memory Usage	Higher due to moment estimates	Lower memory footprint
Best Use Case	Noisy or sparse gradients, complex models	Large datasets, simple models, or fine-tuning
Hyperparameter Tuning	Less sensitive, works well out-of-the-box	Requires careful learning rate and momentum tuning

Key Differences

Adam optimizer combines ideas from RMSProp and momentum by keeping track of both the average of past gradients and squared gradients. This allows it to adapt the learning rate for each parameter individually, which helps in faster and more stable convergence especially when gradients are noisy or sparse.

On the other hand, SGD updates parameters using a fixed learning rate and optionally momentum to smooth updates. It is simpler and uses less memory but often requires more careful tuning of the learning rate and momentum to achieve good results.

In PyTorch, Adam is often the default choice for many deep learning tasks due to its robustness, while SGD is preferred in scenarios where training stability and generalization are critical, such as in large-scale image classification tasks.

Adam Code Example

This example shows how to use the Adam optimizer in PyTorch to train a simple linear model.

python

import torch
import torch.nn as nn
import torch.optim as optim

# Simple linear model
data = torch.randn(100, 1)
target = 3 * data + 2 + 0.1 * torch.randn(100, 1)

model = nn.Linear(1, 1)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.1)

for epoch in range(100):
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()

print(f"Final loss with Adam: {loss.item():.4f}")

Output

Final loss with Adam: 0.0102

SGD Equivalent

This example shows the equivalent training loop using SGD optimizer with momentum in PyTorch.

python

import torch
import torch.nn as nn
import torch.optim as optim

# Simple linear model
data = torch.randn(100, 1)
target = 3 * data + 2 + 0.1 * torch.randn(100, 1)

model = nn.Linear(1, 1)
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)

for epoch in range(100):
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()

print(f"Final loss with SGD: {loss.item():.4f}")

Output

Final loss with SGD: 0.0115

When to Use Which

Choose Adam when you want faster convergence with less tuning, especially for complex models or noisy data. It is great for beginners and most deep learning tasks.

Choose SGD when you have a large dataset, want better generalization, or need more control over training dynamics. It is often used in research and production for fine-tuning and large-scale models.

Key Takeaways

Adam adapts learning rates per parameter and converges faster with less tuning.

SGD uses a fixed learning rate and can generalize better with proper tuning.

Adam uses more memory due to moment estimates; SGD is simpler and lighter.

Use Adam for noisy or complex tasks; use SGD for large-scale or fine-tuning.

Both optimizers are easy to implement in PyTorch with similar training loops.