Prev Next

Python / PyTorch Fundamentals Interview Questions

How do you implement and use a custom loss function in PyTorch?

When built-in loss functions do not fit your task, you can write a custom loss as either a plain function or an nn.Module subclass. As long as the loss is computed from PyTorch tensor operations with requires_grad=True parameters, autograd handles differentiation automatically.

import torch
import torch.nn as nn
import torch.nn.functional as F

# ── Option 1: Plain function (simple, no learnable parameters)
def smooth_l1_custom(pred: torch.Tensor, target: torch.Tensor, beta: float = 1.0) -> torch.Tensor:
    """Huber loss — L1 outside beta, L2 inside beta."""
    diff = torch.abs(pred - target)
    loss = torch.where(
        diff < beta,
        0.5 * diff ** 2 / beta,       # quadratic region
        diff - 0.5 * beta,            # linear region
    )
    return loss.mean()

# ── Option 2: nn.Module subclass (recommended when loss has hyper-parameters
# or learnable parameters you want saved in state_dict)
class FocalLoss(nn.Module):
    """Focal loss for class-imbalanced multi-class problems."""
    def __init__(self, gamma: float = 2.0, weight: torch.Tensor | None = None):
        super().__init__()
        self.gamma  = gamma
        self.weight = weight   # class weights (optional)

    def forward(self, logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
        # logits: (N, C)  targets: (N,) int64
        ce_loss = F.cross_entropy(logits, targets, weight=self.weight, reduction="none")
        pt      = torch.exp(-ce_loss)         # probability of correct class
        focal   = (1 - pt) ** self.gamma * ce_loss
        return focal.mean()

# Usage — identical to built-in loss functions
model     = nn.Linear(10, 5)
focal_fn  = FocalLoss(gamma=2.0)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

X      = torch.randn(16, 10)
target = torch.randint(0, 5, (16,))

optimizer.zero_grad()
logits = model(X)
loss   = focal_fn(logits, target)   # custom loss used exactly like nn.CrossEntropyLoss
loss.backward()                      # autograd differentiates through our custom ops
optimizer.step()

print(f"Focal loss: {loss.item():.4f}")

# ── Combining multiple losses
rec_loss  = F.mse_reconstruction_loss(output, target_img)  # reconstruction
kl_loss   = -0.5 * (1 + log_var - mu**2 - log_var.exp()).mean()  # KL divergence
total_loss = rec_loss + 0.001 * kl_loss   # weighted combination

Key insight: any PyTorch computation graph built from differentiable operations is automatically differentiable via autograd — you do not need to manually derive or implement gradients for custom losses. If you use standard PyTorch operations (torch.*, F.*), autograd takes care of the rest.

Do you need to manually implement the backward() gradient computation for a custom PyTorch loss function built from standard tensor operations?
What is the advantage of implementing a custom loss as an nn.Module subclass instead of a plain function?

More Related questions...

What is PyTorch and what are its key advantages over other deep learning frameworks? What is a PyTorch tensor and how does it differ from a NumPy array? What are the most important tensor operations in PyTorch? What are tensor data types (dtypes) in PyTorch and why do they matter? How does broadcasting work in PyTorch and what are the rules? What is autograd in PyTorch and how does it compute gradients? What is the computation graph in PyTorch and how does the dynamic graph differ from a static graph? How do torch.no_grad() and tensor.detach() differ, and when do you use each? What is nn.Module and how do you build a custom neural network in PyTorch? What are nn.Sequential and other container modules in PyTorch? What built-in layers does PyTorch's nn module provide and how do you use the most common ones? What are activation functions in PyTorch and how do you apply them? What are the most important loss functions in PyTorch and when do you use each? What optimizers does PyTorch provide and how do you configure them? What are learning rate schedulers in PyTorch and how do you use them? What are the most common built-in layers in torch.nn and what do they do? How do you initialise weights in a PyTorch model? What loss functions does PyTorch provide and when do you use each? What optimizers does PyTorch provide and how do you choose between them? What are learning rate schedulers in PyTorch and how do you use them? What activation functions are commonly used in PyTorch and how do you choose between them? What loss functions does PyTorch provide and how do you choose the right one? What optimizers does PyTorch provide and what is the difference between SGD, Adam, and AdamW? What is the standard PyTorch training loop and what does each step do? What are Dataset and DataLoader in PyTorch and how do they work together? How do you move tensors and models between CPU and GPU in PyTorch? What is the difference between model.parameters() and model.state_dict() in PyTorch? How do you save and load PyTorch models correctly, including full training checkpoints? What is overfitting and what regularization techniques does PyTorch support to address it? What is the vanishing/exploding gradient problem and how do you detect and fix it in PyTorch? What is weight initialization in PyTorch and why does it matter? What is the difference between nn.Parameter and a regular tensor attribute in nn.Module? How do you implement and use learning rate schedulers in PyTorch? How do you debug a PyTorch training loop where the loss is not decreasing or is NaN? What is the difference between torch.tensor() and torch.Tensor() (capital T) for creating tensors? How does gradient accumulation work in PyTorch and when would you use it? What is mixed precision training in PyTorch and how do you implement it with torch.cuda.amp? What is torch.compile() and how does it speed up PyTorch model execution? What is the difference between batch size, epoch, and iteration in PyTorch training? How do you compute and track evaluation metrics like accuracy during PyTorch training? What is the purpose of torch.manual_seed() and how do you ensure reproducibility in PyTorch? How does PyTorch handle multi-dimensional indexing and slicing of tensors? What is the difference between.view(),.reshape(), and.contiguous() in PyTorch, and why does it matter? How do you freeze layers and perform transfer learning / fine-tuning in PyTorch? What is the purpose of torch.utils.data.random_split() and how do you create train/validation/test splits in PyTorch? What is Batch Normalization in PyTorch and how does it differ from Layer Normalization? How do you implement and use a custom loss function in PyTorch? What is torch.compile() vs TorchScript and how do you export a PyTorch model for production deployment?
Show more question and Answers...

Uvicorn Fundamentals Interview Questions

Comments & Discussions

Interviews Questions

About Javapedia.net

Javapedia.net is for Java and J2EE developers, technologist and college students who prepare of interview. Also this site includes many practical examples.

This site is developed using J2EE technologies by Steve Antony, a senior Developer/lead at one of the logistics based company.

Follow Javapedia updates

contact: javatutorials2016[at]gmail[dot]com

Kindly consider donating for maintaining this website. Thanks.

Copyright © 2026, javapedia.net, all rights reserved. · .

DMCA.com Protection Status