Python / Python Deep Learning and Neural Networks Interview Questions

What loss functions does PyTorch provide for classification and regression, and which to use when?

The choice of loss function should match the output type and the probabilistic assumption about the data-generating process — it is the mathematical link between model predictions and the training signal.

Common PyTorch Loss Functions
Task	Loss	PyTorch class	Notes
Binary classification	Binary cross-entropy	nn.BCEWithLogitsLoss	Takes logits (pre-sigmoid); numerically stable
Multi-class classification	Cross-entropy	nn.CrossEntropyLoss	Takes logits; combines log-softmax + NLLLoss
Regression	MSE	nn.MSELoss	Sensitive to outliers
Regression (robust)	MAE / Huber	nn.L1Loss / nn.HuberLoss	Huber blends L1+L2; robust to outliers
Multi-label classification	BCE per label	nn.BCEWithLogitsLoss	Each label independent — not mutually exclusive
Contrastive / metric learning	Triplet margin	nn.TripletMarginLoss	Learns embeddings

import torch
import torch.nn as nn

# Binary classification â output is a single logit (no sigmoid)
bce = nn.BCEWithLogitsLoss()  # applies sigmoid internally
logit = torch.tensor([2.0, -1.0, 0.5])
label = torch.tensor([1.0, 0.0, 1.0])
loss = bce(logit, label)

# Multi-class â outputs are raw logits per class (no softmax)
ce = nn.CrossEntropyLoss()
logits = torch.randn(8, 10)    # batch of 8, 10 classes
targets = torch.randint(0, 10, (8,))  # class indices 0-9
loss = ce(logits, targets)

# Class-weighted cross-entropy â for imbalanced datasets
weights = torch.tensor([1.0]*9 + [10.0])  # up-weight class 9
ce_weighted = nn.CrossEntropyLoss(weight=weights)

# Regression
mse = nn.MSELoss()
huber = nn.HuberLoss(delta=1.0)  # L2 for |error|<1, L1 for |error|>1
pred = torch.randn(32, 1)
true = torch.randn(32, 1)
print(mse(pred, true), huber(pred, true))

Take quiz

Why is nn.BCEWithLogitsLoss preferred over applying torch.sigmoid followed by nn.BCELoss?BCEWithLogitsLoss is faster because it skips the sigmoid computation

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Numerically: log(sigmoid(x)) is computed as log(1/(1+e^-x)) which can underflow for large x; BCEWithLogitsLoss uses the logsumexp trick to combine sigmoid and log stably

✓ Correct! Well done.

BCEWithLogitsLoss automatically adjusts the threshold from 0.5

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BCELoss produces incorrect gradients when the prediction is exactly 0 or 1

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What is a key difference between nn.CrossEntropyLoss and nn.BCEWithLogitsLoss?CrossEntropyLoss uses MSE internally; BCEWithLogitsLoss uses absolute error

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CrossEntropyLoss is for mutually exclusive multi-class problems (one label per sample); BCEWithLogitsLoss treats each output independently and is used for multi-label problems where multiple classes can be true simultaneously

✓ Correct! Well done.

BCEWithLogitsLoss requires softmax to be applied before passing logits

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CrossEntropyLoss does not support class weighting

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Python / Python Deep Learning and Neural Networks Interview Questions

What loss functions does PyTorch provide for classification and regression, and which to use when?

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