---
library_name: torch
license: apache-2.0
tags:
- pytorch
- loss-functions
- deep-learning
- machine-learning
- library
---

# torchlosses

A lightweight library of advanced **PyTorch loss functions** — ready to plug into your training loop.  
Designed for deep learning practitioners who want more than just MSE and CrossEntropy.  

Available on **PyPI**: [torchlosses](https://pypi.org/project/torchlosses/)  

---

## Features
- **Focal Loss** — handle class imbalance in classification.
- **Dice Loss** — segmentation-friendly overlap metric.
- **Contrastive Loss** — learn pairwise similarity (Siamese nets).
- **Triplet Loss** — enforce anchor-positive vs negative separation.
- **Cosine Embedding Loss** — similarity learning with cosine distance.
- **Huber Loss** — robust regression, less sensitive to outliers.
- **KL Divergence Loss** — probability distribution alignment.

---

## Created By

Naga Adithya Kaushik (GenAIDevTOProd)
https://medium.com/@GenAIDevTOProd/from-loss-functions-to-training-utilities-building-pytorch-packages-from-scratch-91e884d14001

## Installation

```bash
pip install torchlosses

## Usage

import torch
from torchlosses import FocalLoss, DiceLoss

# Focal Loss for classification
inputs = torch.randn(4, 5, requires_grad=True)  # logits for 5 classes
targets = torch.randint(0, 5, (4,))
criterion = FocalLoss()
loss = criterion(inputs, targets)
print("Focal Loss:", loss.item())

# Dice Loss for segmentation
inputs = torch.randn(4, 1, 8, 8)
targets = torch.randint(0, 2, (4, 1, 8, 8))
criterion = DiceLoss()
loss = criterion(inputs, targets)
print("Dice Loss:", loss.item())