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--- |
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library_name: torch |
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license: apache-2.0 |
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tags: |
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- pytorch |
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- loss-functions |
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- deep-learning |
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- machine-learning |
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- library |
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--- |
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# torchlosses |
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A lightweight library of advanced **PyTorch loss functions** — ready to plug into your training loop. |
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Designed for deep learning practitioners who want more than just MSE and CrossEntropy. |
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Available on **PyPI**: [torchlosses](https://pypi.org/project/torchlosses/) |
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--- |
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## Features |
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- **Focal Loss** — handle class imbalance in classification. |
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- **Dice Loss** — segmentation-friendly overlap metric. |
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- **Contrastive Loss** — learn pairwise similarity (Siamese nets). |
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- **Triplet Loss** — enforce anchor-positive vs negative separation. |
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- **Cosine Embedding Loss** — similarity learning with cosine distance. |
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- **Huber Loss** — robust regression, less sensitive to outliers. |
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- **KL Divergence Loss** — probability distribution alignment. |
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--- |
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## Created By |
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Naga Adithya Kaushik (GenAIDevTOProd) |
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https://medium.com/@GenAIDevTOProd/from-loss-functions-to-training-utilities-building-pytorch-packages-from-scratch-91e884d14001 |
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## Installation |
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```bash |
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pip install torchlosses |
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## Usage |
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import torch |
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from torchlosses import FocalLoss, DiceLoss |
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# Focal Loss for classification |
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inputs = torch.randn(4, 5, requires_grad=True) # logits for 5 classes |
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targets = torch.randint(0, 5, (4,)) |
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criterion = FocalLoss() |
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loss = criterion(inputs, targets) |
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print("Focal Loss:", loss.item()) |
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# Dice Loss for segmentation |
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inputs = torch.randn(4, 1, 8, 8) |
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targets = torch.randint(0, 2, (4, 1, 8, 8)) |
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criterion = DiceLoss() |
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loss = criterion(inputs, targets) |
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print("Dice Loss:", loss.item()) |
