Practical overview
When it fits
Use it when building or experimenting with a PyTorch regression pipeline and you want a histogram-based loss, an embedding-to-logits layer, or a documented regular-regression comparison.
Who it is for
- PyTorch developers adding a distribution-based loss to a regression model.
- Researchers comparing Gaussian histogram loss with regular regression.
- Reinforcement-learning practitioners exploring value-function training with histogram loss.
Documented uses
- Calculate Gaussian histogram loss from model logits and floating-point targets.
- Add an embedding-to-logits prediction layer to a model.
- Compare the layer's histogram-loss approach with regular regression.
- Explore histogram-loss training in value-function experiments.
Requirements & limits
The repository is public, identifies an MIT license, and documents installation with pip.
- HLGaussLoss expects logits over the configured bins; the example uses a final dimension matching
num_bins. - Choose the target range, bin count and sigma to fit the task; the README supplies an example but does not recommend universal settings.
- The README warns that out-of-bin targets can yield zero loss with the shown logic and demonstrates
clamp_to_range=True. - HLGaussLayer accepts embeddings and can switch to regular regression with
use_regression=True. - The library provides a training component; model design, data and the surrounding reinforcement-learning setup remain the user's responsibility.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Add HLGaussLoss to a PyTorch training step
Install the package, calculate histogram loss from logits and targets, and obtain a prediction from the loss module.
Before you start
- Python environment with PyTorch available.
- A model outputting logits over the configured bins and floating-point targets.
Steps
Install the package with the command below.
pip install hl-gauss-pytorchImport
HLGaussLossand configuremin_value,max_value,num_binsandsigmafor the target range.Pass logits and targets to the loss module, then call
loss.backward()as shown in the README.After training, pass logits to the module to obtain predicted target values.
Expected outcome
A documented PyTorch loss calculation with a prediction interface for the configured histogram bins.
Guide limitations
- The example notes that targets outside the bins can produce zero loss with the current logic; it sets
clamp_to_range=Trueto address this. - The README does not prescribe suitable range, bin or sigma values for a particular dataset.
Use HLGaussLayer for embedding-based regression
Configure the documented layer that maps embeddings to logits and computes a target loss.
Before you start
- Python environment with PyTorch available.
- Input embeddings and floating-point targets, with a known embedding dimension and target range.
Steps
Install the package with the command below.
pip install hl-gauss-pytorchImport
HLGaussLayerand setdimto the embedding dimension.Provide loss settings such as
min_value,max_value,num_binsandsigmathroughhl_gauss_loss.Pass embeddings and targets to the layer, then call
loss.backward(); after training, pass embeddings alone to obtain predictions.
Expected outcome
A layer that calculates the documented histogram loss during training and returns target predictions from embeddings.
Guide limitations
- The README does not specify parameter selection for a particular task.
- The
use_regression=Trueoption switches the layer to regular regression; it is not an additional histogram-loss mode.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 12 October 2026
- Original website title
- lucidrains/hl-gauss-pytorch · Official homepage title · retained in its source language