Metadata-Version: 2.1
Name: torchpropel
Version: 0.0.2
Summary: Probabilistic Parameteric Regression Loss (PROPEL)
Home-page: http://github.com/masadcv/torchpropel
Author: Muhammad Asad
Author-email: muhammad.asad@kcl.ac.uk
License: BSD-3-Clause
Description: # PRObablistic Parametric rEgression Loss (PROPEL) 
        PRObabilistic Parametric rEgresison Loss (PROPEL) is a loss function that enables probabilisitic regression for a neural network. It achieves this by enabling a neural network to learn parameters of a mixture of Gaussian distribution. 
        
        Further details about the loss can be found in the paper: [PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks](https://arxiv.org/pdf/1807.10937.pdf)
        
        This repository provides official pytorch implementation of PROPEL. 
        
        # Installation Instructions
        PROPEL can be installed using the following command 
        
        ```bash
        pip install torchpropel
        ```
        
        
        ```bash
        pip install git+https://github.com/masadcv/PROPEL.git
        ```
        
        # Usage Example
        ```python
        import torch
        import numpy as np
        
        from torchpropel import PROPEL
        
        # Our example has a neural network with
        # output [num_batch, num_gaussians, num_dims]
        num_batch = 4
        num_gaussians = 6
        num_dims = 3
        
        # setting ground-truth variance sigma_gt=0.2
        sigma_gt = 0.2
        propel_loss = PROPEL(sigma_gt)
        
        # ground truth targets for loss
        y = torch.ones((num_batch, num_dims)) * 0.5
        
        # example prediction - this can also be coming as output of a neural network
        feat_g = np.random.randn(num_batch, num_gaussians, 2 * num_dims) * 0.5
        feat_g[:, :, num_dims::] = 0.2
        feat = torch.tensor(feat_g, dtype=y.dtype)
        
        # compute the loss
        L = propel_loss(feat, y)
        
        print(L)
        ```
        # Documentation
        Further details of each function implemented for PROPEL can be accessed at the documentation hosted at: [https://masadcv.github.io/PROPEL/index.html](https://masadcv.github.io/PROPEL/index.html). 
        
        # Citing PROPEL
        Pre-print of PROPEL can be found at: [PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks](https://arxiv.org/pdf/1807.10937.pdf)
        
        If you use PROPEL in your research, then please cite:
        
        BibTeX:
        ```
        @inproceedings{asad2020propel,
          title={PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks},
          author={Asad, Muhammad and Basaru, Rilwan and Arif, SM and Slabaugh, Greg},
          booktitle={25th International Conference on Pattern Recognition},
          pages={},
          year={2020}}
        ```
        
Keywords: regression probabilistic neural networks machine learning
Platform: UNKNOWN
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
