This is the Windows app named PML whose latest release can be downloaded as v2.3.0.zip. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS
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PML
DESCRIPTION
This library contains 9 modules, each of which can be used independently within your existing codebase, or combined together for a complete train/test workflow. To compute the loss in your training loop, pass in the embeddings computed by your model, and the corresponding labels. The embeddings should have size (N, embedding_size), and the labels should have size (N), where N is the batch size. The TripletMarginLoss computes all possible triplets within the batch, based on the labels you pass into it. Anchor-positive pairs are formed by embeddings that share the same label, and anchor-negative pairs are formed by embeddings that have different labels. Loss functions can be customized using distances, reducers, and regularizers. In the diagram below, a miner finds the indices of hard pairs within a batch. These are used to index into the distance matrix, computed by the distance object. For this diagram, the loss function is pair-based, so it computes a loss per pair.
Features
- Customize loss functions
- Use loss functions for unsupervised / self-supervised learning
- Required PyTorch version torch >= 1.6
- Development is done on the dev branch
- Code is formatted using black and isort
- You can specify the test datatypes and test device as environment variables
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/pml.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
