This is the Windows app named TensorFlow Ranking whose latest release can be downloaded as TensorFlowRankingv0.5.2.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named TensorFlow Ranking with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS:
TensorFlow Ranking
DESCRIPTION:
TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform. Commonly used loss functions including pointwise, pairwise, and listwise losses. Commonly used ranking metrics like Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG). Multi-item (also known as groupwise) scoring functions. LambdaLoss implementation for direct ranking metric optimization. Unbiased Learning-to-Rank from biased feedback data. We envision that this library will provide a convenient open platform for hosting and advancing state-of-the-art ranking models based on deep learning techniques, and thus facilitate both academic research and industrial applications. We provide a demo, with no installation required, to get started on using TF-Ranking. This demo runs on a colaboratory notebook, an interactive Python environment. Using sparse features and embeddings in TF-Ranking.
Features
- Use sparse/embedding features
- Process data in TFRecord format
- Tensorboard integration in colab notebook, for Estimator API
- Build TensorFlow Ranking locally
- For ease of experimentation, we also provide a TFRecord example and a LIBSVM example
- The training results such as loss and metrics can be visualized using Tensorboard
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/tensorflow-ranking.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.