NGC | Catalog
Welcome Guest
CatalogModelsWide & Deep TF checkpoint (Base, 128k, v1, AMP)

Wide & Deep TF checkpoint (Base, 128k, v1, AMP)

For downloads and more information, please view on a desktop device.
Logo for Wide & Deep TF checkpoint (Base, 128k, v1, AMP)

Description

Wide & Deep Base TensorFlow checkpoint trained with AMP

Publisher

NVIDIA Deep Learning Examples

Use Case

Recommendation

Framework

TensorFlow

Latest Version

20.06.0

Modified

February 2, 2022

Size

769.74 MB

Model Overview

Wide & Deep Recommender model.

Model Architecture

Wide & Deep refers to a class of networks that use the output of two parts working in parallel - wide model and deep model - to make predictions of recommenders. The wide model is a generalized linear model of features together with their transforms. The deep model is a series of 5 hidden MLP layers of 1024 neurons each beginning with a dense embedding of features. The architecture is presented in Figure 1.


Figure 1. The architecture of the Wide & Deep model.

Training

This model was trained using script available on NGC and in GitHub repo

Dataset

The following datasets were used to train this model:

  • Outbrain - Dataset containing a sample of users’ page views and clicks, as observed on multiple publisher sites in the United States between 14-June-2016 and 28-June-2016. Each viewed page or clicked recommendation is further accompanied by some semantic attributes of those documents.

Performance

Performance numbers for this model are available in NGC

References

License

This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.