Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.

Related tags

Deep Learning ai tensorflow keras generative-model tensorflow2
Overview

Sarus published models

Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.

The required packages are managed with pipenv and can be installed using pipenv install. Please see the pipenv documentation for more information.

Philosophy

These models' implementations are intended to be easy to read and to adapt by making use of the latest Tensorflow 2 library and Keras API.

Basic usage

To install and train a model.

pipenv install
pipenv shell
python train.py

To visualize losses and reconstructions.

tensorboard --logdir ./logs/

Available models

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Comments
  • Detailed example ?

    Detailed example ?

    Hi,

    First of all thank you very much for your repo.

    I am studying your CNP implementation and, as for the other networks you propose, you stop the script to the training of the model. Could you please provide the code to fully reproduce the figure you show (especially for the regression) ?

    Hopefully this detailed example will call the plot functions implemented in the utils/ directory.

    Thank you in advance for your assistance.

    Cheers, Philippe

    opened by pbacon42 2
Owner
Sarus Technologies
Sarus Technologies
GitHub https://sarus.tech/
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