If you're completely new to using R with Tensorflow, the hello_keras.Rmd notebook will give you an introduction.
Reproducibility.Rmd shows how to share and reuse models.
Visualize.Rmd is a playground for leveraging R's data visualization methods for introspecting experiment results.
Examples - You can find 2 examples in the R folder:
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mnist_mlp: Trains a simple deep NN on the MNIST dataset.
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cats_and_dogs_small: Convnet for dogs-versus-cats classification.
Download large datasets to the downloads/ folder and git will ignore them.
Models are written to the models/ folder.
- Installing RStudio's Tensorflow packages - https://keras.rstudio.com/
- R Interface to CloudML (training in the cloud) - https://tensorflow.rstudio.com/tools/cloudml/articles/getting_started.html
- Run RStudio in the Cloud with IBM Watson Studio - https://ibm.biz/BdYRNi
- IBM's Model Asset Exchange - https://developer.ibm.com/code/exchanges/models/
- Example Shiny App using Keras and LIME! https://jjallaire.shinyapps.io/keras-customer-churn/