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tfruns: Tools for TensorFlow Training Runs

The tfruns package provides a suite of tools for tracking, visualizing, and managing TensorFlow training runs and experiments from R. Use the tfruns package to: Track the hyperparameters, metrics, output, and source code of every training run. Compare hyperparmaeters and metrics across runs to find the best performing model. Automatically generate reports to visualize individual training runs or comparisons between runs. You can install the tfruns package from GitHub as follows: Read more →

Keras for R

We are excited to announce that the keras package is now available on CRAN. The package provides an R interface to Keras, a high-level neural networks API developed with a focus on enabling fast experimentation. Keras has the following key features: Allows the same code to run on CPU or on GPU, seamlessly. User-friendly API which makes it easy to quickly prototype deep learning models. Built-in support for convolutional networks (for computer vision), recurrent networks (for sequence processing), and any combination of both. Read more →

TensorFlow Estimators

The tfestimators package is an R interface to TensorFlow Estimators, a high-level API that provides implementations of many different model types including linear models and deep neural networks. More models are coming soon such as state saving recurrent neural networks, dynamic recurrent neural networks, support vector machines, random forest, KMeans clustering, etc. TensorFlow estimators also provides a flexible framework for defining arbitrary new model types as custom estimators. The framework balances the competing demands for flexibility and simplicity by offering APIs at different levels of abstraction, making common model architectures available out of the box, while providing a library of utilities designed to speed up experimentation with model architectures. Read more →

TensorFlow v1.3 Released

The final release of TensorFlow v1.3 is now available. This release of TensorFlow marks the initial availability of several canned estimators, including: DNNClassifier DNNRegressor LinearClassifier LinearRegressor DNNLinearCombinedClassifier DNNLinearCombinedRegressor. The tfestimators package provides a high level R interface for these estimators. Full details on the release of TensorFlow v1.3 are available here: You can update your R installation of TensorFlow using the install_tensorflow function: library(tensorflow) install_tensorflow() Note that you should also provide any options used in your original installation (e. Read more →