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R Interface to TensorFlow

14-05-2019  0 Comment(s)

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

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.

  • A flexible framework for defining arbitrary new model types as custom estimators.

 

The following canned estimators are currently available:

Estimator Description
linear_regressor() Linear regressor model.
linear_classifier() Linear classifier model.
dnn_regressor() DNN Regression.
dnn_classifier() DNN Classification.
dnn_linear_combined_regressor() DNN Linear Combined Regression.
dnn_linear_combined_classifier() DNN Linear Combined Classification.

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