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tfestimators - R Interface to TensorFlow Estimator API

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.

  • Standalone deployment of models (no R runtime required) in a wide variety of environments.

  • An Experiment API that provides distributed training and hyperparameter tuning for both canned and custom estimators.

For documentation on using tfestimators, see the package website at https://tensorflow.rstudio.com/tfestimators

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Install

install.packages('tfestimators')

Monthly Downloads

338

Version

1.9.1

License

Apache License 2.0

Issues

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Maintainer

Kevin Kuo

Last Published

November 7th, 2018

Functions in tfestimators (1.9.1)

column-scope

Establish a Feature Columns Selection Scope
boosted_trees_estimators

Boosted Trees Estimator
column_crossed

Construct a Crossed Column
column_embedding

Construct a Dense Column
classifier_parse_example_spec

Generates Parsing Spec for TensorFlow Example to be Used with Classifiers
column_indicator

Represents Multi-Hot Representation of Given Categorical Column
column_numeric

Construct a Real-Valued Column
keras_model_to_estimator

Keras Estimators
latest_checkpoint

Get the Latest Checkpoint in a Checkpoint Directory
graph_keys

Standard Names to Use for Graph Collections
reexports

Objects exported from other packages
input_fn

Construct an Input Function
input_layer

Construct an Input Layer
hook_checkpoint_saver

Saves Checkpoints Every N Steps or Seconds
column_categorical_with_hash_bucket

Represents Sparse Feature where IDs are set by Hashing
column_bucketized

Construct a Bucketized Column
run_config

Run Configuration
regressor_parse_example_spec

Generates Parsing Spec for TensorFlow Example to be Used with Regressors
evaluate.tf_estimator

Evaluate an Estimator
column_categorical_with_identity

Construct a Categorical Column that Returns Identity Values
session_run_args

Create Session Run Arguments
train_spec

Configuration for the train component of train_and_evaluate
variable_names_values

Get variable names and values associated with an estimator
column_categorical_with_vocabulary_file

Construct a Categorical Column with a Vocabulary File
column_categorical_weighted

Construct a Weighted Categorical Column
column_categorical_with_vocabulary_list

Construct a Categorical Column with In-Memory Vocabulary
experiment

Construct an Experiment
estimators

Base Documentation for Canned Estimators
hook_stop_at_step

Monitor to Request Stop at a Specified Step
eval_spec

Configuration for the eval component of train_and_evaluate
hook_summary_saver

Saves Summaries Every N Steps
mode_keys

Canonical Mode Keys
model_dir

Model directory
hook_logging_tensor

Prints Given Tensors Every N Local Steps, Every N Seconds, or at End
session_run_hook

Create Custom Session Run Hooks
dnn_estimators

Deep Neural Networks
estimator

Construct a Custom Estimator
dnn_linear_combined_estimators

Linear Combined Deep Neural Networks
hook_nan_tensor

NaN Loss Monitor
estimator_spec

Define an Estimator Specification
export_savedmodel.tf_estimator

Save an Estimator
hook_global_step_waiter

Delay Execution until Global Step Reaches to wait_until_step.
task_type

Task Types
hook_progress_bar

A Custom Run Hook to Create and Update Progress Bar During Training or Evaluation
hook_step_counter

Steps per Second Monitor
hook_history_saver

A Custom Run Hook for Saving Metrics History
predict.tf_estimator

Generate Predictions with an Estimator
feature_columns

Feature Columns
prediction_keys

Canonical Model Prediction Keys
linear_estimators

Construct a Linear Estimator
train.tf_estimator

Train an Estimator
metric_keys

Canonical Metric Keys
numpy_input_fn

Construct Input Function Containing Python Dictionaries of Numpy Arrays
train_and_evaluate.tf_estimator

Train and evaluate the estimator.
plot.tf_estimator_history

Plot training history
tfestimators

High-level Estimator API in TensorFlow for R
train-evaluate-predict

Base Documentation for train, evaluate, and predict.
column_base

Base Documentation for Feature Column Constructors