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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

899

Version

1.9.2

License

Apache License 2.0

Issues

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Maintainer

Tomasz Kalinowski

Last Published

August 9th, 2021

Functions in tfestimators (1.9.2)

column_categorical_with_identity

Construct a Categorical Column that Returns Identity Values
column_categorical_with_vocabulary_file

Construct a Categorical Column with a Vocabulary File
boosted_trees_estimators

Boosted Trees Estimator
column_base

Base Documentation for Feature Column Constructors
column_bucketized

Construct a Bucketized Column
column_categorical_with_hash_bucket

Represents Sparse Feature where IDs are set by Hashing
column_categorical_with_vocabulary_list

Construct a Categorical Column with In-Memory Vocabulary
classifier_parse_example_spec

Generates Parsing Spec for TensorFlow Example to be Used with Classifiers
column-scope

Establish a Feature Columns Selection Scope
column_categorical_weighted

Construct a Weighted Categorical Column
column_crossed

Construct a Crossed Column
eval_spec

Configuration for the eval component of train_and_evaluate
estimators

Base Documentation for Canned Estimators
dnn_estimators

Deep Neural Networks
dnn_linear_combined_estimators

Linear Combined Deep Neural Networks
estimator_spec

Define an Estimator Specification
estimator

Construct a Custom Estimator
evaluate.tf_estimator

Evaluate an Estimator
experiment

Construct an Experiment
hook_global_step_waiter

Delay Execution until Global Step Reaches to wait_until_step.
hook_history_saver

A Custom Run Hook for Saving Metrics History
column_indicator

Represents Multi-Hot Representation of Given Categorical Column
column_numeric

Construct a Real-Valued Column
graph_keys

Standard Names to Use for Graph Collections
hook_progress_bar

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

Construct a Linear Estimator
metric_keys

Canonical Metric Keys
hook_checkpoint_saver

Saves Checkpoints Every N Steps or Seconds
hook_step_counter

Steps per Second Monitor
export_savedmodel.tf_estimator

Save an Estimator
hook_nan_tensor

NaN Loss Monitor
hook_logging_tensor

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

Keras Estimators
hook_stop_at_step

Monitor to Request Stop at a Specified Step
input_fn

Construct an Input Function
session_run_args

Create Session Run Arguments
run_config

Run Configuration
input_layer

Construct an Input Layer
column_embedding

Construct a Dense Column
latest_checkpoint

Get the Latest Checkpoint in a Checkpoint Directory
prediction_keys

Canonical Model Prediction Keys
hook_summary_saver

Saves Summaries Every N Steps
predict.tf_estimator

Generate Predictions with an Estimator
train_spec

Configuration for the train component of train_and_evaluate
variable_names_values

Get variable names and values associated with an estimator
plot.tf_estimator_history

Plot training history
train-evaluate-predict

Base Documentation for train, evaluate, and predict.
tfestimators

High-level Estimator API in TensorFlow for R
numpy_input_fn

Construct Input Function Containing Python Dictionaries of Numpy Arrays
session_run_hook

Create Custom Session Run Hooks
task_type

Task Types
model_dir

Model directory
train_and_evaluate.tf_estimator

Train and evaluate the estimator.
mode_keys

Canonical Mode Keys
train.tf_estimator

Train an Estimator
feature_columns

Feature Columns
regressor_parse_example_spec

Generates Parsing Spec for TensorFlow Example to be Used with Regressors
reexports

Objects exported from other packages