tfestimators v1.9.1

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Interface to 'TensorFlow' Estimators

Interface to 'TensorFlow' Estimators <https://www.tensorflow.org/programmers_guide/estimators>, a high-level API that provides implementations of many different model types including linear models and deep neural networks.

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tfestimators - R Interface to TensorFlow Estimator API

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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

Functions in tfestimators

Name Description
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
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Vignettes of tfestimators

Name
examples/custom_estimator.R
examples/custom_estimator.Rmd
examples/index.Rmd
examples/iris_custom_decay_dnn.R
examples/iris_custom_decay_dnn.Rmd
examples/iris_dnn_classifier.R
examples/iris_dnn_classifier.Rmd
examples/mnist.R
examples/mnist.Rmd
examples/tensorflow_layers.R
examples/tensorflow_layers.Rmd
examples/wide_and_deep.R
examples/wide_and_deep.Rmd
images/estimator-apis.png
images/estimator.png
images/experiment.png
images/tensorboard-graph-details.png
images/tensorboard-graph.png
images/tensorboard-loss.png
images/tensorflow-architecture.png
creating_estimators.Rmd
dataset_api.Rmd
estimator_basics.Rmd
feature_columns.Rmd
input_functions.Rmd
parsing_spec.Rmd
run_hooks.Rmd
tensorboard.Rmd
tensorflow_layers.Rmd
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Last month downloads

Details

Type Package
License Apache License 2.0
URL https://github.com/rstudio/tfestimators
BugReports https://github.com/rstudio/tfestimators/issues
SystemRequirements TensorFlow (https://www.tensorflow.org/)
Encoding UTF-8
LazyData true
RoxygenNote 6.1.0
VignetteBuilder knitr
NeedsCompilation no
Packaged 2018-11-07 10:15:19 UTC; kevinykuo
Repository CRAN
Date/Publication 2018-11-07 10:30:02 UTC

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