Interface to 'TensorFlow' Estimators

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Documentation for package ‘tfestimators’ version 1.9.2

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

boosted_trees_classifier	Boosted Trees Estimator
boosted_trees_estimators	Boosted Trees Estimator
boosted_trees_regressor	Boosted Trees Estimator
classifier_parse_example_spec	Generates Parsing Spec for TensorFlow Example to be Used with Classifiers
column-scope	Establish a Feature Columns Selection Scope
column_base	Base Documentation for Feature Column Constructors
column_bucketized	Construct a Bucketized Column
column_categorical_weighted	Construct a Weighted Categorical Column
column_categorical_with_hash_bucket	Represents Sparse Feature where IDs are set by Hashing
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
column_categorical_with_vocabulary_list	Construct a Categorical Column with In-Memory Vocabulary
column_crossed	Construct a Crossed Column
column_embedding	Construct a Dense Column
column_indicator	Represents Multi-Hot Representation of Given Categorical Column
column_numeric	Construct a Real-Valued Column
dnn_classifier	Deep Neural Networks
dnn_estimators	Deep Neural Networks
dnn_linear_combined_classifier	Linear Combined Deep Neural Networks
dnn_linear_combined_estimators	Linear Combined Deep Neural Networks
dnn_linear_combined_regressor	Linear Combined Deep Neural Networks
dnn_regressor	Deep Neural Networks
estimator	Construct a Custom Estimator
estimators	Base Documentation for Canned Estimators
estimator_spec	Define an Estimator Specification
evaluate.tf_estimator	Evaluate an Estimator
eval_spec	Configuration for the eval component of 'train_and_evaluate'
experiment	Construct an Experiment
export_savedmodel.tf_estimator	Save an Estimator
feature_columns	Feature Columns
graph_keys	Standard Names to Use for Graph Collections
hook_checkpoint_saver	Saves Checkpoints Every N Steps or Seconds
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
hook_logging_tensor	Prints Given Tensors Every N Local Steps, Every N Seconds, or at End
hook_nan_tensor	NaN Loss Monitor
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_stop_at_step	Monitor to Request Stop at a Specified Step
hook_summary_saver	Saves Summaries Every N Steps
input_fn	Construct an Input Function
input_fn.data.frame	Construct an Input Function
input_fn.default	Construct an Input Function
input_fn.formula	Construct an Input Function
input_fn.list	Construct an Input Function
input_fn.matrix	Construct an Input Function
input_layer	Construct an Input Layer
keras_model_to_estimator	Keras Estimators
latest_checkpoint	Get the Latest Checkpoint in a Checkpoint Directory
linear_classifier	Construct a Linear Estimator
linear_estimators	Construct a Linear Estimator
linear_regressor	Construct a Linear Estimator
metric_keys	Canonical Metric Keys
model_dir	Model directory
mode_keys	Canonical Mode Keys
numpy_input_fn	Construct Input Function Containing Python Dictionaries of Numpy Arrays
plot.tf_estimator_history	Plot training history
predict.tf_estimator	Generate Predictions with an Estimator
prediction_keys	Canonical Model Prediction Keys
regressor_parse_example_spec	Generates Parsing Spec for TensorFlow Example to be Used with Regressors
run_config	Run Configuration
scoped_columns	Establish a Feature Columns Selection Scope
session_run_args	Create Session Run Arguments
session_run_hook	Create Custom Session Run Hooks
set_columns	Establish a Feature Columns Selection Scope
task_type	Task Types
tfestimators	High-level Estimator API in TensorFlow for R
train-evaluate-predict	Base Documentation for train, evaluate, and predict.
train.tf_estimator	Train an Estimator
train_and_evaluate.tf_estimator	Train and evaluate the estimator.
train_spec	Configuration for the train component of 'train_and_evaluate'
variable_names	Get variable names and values associated with an estimator
variable_names_values	Get variable names and values associated with an estimator
variable_value	Get variable names and values associated with an estimator
with_columns	Establish a Feature Columns Selection Scope