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.
Readme
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
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
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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 |
| imports | forge , magrittr , progress , purrr , reticulate (>= 1.10) , rlang (>= 0.3) , tensorflow (>= 1.9) , tfruns (>= 1.1) , tibble , tidyr , tidyselect , utils |
| suggests | ggplot2 , knitr , modelr (>= 0.1.1) , testthat |
| depends | R (>= 3.1) |
| Contributors | JJ Allaire, Kevin Ushey, Yuan Tang, RStudio, Daniel Falbel, Google Inc. |
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