Package 'tfestimators' reference manual

Title:	Interface to 'TensorFlow' Estimators
Description:	Interface to 'TensorFlow' Estimators <https://www.tensorflow.org/guide/estimator>, a high-level API that provides implementations of many different model types including linear models and deep neural networks.
Authors:	JJ Allaire [aut], Yuan Tang [aut] , Kevin Ushey [aut], Kevin Kuo [aut] , Tomasz Kalinowski [cre], Daniel Falbel [ctb, cph], RStudio [cph, fnd], Google Inc. [cph]
Maintainer:	Tomasz Kalinowski <[email protected]>
License:	Apache License 2.0
Version:	1.9.2
Built:	2024-11-11 03:45:02 UTC
Source:	https://github.com/rstudio/tfestimators

Boosted Trees Estimator

Description

Construct a boosted trees estimator.

Usage

boosted_trees_regressor(
  feature_columns,
  n_batches_per_layer,
  model_dir = NULL,
  label_dimension = 1L,
  weight_column = NULL,
  n_trees = 100L,
  max_depth = 6L,
  learning_rate = 0.1,
  l1_regularization = 0,
  l2_regularization = 0,
  tree_complexity = 0,
  min_node_weight = 0,
  config = NULL
)

boosted_trees_classifier(
  feature_columns,
  n_batches_per_layer,
  model_dir = NULL,
  n_classes = 2L,
  weight_column = NULL,
  label_vocabulary = NULL,
  n_trees = 100L,
  max_depth = 6L,
  learning_rate = 0.1,
  l1_regularization = 0,
  l2_regularization = 0,
  tree_complexity = 0,
  min_node_weight = 0,
  config = NULL
)
boosted_trees_regressor(
  feature_columns,
  n_batches_per_layer,
  model_dir = NULL,
  label_dimension = 1L,
  weight_column = NULL,
  n_trees = 100L,
  max_depth = 6L,
  learning_rate = 0.1,
  l1_regularization = 0,
  l2_regularization = 0,
  tree_complexity = 0,
  min_node_weight = 0,
  config = NULL
)

boosted_trees_classifier(
  feature_columns,
  n_batches_per_layer,
  model_dir = NULL,
  n_classes = 2L,
  weight_column = NULL,
  label_vocabulary = NULL,
  n_trees = 100L,
  max_depth = 6L,
  learning_rate = 0.1,
  l1_regularization = 0,
  l2_regularization = 0,
  tree_complexity = 0,
  min_node_weight = 0,
  config = NULL
)

Arguments

`feature_columns`	An R list containing all of the feature columns used by the model (typically, generated by `feature_columns()`).
`n_batches_per_layer`	The number of batches to collect statistics per layer.
`model_dir`	Directory to save the model parameters, graph, and so on. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
`label_dimension`	Number of regression targets per example. This is the size of the last dimension of the labels and logits `Tensor` objects (typically, these have shape `⁠[batch_size, label_dimension]⁠`).
`weight_column`	A string, or a numeric column created by `column_numeric()` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the `features` argument. If it is a numeric column, then the raw tensor is fetched by key `weight_column$key`, then `weight_column$normalizer_fn` is applied on it to get weight tensor.
`n_trees`	Number trees to be created.
`max_depth`	Maximum depth of the tree to grow.
`learning_rate`	Shrinkage parameter to be used when a tree added to the model.
`l1_regularization`	Regularization multiplier applied to the absolute weights of the tree leafs.
`l2_regularization`	Regularization multiplier applied to the square weights of the tree leafs.
`tree_complexity`	Regularization factor to penalize trees with more leaves.
`min_node_weight`	Minimum hessian a node must have for a split to be considered. The value will be compared with sum(leaf_hessian)/(batch_size * n_batches_per_layer).
`config`	A run configuration created by `run_config()`, used to configure the runtime settings.
`n_classes`	The number of label classes.
`label_vocabulary`	A list of strings represents possible label values. If given, labels must be string type and have any value in `label_vocabulary`. If it is not given, that means labels are already encoded as integer or float within `⁠[0, 1]⁠` for `n_classes == 2` and encoded as integer values in `⁠{0, 1,..., n_classes -1}⁠` for `n_classes > 2`. Also there will be errors if vocabulary is not provided and labels are string.

Generates Parsing Spec for TensorFlow Example to be Used with Classifiers

Description

If users keep data in TensorFlow Example format, they need to call tf$parse_example with a proper feature spec. There are two main things that this utility helps:

Users need to combine parsing spec of features with labels and weights (if any) since they are all parsed from same tf$Example instance. This utility combines these specs.
It is difficult to map expected label by a classifier such as dnn_classifier to corresponding tf$parse_example spec. This utility encodes it by getting related information from users (key, dtype).

Usage

classifier_parse_example_spec(
  feature_columns,
  label_key,
  label_dtype = tf$int64,
  label_default = NULL,
  weight_column = NULL
)
classifier_parse_example_spec(
  feature_columns,
  label_key,
  label_dtype = tf$int64,
  label_default = NULL,
  weight_column = NULL
)

Arguments

`feature_columns`	An iterable containing all feature columns. All items should be instances of classes derived from `⁠_FeatureColumn⁠`.
`label_key`	A string identifying the label. It means `tf$Example` stores labels with this key.
`label_dtype`	A `tf$dtype` identifies the type of labels. By default it is `tf$int64`. If user defines a `label_vocabulary`, this should be set as `tf$string`. `tf$float32` labels are only supported for binary classification.
`label_default`	used as label if label_key does not exist in given `tf$Example`. An example usage: let's say `label_key` is 'clicked' and `tf$Example` contains clicked data only for positive examples in following format `⁠key:clicked, value:1⁠`. This means that if there is no data with key 'clicked' it should count as negative example by setting `label_deafault=0`. Type of this value should be compatible with `label_dtype`.
`weight_column`	A string or a numeric column created by `column_numeric()` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the `features`. If it is a numeric column, raw tensor is fetched by key `weight_column$key`, then `weight_column$normalizer_fn` is applied on it to get weight tensor.

Value

A dict mapping each feature key to a FixedLenFeature or VarLenFeature value.

Raises

ValueError: If label is used in feature_columns.
ValueError: If weight_column is used in feature_columns.
ValueError: If any of the given feature_columns is not a feature column instance.
ValueError: If weight_column is not a numeric column instance.
ValueError: if label_key is NULL.

Base Documentation for Feature Column Constructors

Description

Base Documentation for Feature Column Constructors

Arguments

...

Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns.

Construct a Bucketized Column

Description

Construct a bucketized column, representing discretized dense input. Buckets include the left boundary, and exclude the right boundary.

Usage

column_bucketized(source_column, boundaries)
column_bucketized(source_column, boundaries)

Arguments

`source_column`	A one-dimensional dense column, as generated by `column_numeric()`.
`boundaries`	A sorted list or list of floats specifying the boundaries.

Value

A bucketized column.

Raises

ValueError: If source_column is not a numeric column, or if it is not one-dimensional.
ValueError: If boundaries is not a sorted list or list.

Construct a Weighted Categorical Column

Description

Use this when each of your sparse inputs has both an ID and a value. For example, if you're representing text documents as a collection of word frequencies, you can provide 2 parallel sparse input features ('terms' and 'frequencies' below).

Usage

column_categorical_weighted(
  categorical_column,
  weight_feature_key,
  dtype = tf$float32
)
column_categorical_weighted(
  categorical_column,
  weight_feature_key,
  dtype = tf$float32
)

Arguments

`categorical_column`	A categorical column created by `⁠column_categorical_*()⁠` functions.
`weight_feature_key`	String key for weight values.
`dtype`	Type of weights, such as `tf$float32`. Only float and integer weights are supported.

Value

A categorical column composed of two sparse features: one represents id, the other represents weight (value) of the id feature in that example.

Raises

ValueError: if dtype is not convertible to float.

Represents Sparse Feature where IDs are set by Hashing

Description

Use this when your sparse features are in string or integer format, and you want to distribute your inputs into a finite number of buckets by hashing. output_id = Hash(input_feature_string) % bucket_size For input dictionary features, ⁠features$key$⁠ is either tensor or sparse tensor object. If it's tensor object, missing values can be represented by -1 for int and '' for string. Note that these values are independent of the default_value argument.

Usage

column_categorical_with_hash_bucket(..., hash_bucket_size, dtype = tf$string)
column_categorical_with_hash_bucket(..., hash_bucket_size, dtype = tf$string)

Arguments

`...`	Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns.
`hash_bucket_size`	An int > 1. The number of buckets.
`dtype`	The type of features. Only string and integer types are supported.

Value

A ⁠_HashedCategoricalColumn⁠.

Raises

ValueError: hash_bucket_size is not greater than 1.
ValueError: dtype is neither string nor integer.

Construct a Categorical Column that Returns Identity Values

Description

Use this when your inputs are integers in the range ⁠[0, num_buckets)⁠, and you want to use the input value itself as the categorical ID. Values outside this range will result in default_value if specified, otherwise it will fail.

Usage

column_categorical_with_identity(..., num_buckets, default_value = NULL)
column_categorical_with_identity(..., num_buckets, default_value = NULL)

Arguments

`...`	Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns.
`num_buckets`	Number of unique values.
`default_value`	If `NULL`, this column's graph operations will fail for out-of-range inputs. Otherwise, this value must be in the range `⁠[0, num_buckets)⁠`, and will replace inputs in that range.

Details

Typically, this is used for contiguous ranges of integer indexes, but it doesn't have to be. This might be inefficient, however, if many of IDs are unused. Consider column_categorical_with_hash_bucket() in that case.

For input dictionary features, features$key is either tensor or sparse tensor object. If it's tensor object, missing values can be represented by -1 for int and '' for string. Note that these values are independent of the default_value argument.

Value

A categorical column that returns identity values.

Raises

ValueError: if num_buckets is less than one.
ValueError: if default_value is not in range ⁠[0, num_buckets)⁠.

Construct a Categorical Column with a Vocabulary File

Description

Use this when your inputs are in string or integer format, and you have a vocabulary file that maps each value to an integer ID. By default, out-of-vocabulary values are ignored. Use either (but not both) of num_oov_buckets and default_value to specify how to include out-of-vocabulary values. For input dictionary features, features[key] is either tensor or sparse tensor object. If it's tensor object, missing values can be represented by -1 for int and '' for string. Note that these values are independent of the default_value argument.

Usage

column_categorical_with_vocabulary_file(
  ...,
  vocabulary_file,
  vocabulary_size,
  num_oov_buckets = 0L,
  default_value = NULL,
  dtype = tf$string
)
column_categorical_with_vocabulary_file(
  ...,
  vocabulary_file,
  vocabulary_size,
  num_oov_buckets = 0L,
  default_value = NULL,
  dtype = tf$string
)

Arguments

`...`	Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns.
`vocabulary_file`	The vocabulary file name.
`vocabulary_size`	Number of the elements in the vocabulary. This must be no greater than length of `vocabulary_file`, if less than length, later values are ignored.
`num_oov_buckets`	Non-negative integer, the number of out-of-vocabulary buckets. All out-of-vocabulary inputs will be assigned IDs in the range `⁠[vocabulary_size, vocabulary_size+num_oov_buckets)⁠` based on a hash of the input value. A positive `num_oov_buckets` can not be specified with `default_value`.
`default_value`	The integer ID value to return for out-of-vocabulary feature values, defaults to `-1`. This can not be specified with a positive `num_oov_buckets`.
`dtype`	The type of features. Only string and integer types are supported.

Value

A categorical column with a vocabulary file.

Raises

ValueError: vocabulary_file is missing.
ValueError: vocabulary_size is missing or < 1.
ValueError: num_oov_buckets is not a non-negative integer.
ValueError: dtype is neither string nor integer.

Construct a Categorical Column with In-Memory Vocabulary

Description

Use this when your inputs are in string or integer format, and you have an in-memory vocabulary mapping each value to an integer ID. By default, out-of-vocabulary values are ignored. Use default_value to specify how to include out-of-vocabulary values. For the input dictionary features, features$key is either tensor or sparse tensor object. If it's tensor object, missing values can be represented by -1 for int and '' for string.

Usage

column_categorical_with_vocabulary_list(
  ...,
  vocabulary_list,
  dtype = NULL,
  default_value = -1L,
  num_oov_buckets = 0L
)
column_categorical_with_vocabulary_list(
  ...,
  vocabulary_list,
  dtype = NULL,
  default_value = -1L,
  num_oov_buckets = 0L
)

Arguments

`...`	Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns.
`vocabulary_list`	An ordered iterable defining the vocabulary. Each feature is mapped to the index of its value (if present) in `vocabulary_list`. Must be castable to `dtype`.
`dtype`	The type of features. Only string and integer types are supported. If `NULL`, it will be inferred from `vocabulary_list`.
`default_value`	The value to use for values not in `vocabulary_list`.
`num_oov_buckets`	Non-negative integer, the number of out-of-vocabulary buckets. All out-of-vocabulary inputs will be assigned IDs in the range `⁠[vocabulary_size, vocabulary_size+num_oov_buckets)⁠` based on a hash of the input value. A positive `num_oov_buckets` can not be specified with `default_value`.

Details

Note that these values are independent of the default_value argument.

Value

A categorical column with in-memory vocabulary.

Raises

ValueError: if vocabulary_list is empty, or contains duplicate keys.
ValueError: if dtype is not integer or string.

Construct a Crossed Column

Description

Returns a column for performing crosses of categorical features. Crossed features will be hashed according to hash_bucket_size.

Usage

column_crossed(keys, hash_bucket_size, hash_key = NULL)
column_crossed(keys, hash_bucket_size, hash_key = NULL)

Arguments

keys

An iterable identifying the features to be crossed. Each element can be either:

string: Will use the corresponding feature which must be of string type.
categorical column: Will use the transformed tensor produced by this column. Does not support hashed categorical columns.

hash_bucket_size

The number of buckets (> 1).

hash_key

Optional: specify the hash_key that will be used by the FingerprintCat64 function to combine the crosses fingerprints on SparseCrossOp.

Value

A crossed column.

Raises

ValueError: If len(keys) < 2.
ValueError: If any of the keys is neither a string nor categorical column.
ValueError: If any of the keys is ⁠_HashedCategoricalColumn⁠.
ValueError: If hash_bucket_size < 1.

Construct a Dense Column

Description

Use this when your inputs are sparse, but you want to convert them to a dense representation (e.g., to feed to a DNN). Inputs must be a categorical column created by any of the ⁠column_categorical_*()⁠ functions.

Usage

column_embedding(
  categorical_column,
  dimension,
  combiner = "mean",
  initializer = NULL,
  ckpt_to_load_from = NULL,
  tensor_name_in_ckpt = NULL,
  max_norm = NULL,
  trainable = TRUE
)
column_embedding(
  categorical_column,
  dimension,
  combiner = "mean",
  initializer = NULL,
  ckpt_to_load_from = NULL,
  tensor_name_in_ckpt = NULL,
  max_norm = NULL,
  trainable = TRUE
)

Arguments

`categorical_column`	A categorical column created by a `⁠column_categorical_*()⁠` function. This column produces the sparse IDs that are inputs to the embedding lookup.
`dimension`	A positive integer, specifying dimension of the embedding.
`combiner`	A string specifying how to reduce if there are multiple entries in a single row. Currently `"mean"`, `"sqrtn"` and `"sum"` are supported, with `"mean"` the default. `"sqrtn"`' often achieves good accuracy, in particular with bag-of-words columns. Each of this can be thought as example level normalizations on the column.
`initializer`	A variable initializer function to be used in embedding variable initialization. If not specified, defaults to `tf$truncated_normal_initializer` with mean `0.0` and standard deviation `1 / sqrt(dimension)`.
`ckpt_to_load_from`	String representing checkpoint name/pattern from which to restore column weights. Required if `tensor_name_in_ckpt` is not `NULL`.
`tensor_name_in_ckpt`	Name of the `Tensor` in `ckpt_to_load_from` from which to restore the column weights. Required if `ckpt_to_load_from` is not `NULL`.
`max_norm`	If not `NULL`, embedding values are l2-normalized to this value.
`trainable`	Whether or not the embedding is trainable. Default is TRUE.

Value

A dense column that converts from sparse input.

Raises

ValueError: if dimension not > 0.
ValueError: if exactly one of ckpt_to_load_from and tensor_name_in_ckpt is specified.
ValueError: if initializer is specified and is not callable.

Represents Multi-Hot Representation of Given Categorical Column

Description

Used to wrap any ⁠column_categorical()*⁠ (e.g., to feed to DNN). Use column_embedding() if the inputs are sparse.

Usage

column_indicator(categorical_column)
column_indicator(categorical_column)

Arguments

categorical_column

A categorical column which is created by the ⁠column_categorical_with_*()⁠ or column_crossed() functions.

Value

An indicator column.

Construct a Real-Valued Column

Description

Construct a Real-Valued Column

Usage

column_numeric(
  ...,
  shape = c(1L),
  default_value = NULL,
  dtype = tf$float32,
  normalizer_fn = NULL
)
column_numeric(
  ...,
  shape = c(1L),
  default_value = NULL,
  dtype = tf$float32,
  normalizer_fn = NULL
)

Arguments

`...`	Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns.
`shape`	An integer vector that specifies the shape of the tensor. An integer can be given which means a single dimension tensor with given width. The tensor representing the column will have the shape of `batch_size + shape`.
`default_value`	A single value compatible with `dtype` or an iterable of values compatible with `dtype` which the column takes on during parsing if data is missing. A default value of `NULL` will cause `tf$parse_example` to fail if an example does not contain this column. If a single value is provided, the same value will be applied as the default value for every item. If an iterable of values is provided, the shape of the `default_value` should be equal to the given `shape`.
`dtype`	The types for values contained in the column. The default value is `tf$float32`. Must be a non-quantized, real integer or floating point type.
`normalizer_fn`	If not `NULL`, a function that can be used to normalize the value of the tensor after `default_value` is applied for parsing. Normalizer function takes the input `Tensor` as its argument, and returns the output tensor. (e.g. `⁠function(x) {(x - 3.0) / 4.2)}⁠`. Please note that even though the most common use case of this function is normalization, it can be used for any kind of Tensorflow transformations.

Value

A numeric column.

Raises

TypeError: if any dimension in shape is not an int
ValueError: if any dimension in shape is not a positive integer
TypeError: if default_value is an iterable but not compatible with shape
TypeError: if default_value is not compatible with dtype
ValueError: if dtype is not convertible to tf$float32

Establish a Feature Columns Selection Scope

Description

This helper function provides a set of names to be used by tidyselect helpers in e.g. feature_columns().

Usage

set_columns(columns)

with_columns(columns, expr)

scoped_columns(columns)
set_columns(columns)

with_columns(columns, expr)

scoped_columns(columns)

Arguments

`columns`	Either a named R object (whose names will be used to provide a selection context), or a character vector of such names.
`expr`	An R expression, to be evaluated with the selection context active.

Deep Neural Networks

Description

Create a deep neural network (DNN) estimator.

Usage

dnn_regressor(
  hidden_units,
  feature_columns,
  model_dir = NULL,
  label_dimension = 1L,
  weight_column = NULL,
  optimizer = "Adagrad",
  activation_fn = "relu",
  dropout = NULL,
  input_layer_partitioner = NULL,
  config = NULL
)

dnn_classifier(
  hidden_units,
  feature_columns,
  model_dir = NULL,
  n_classes = 2L,
  weight_column = NULL,
  label_vocabulary = NULL,
  optimizer = "Adagrad",
  activation_fn = "relu",
  dropout = NULL,
  input_layer_partitioner = NULL,
  config = NULL
)
dnn_regressor(
  hidden_units,
  feature_columns,
  model_dir = NULL,
  label_dimension = 1L,
  weight_column = NULL,
  optimizer = "Adagrad",
  activation_fn = "relu",
  dropout = NULL,
  input_layer_partitioner = NULL,
  config = NULL
)

dnn_classifier(
  hidden_units,
  feature_columns,
  model_dir = NULL,
  n_classes = 2L,
  weight_column = NULL,
  label_vocabulary = NULL,
  optimizer = "Adagrad",
  activation_fn = "relu",
  dropout = NULL,
  input_layer_partitioner = NULL,
  config = NULL
)

Arguments

`hidden_units`	An integer vector, indicating the number of hidden units in each layer. All layers are fully connected. For example, `c(64, 32)` means the first layer has 64 nodes, and the second layer has 32 nodes.
`feature_columns`	An R list containing all of the feature columns used by the model (typically, generated by `feature_columns()`).
`model_dir`	Directory to save the model parameters, graph, and so on. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
`label_dimension`	Number of regression targets per example. This is the size of the last dimension of the labels and logits `Tensor` objects (typically, these have shape `⁠[batch_size, label_dimension]⁠`).
`weight_column`	A string, or a numeric column created by `column_numeric()` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the `features` argument. If it is a numeric column, then the raw tensor is fetched by key `weight_column$key`, then `weight_column$normalizer_fn` is applied on it to get weight tensor.
`optimizer`	Either the name of the optimizer to be used when training the model, or a TensorFlow optimizer instance. Defaults to the Adagrad optimizer.
`activation_fn`	The activation function to apply to each layer. This can either be an actual activation function (e.g. `tf$nn$relu`), or the name of an activation function (e.g. `"relu"`). Defaults to the `"relu"` activation function. See https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/nn for documentation related to the set of activation functions available in TensorFlow.
`dropout`	When not `NULL`, the probability we will drop out a given coordinate.
`input_layer_partitioner`	An optional partitioner for the input layer. Defaults to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
`config`	A run configuration created by `run_config()`, used to configure the runtime settings.
`n_classes`	The number of label classes.
`label_vocabulary`	A list of strings represents possible label values. If given, labels must be string type and have any value in `label_vocabulary`. If it is not given, that means labels are already encoded as integer or float within `⁠[0, 1]⁠` for `n_classes == 2` and encoded as integer values in `⁠{0, 1,..., n_classes -1}⁠` for `n_classes > 2`. Also there will be errors if vocabulary is not provided and labels are string.

Linear Combined Deep Neural Networks

Description

Also known as wide-n-deep estimators, these are estimators for TensorFlow Linear and DNN joined models for regression.

Usage

dnn_linear_combined_regressor(
  model_dir = NULL,
  linear_feature_columns = NULL,
  linear_optimizer = "Ftrl",
  dnn_feature_columns = NULL,
  dnn_optimizer = "Adagrad",
  dnn_hidden_units = NULL,
  dnn_activation_fn = "relu",
  dnn_dropout = NULL,
  label_dimension = 1L,
  weight_column = NULL,
  input_layer_partitioner = NULL,
  config = NULL
)

dnn_linear_combined_classifier(
  model_dir = NULL,
  linear_feature_columns = NULL,
  linear_optimizer = "Ftrl",
  dnn_feature_columns = NULL,
  dnn_optimizer = "Adagrad",
  dnn_hidden_units = NULL,
  dnn_activation_fn = "relu",
  dnn_dropout = NULL,
  n_classes = 2L,
  weight_column = NULL,
  label_vocabulary = NULL,
  input_layer_partitioner = NULL,
  config = NULL
)
dnn_linear_combined_regressor(
  model_dir = NULL,
  linear_feature_columns = NULL,
  linear_optimizer = "Ftrl",
  dnn_feature_columns = NULL,
  dnn_optimizer = "Adagrad",
  dnn_hidden_units = NULL,
  dnn_activation_fn = "relu",
  dnn_dropout = NULL,
  label_dimension = 1L,
  weight_column = NULL,
  input_layer_partitioner = NULL,
  config = NULL
)

dnn_linear_combined_classifier(
  model_dir = NULL,
  linear_feature_columns = NULL,
  linear_optimizer = "Ftrl",
  dnn_feature_columns = NULL,
  dnn_optimizer = "Adagrad",
  dnn_hidden_units = NULL,
  dnn_activation_fn = "relu",
  dnn_dropout = NULL,
  n_classes = 2L,
  weight_column = NULL,
  label_vocabulary = NULL,
  input_layer_partitioner = NULL,
  config = NULL
)

Arguments

`model_dir`	Directory to save the model parameters, graph, and so on. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
`linear_feature_columns`	The feature columns used by linear (wide) part of the model.
`linear_optimizer`	Either the name of the optimizer to be used when training the model, or a TensorFlow optimizer instance. Defaults to the FTRL optimizer.
`dnn_feature_columns`	The feature columns used by the neural network (deep) part in the model.
`dnn_optimizer`	Either the name of the optimizer to be used when training the model, or a TensorFlow optimizer instance. Defaults to the Adagrad optimizer.
`dnn_hidden_units`	An integer vector, indicating the number of hidden units in each layer. All layers are fully connected. For example, `c(64, 32)` means the first layer has 64 nodes, and the second layer has 32 nodes.
`dnn_activation_fn`	The activation function to apply to each layer. This can either be an actual activation function (e.g. `tf$nn$relu`), or the name of an activation function (e.g. `"relu"`). Defaults to the `"relu"` activation function. See https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/nn for documentation related to the set of activation functions available in TensorFlow.
`dnn_dropout`	When not `NULL`, the probability we will drop out a given coordinate.
`label_dimension`	Number of regression targets per example. This is the size of the last dimension of the labels and logits `Tensor` objects (typically, these have shape `⁠[batch_size, label_dimension]⁠`).
`weight_column`	A string, or a numeric column created by `column_numeric()` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the `features` argument. If it is a numeric column, then the raw tensor is fetched by key `weight_column$key`, then `weight_column$normalizer_fn` is applied on it to get weight tensor.
`input_layer_partitioner`	An optional partitioner for the input layer. Defaults to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
`config`	A run configuration created by `run_config()`, used to configure the runtime settings.
`n_classes`	The number of label classes.
`label_vocabulary`	A list of strings represents possible label values. If given, labels must be string type and have any value in `label_vocabulary`. If it is not given, that means labels are already encoded as integer or float within `⁠[0, 1]⁠` for `n_classes == 2` and encoded as integer values in `⁠{0, 1,..., n_classes -1}⁠` for `n_classes > 2`. Also there will be errors if vocabulary is not provided and labels are string.

Construct a Custom Estimator

Description

Construct a custom estimator, to be used to train and evaluate TensorFlow models.

Usage

estimator(
  model_fn,
  model_dir = NULL,
  config = NULL,
  params = NULL,
  class = NULL
)
estimator(
  model_fn,
  model_dir = NULL,
  config = NULL,
  params = NULL,
  class = NULL
)

Arguments

`model_fn`	The model function. See Model Function for details on the structure of a model function.
`model_dir`	Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model. If `NULL`, the `model_dir` in `config` will be used if set. If both are set, they must be same. If both are `NULL`, a temporary directory will be used.
`config`	Configuration object.
`params`	List of hyper parameters that will be passed into `model_fn`. Keys are names of parameters, values are basic python types.
`class`	An optional set of R classes to add to the generated object.

Details

The Estimator object wraps a model which is specified by a model_fn, which, given inputs and a number of other parameters, returns the operations necessary to perform training, evaluation, and prediction.

All outputs (checkpoints, event files, etc.) are written to model_dir, or a subdirectory thereof. If model_dir is not set, a temporary directory is used.

The config argument can be used to passed run configuration object containing information about the execution environment. It is passed on to the model_fn, if the model_fn has a parameter named "config" (and input functions in the same manner). If the config parameter is not passed, it is instantiated by estimator(). Not passing config means that defaults useful for local execution are used. estimator() makes config available to the model (for instance, to allow specialization based on the number of workers available), and also uses some of its fields to control internals, especially regarding checkpointing.

The params argument contains hyperparameters. It is passed to the model_fn, if the model_fn has a parameter named "params", and to the input functions in the same manner. estimator() only passes params along, it does not inspect it. The structure of params is therefore entirely up to the developer.

None of estimator's methods can be overridden in subclasses (its constructor enforces this). Subclasses should use model_fn to configure the base class, and may add methods implementing specialized functionality.

Model Functions

The model_fn should be an R function of the form:

function(features, labels, mode, params) {
    # 1. Configure the model via TensorFlow operations.
    # 2. Define the loss function for training and evaluation.
    # 3. Define the training optimizer.
    # 4. Define how predictions should be produced.
    # 5. Return the result as an `estimator_spec()` object.
    estimator_spec(mode, predictions, loss, train_op, eval_metric_ops)
}

The model function's inputs are defined as follows:

`features`	The feature tensor(s).
`labels`	The label tensor(s).
`mode`	The current training mode ("train", "eval", "infer"). These can be accessed through the `mode_keys()` object.
`params`	An optional list of hyperparameters, as received through the `estimator()` constructor.

See estimator_spec() for more details as to how the estimator specification should be constructed, and https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/estimator/Estimator for more information as to how the model function should be constructed.

Define an Estimator Specification

Description

Define the estimator specification, used as part of the model_fn defined with custom estimators created by estimator(). See estimator() for more details.

Usage

estimator_spec(
  mode,
  predictions = NULL,
  loss = NULL,
  train_op = NULL,
  eval_metric_ops = NULL,
  training_hooks = NULL,
  evaluation_hooks = NULL,
  prediction_hooks = NULL,
  training_chief_hooks = NULL,
  ...
)
estimator_spec(
  mode,
  predictions = NULL,
  loss = NULL,
  train_op = NULL,
  eval_metric_ops = NULL,
  training_hooks = NULL,
  evaluation_hooks = NULL,
  prediction_hooks = NULL,
  training_chief_hooks = NULL,
  ...
)

Arguments

`mode`	A key that specifies whether we are performing training (`"train"`), evaluation (`"eval"`), or prediction (`"infer"`). These values can also be accessed through the `mode_keys()` object.
`predictions`	The prediction tensor(s).
`loss`	The training loss tensor. Must be either scalar, or with shape `c(1)`.
`train_op`	The training operation – typically, a call to `optimizer$minimize(...)`, depending on the type of optimizer used during training.
`eval_metric_ops`	A list of metrics to be computed as part of evaluation. This should be a named list, mapping metric names (e.g. `"rmse"`) to the operation that computes the associated metric (e.g. `tf$metrics$root_mean_squared_error(...)`). These metric operations should be evaluated without any impact on state (typically is a pure computation results based on variables). For example, it should not trigger the update ops or requires any input fetching.
`training_hooks`	(Available since TensorFlow v1.4) A list of session run hooks to run on all workers during training.
`evaluation_hooks`	(Available since TensorFlow v1.4) A list of session run hooks to run during evaluation.
`prediction_hooks`	(Available since TensorFlow v1.7) A list of session run hooks to run during prediciton.
`training_chief_hooks`	(Available since TensorFlow v1.4) A list of session run hooks to run on chief worker during training.
`...`	Other optional (named) arguments, to be passed to the `EstimatorSpec` constructor.

Base Documentation for Canned Estimators

Description

Base Documentation for Canned Estimators

Arguments

`object`	A TensorFlow estimator.
`feature_columns`	An R list containing all of the feature columns used by the model (typically, generated by `feature_columns()`).
`model_dir`	Directory to save the model parameters, graph, and so on. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
`label_dimension`	Number of regression targets per example. This is the size of the last dimension of the labels and logits `Tensor` objects (typically, these have shape `⁠[batch_size, label_dimension]⁠`).
`label_vocabulary`	A list of strings represents possible label values. If given, labels must be string type and have any value in `label_vocabulary`. If it is not given, that means labels are already encoded as integer or float within `⁠[0, 1]⁠` for `n_classes == 2` and encoded as integer values in `⁠{0, 1,..., n_classes -1}⁠` for `n_classes > 2`. Also there will be errors if vocabulary is not provided and labels are string.
`weight_column`	A string, or a numeric column created by `column_numeric()` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the `features` argument. If it is a numeric column, then the raw tensor is fetched by key `weight_column$key`, then `weight_column$normalizer_fn` is applied on it to get weight tensor.
`n_classes`	The number of label classes.
`config`	A run configuration created by `run_config()`, used to configure the runtime settings.
`input_layer_partitioner`	An optional partitioner for the input layer. Defaults to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
`partitioner`	An optional partitioner for the input layer.

Configuration for the eval component of `train_and_evaluate`

Description

EvalSpec combines details of evaluation of the trained model as well as its export. Evaluation consists of computing metrics to judge the performance of the trained model. Export writes out the trained model on to external storage.

Usage

eval_spec(
  input_fn,
  steps = 100,
  name = NULL,
  hooks = NULL,
  exporters = NULL,
  start_delay_secs = 120,
  throttle_secs = 600
)
eval_spec(
  input_fn,
  steps = 100,
  name = NULL,
  hooks = NULL,
  exporters = NULL,
  start_delay_secs = 120,
  throttle_secs = 600
)

Arguments

`input_fn`	Evaluation input function returning a tuple of: features - `Tensor` or dictionary of string feature name to `Tensor`. labels - `Tensor` or dictionary of `Tensor` with labels.
`steps`	Positive number of steps for which to evaluate model. If `NULL`, evaluates until `input_fn` raises an end-of-input exception.
`name`	Name of the evaluation if user needs to run multiple evaluations on different data sets. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.
`hooks`	List of session run hooks to run during evaluation.
`exporters`	List of `Exporter`s, or a single one, or `NULL`. `exporters` will be invoked after each evaluation.
`start_delay_secs`	Start evaluating after waiting for this many seconds.
`throttle_secs`	Do not re-evaluate unless the last evaluation was started at least this many seconds ago. Of course, evaluation does not occur if no new checkpoints are available, hence, this is the minimum.

Evaluate an Estimator

Description

Evaluate an estimator on input data provided by an input_fn().

Usage

## S3 method for class 'tf_estimator'
evaluate(
  object,
  input_fn,
  steps = NULL,
  checkpoint_path = NULL,
  name = NULL,
  hooks = NULL,
  simplify = TRUE,
  ...
)
## S3 method for class 'tf_estimator'
evaluate(
  object,
  input_fn,
  steps = NULL,
  checkpoint_path = NULL,
  name = NULL,
  hooks = NULL,
  simplify = TRUE,
  ...
)

Arguments

`object`	A TensorFlow estimator.
`input_fn`	An input function, typically generated by the `input_fn()` helper function.
`steps`	The number of steps for which the model should be evaluated on this particular `evaluate()` invocation. If `NULL` (the default), this function will either evaluate forever, or until the supplied `input_fn()` has provided all available data.
`checkpoint_path`	The path to a specific model checkpoint to be used for prediction. If `NULL` (the default), the latest checkpoint in `model_dir` is used.
`name`	Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.
`hooks`	A list of R functions, to be used as callbacks inside the training loop. By default, `hook_history_saver(every_n_step = 10)` and `hook_progress_bar()` will be attached if not provided to save the metrics history and create the progress bar.
`simplify`	Whether to simplify evaluation results into a `tibble`, as opposed to a list. Defaults to `TRUE`.
`...`	Optional arguments passed on to the estimator's `evaluate()` method.

Details

For each step, this method will call input_fn() to produce a single batch of data. Evaluation continues until:

steps batches are processed, or
The input_fn() is exhausted of data.

Value

An R list of evaluation metrics.

Construct an Experiment

Description

Construct an experiment object.

Usage

experiment(object, ...)
experiment(object, ...)

Arguments

`object`	An R object.
`...`	Optional arguments passed on to implementing methods.

Save an Estimator

Description

Save an estimator (alongside its weights) to the directory export_dir_base.

Usage

## S3 method for class 'tf_estimator'
export_savedmodel(
  object,
  export_dir_base,
  serving_input_receiver_fn = NULL,
  assets_extra = NULL,
  as_text = FALSE,
  checkpoint_path = NULL,
  overwrite = TRUE,
  versioned = !overwrite,
  ...
)
## S3 method for class 'tf_estimator'
export_savedmodel(
  object,
  export_dir_base,
  serving_input_receiver_fn = NULL,
  assets_extra = NULL,
  as_text = FALSE,
  checkpoint_path = NULL,
  overwrite = TRUE,
  versioned = !overwrite,
  ...
)

Arguments

`object`	A TensorFlow estimator.
`export_dir_base`	A string containing a directory in which to export the SavedModel.
`serving_input_receiver_fn`	A function that takes no argument and returns a `ServingInputReceiver`. Required for custom models.
`assets_extra`	A dict specifying how to populate the assets.extra directory within the exported SavedModel, or `NULL` if no extra assets are needed.
`as_text`	whether to write the SavedModel proto in text format.
`checkpoint_path`	The checkpoint path to export. If `NULL` (the default), the most recent checkpoint found within the model directory is chosen.
`overwrite`	Should the `export_dir` directory be overwritten?
`versioned`	Should the model be exported under a versioned subdirectory?
`...`	Optional arguments passed on to the estimator's `export_savedmodel()` method.

Details

This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensors, and then calling this Estimator's model_fn to generate the model graph based on those features. It restores the given checkpoint (or, lacking that, the most recent checkpoint) into this graph in a fresh session. Finally it creates a timestamped export directory below the given export_dir_base, and writes a SavedModel into it containing a single MetaGraphDef saved from this session. The exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn. Extra assets may be written into the SavedModel via the extra_assets argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.

Value

The path to the exported directory, as a string.

Raises

ValueError: if no serving_input_receiver_fn is provided, no export_outputs are provided, or no checkpoint can be found.

Feature Columns

Description

Constructors for feature columns. A feature column defines the expected 'shape' of an input Tensor.

Usage

feature_columns(..., names = NULL)
feature_columns(..., names = NULL)

Arguments

`...`	One or more feature column definitions. The tidyselect package is used to power generation of feature columns.
`names`	Available feature names (for selection / pattern matching) as a character vector (or R object that implements `names()` or `colnames()`).

Standard Names to Use for Graph Collections

Description

The standard library uses various well-known names to collect and retrieve values associated with a graph.

Usage

graph_keys()
graph_keys()

Details

For example, the tf$Optimizer subclasses default to optimizing the variables collected undergraph_keys()$TRAINABLE_VARIABLES if NULL is specified, but it is also possible to pass an explicit list of variables.

The following standard keys are defined:

GLOBAL_VARIABLES: the default collection of Variable objects, shared across distributed environment (model variables are subset of these). See tf$global_variables for more details. Commonly, all TRAINABLE_VARIABLES variables will be in MODEL_VARIABLES, and all MODEL_VARIABLES variables will be in GLOBAL_VARIABLES.
LOCAL_VARIABLES: the subset of Variable objects that are local to each machine. Usually used for temporarily variables, like counters. Note: use tf$contrib$framework$local_variable to add to this collection.
MODEL_VARIABLES: the subset of Variable objects that are used in the model for inference (feed forward). Note: use tf$contrib$framework$model_variable to add to this collection.
TRAINABLE_VARIABLES: the subset of Variable objects that will be trained by an optimizer. See tf$trainable_variables for more details.
SUMMARIES: the summary Tensor objects that have been created in the graph. See tf$summary$merge_all for more details.
QUEUE_RUNNERS: the QueueRunner objects that are used to produce input for a computation. See tf$train$start_queue_runners for more details.
MOVING_AVERAGE_VARIABLES: the subset of Variable objects that will also keep moving averages. See tf$moving_average_variables for more details.
REGULARIZATION_LOSSES: regularization losses collected during graph construction. The following standard keys are defined, but their collections are not automatically populated as many of the others are:
- WEIGHTS
- BIASES
- ACTIVATIONS

Examples

## Not run: 
graph_keys()
graph_keys()$LOSSES

## End(Not run)

## Not run: 
graph_keys()
graph_keys()$LOSSES

## End(Not run)

Saves Checkpoints Every N Steps or Seconds

Description

Saves Checkpoints Every N Steps or Seconds

Usage

hook_checkpoint_saver(
  checkpoint_dir,
  save_secs = NULL,
  save_steps = NULL,
  saver = NULL,
  checkpoint_basename = "model.ckpt",
  scaffold = NULL,
  listeners = NULL
)
hook_checkpoint_saver(
  checkpoint_dir,
  save_secs = NULL,
  save_steps = NULL,
  saver = NULL,
  checkpoint_basename = "model.ckpt",
  scaffold = NULL,
  listeners = NULL
)

Arguments

`checkpoint_dir`	The base directory for the checkpoint files.
`save_secs`	An integer, indicating saving checkpoints every N secs.
`save_steps`	An integer, indicating saving checkpoints every N steps.
`saver`	A saver object, used for saving.
`checkpoint_basename`	The base name for the checkpoint files.
`scaffold`	A scaffold, used to get saver object.
`listeners`	List of checkpoint saver listener subclass instances, used for callbacks that run immediately after the corresponding `hook_checkpoint_saver` callbacks, only in steps where `⁠the hook_checkpoint_saver⁠` was triggered.

Delay Execution until Global Step Reaches to `wait_until_step`.

Description

This hook delays execution until global step reaches to wait_until_step. It is used to gradually start workers in distributed settings. One example usage would be setting wait_until_step=int(K*log(task_id+1)) assuming that task_id=0 is the chief.

Usage

hook_global_step_waiter(wait_until_step)
hook_global_step_waiter(wait_until_step)

Arguments

wait_until_step

An integer indicating that until which global step should we wait.

A Custom Run Hook for Saving Metrics History

Description

This hook allows users to save the metrics history produced during training or evaluation in a specified frequency.

Usage

hook_history_saver(every_n_step = 10)
hook_history_saver(every_n_step = 10)

Arguments

every_n_step

Save the metrics every N steps

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

Description

The tensors will be printed to the log, with INFO severity.

Usage

hook_logging_tensor(
  tensors,
  every_n_iter = NULL,
  every_n_secs = NULL,
  formatter = NULL,
  at_end = FALSE
)
hook_logging_tensor(
  tensors,
  every_n_iter = NULL,
  every_n_secs = NULL,
  formatter = NULL,
  at_end = FALSE
)

Arguments

`tensors`	A list that maps string-valued tags to tensors/tensor names.
`every_n_iter`	An integer value, indicating the values of `tensors` will be printed once every N local steps taken on the current worker.
`every_n_secs`	An integer or float value, indicating the values of `tensors` will be printed once every N seconds. Exactly one of `every_n_iter` and `every_n_secs` should be provided.
`formatter`	A function that takes `list(tag = tensor)` and returns a string. If `NULL` uses default printing all tensors.
`at_end`	A boolean value specifying whether to print the values of `tensors` at the end of the run.

Details

Note that if at_end is TRUE, tensors should not include any tensor whose evaluation produces a side effect such as consuming additional inputs.

NaN Loss Monitor

Description

Monitors loss and stops training if loss is NaN. Can either fail with exception or just stop training.

Usage

hook_nan_tensor(loss_tensor, fail_on_nan_loss = TRUE)
hook_nan_tensor(loss_tensor, fail_on_nan_loss = TRUE)

Arguments

`loss_tensor`	The loss tensor.
`fail_on_nan_loss`	A boolean indicating whether to raise exception when loss is NaN.

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

Description

This hook creates a progress bar that creates and updates the progress bar during training or evaluation.

Usage

hook_progress_bar()
hook_progress_bar()

Steps per Second Monitor

Description

Steps per Second Monitor

Usage

hook_step_counter(
  every_n_steps = 100,
  every_n_secs = NULL,
  output_dir = NULL,
  summary_writer = NULL
)
hook_step_counter(
  every_n_steps = 100,
  every_n_secs = NULL,
  output_dir = NULL,
  summary_writer = NULL
)

Arguments

`every_n_steps`	Run this counter every N steps
`every_n_secs`	Run this counter every N seconds
`output_dir`	The output directory
`summary_writer`	The summary writer

Monitor to Request Stop at a Specified Step

Description

Monitor to Request Stop at a Specified Step

Usage

hook_stop_at_step(num_steps = NULL, last_step = NULL)
hook_stop_at_step(num_steps = NULL, last_step = NULL)

Arguments

`num_steps`	Number of steps to execute.
`last_step`	Step after which to stop.

Saves Summaries Every N Steps

Description

Saves Summaries Every N Steps

Usage

hook_summary_saver(
  save_steps = NULL,
  save_secs = NULL,
  output_dir = NULL,
  summary_writer = NULL,
  scaffold = NULL,
  summary_op = NULL
)
hook_summary_saver(
  save_steps = NULL,
  save_secs = NULL,
  output_dir = NULL,
  summary_writer = NULL,
  scaffold = NULL,
  summary_op = NULL
)

Arguments

`save_steps`	An integer indicating saving summaries every N steps. Exactly one of `save_secs` and `save_steps` should be set.
`save_secs`	An integer indicating saving summaries every N seconds.
`output_dir`	The directory to save the summaries to. Only used if no `summary_writer` is supplied.
`summary_writer`	The summary writer. If `NULL` and an `output_dir` was passed, one will be created accordingly.
`scaffold`	A scaffold to get summary_op if it's not provided.
`summary_op`	A tensor of type `tf$string` containing the serialized summary protocol buffer or a list of tensors. They are most likely an output by TensorFlow summary methods like `tf$summary$scalar` or `tf$summary$merge_all`. It can be passed in as one tensor; if more than one, they must be passed in as a list.

Construct an Input Function

Description

This function constructs input function from various types of input used to feed different TensorFlow estimators.

Usage

input_fn(object, ...)

## Default S3 method:
input_fn(object, ...)

## S3 method for class 'formula'
input_fn(object, data, ...)

## S3 method for class 'data.frame'
input_fn(
  object,
  features,
  response = NULL,
  batch_size = 128,
  shuffle = "auto",
  num_epochs = 1,
  queue_capacity = 1000,
  num_threads = 1,
  ...
)

## S3 method for class 'list'
input_fn(
  object,
  features,
  response = NULL,
  batch_size = 128,
  shuffle = "auto",
  num_epochs = 1,
  queue_capacity = 1000,
  num_threads = 1,
  ...
)

## S3 method for class 'matrix'
input_fn(object, ...)
input_fn(object, ...)

## Default S3 method:
input_fn(object, ...)

## S3 method for class 'formula'
input_fn(object, data, ...)

## S3 method for class 'data.frame'
input_fn(
  object,
  features,
  response = NULL,
  batch_size = 128,
  shuffle = "auto",
  num_epochs = 1,
  queue_capacity = 1000,
  num_threads = 1,
  ...
)

## S3 method for class 'list'
input_fn(
  object,
  features,
  response = NULL,
  batch_size = 128,
  shuffle = "auto",
  num_epochs = 1,
  queue_capacity = 1000,
  num_threads = 1,
  ...
)

## S3 method for class 'matrix'
input_fn(object, ...)

Arguments

`object`, `data`	An 'input source' – either a data set (e.g. an R `data.frame`), or another kind of object that can provide the data required for training.
`...`	Optional arguments passed on to implementing submethods.
`features`	The names of feature variables to be used.
`response`	The name of the response variable.
`batch_size`	The batch size.
`shuffle`	Whether to shuffle the queue. When `"auto"` (the default), shuffling will be performed except when this input function is called by a `predict()` method.
`num_epochs`	The number of epochs to iterate over data.
`queue_capacity`	The size of queue to accumulate.
`num_threads`	The number of threads used for reading and enqueueing. In order to have predictable and repeatable order of reading and enqueueing, such as in prediction and evaluation mode, `num_threads` should be 1.

Details

For list objects, this method is particularly useful when constructing dynamic length of inputs for models like recurrent neural networks. Note that some arguments are not available yet for input_fn applied to list objects. See S3 method signatures below for more details.

Examples

## Not run: 
# Construct the input function through formula interface
input_fn1 <- input_fn(mpg ~ drat + cyl, mtcars)

## End(Not run)

## Not run: 
# Construct the input function from a data.frame object
input_fn1 <- input_fn(mtcars, response = mpg, features = c(drat, cyl))

## End(Not run)

## Not run: 
# Construct the input function from a list object
input_fn1 <- input_fn(
   object = list(
     feature1 = list(
       list(list(1), list(2), list(3)),
       list(list(4), list(5), list(6))),
     feature2 = list(
       list(list(7), list(8), list(9)),
       list(list(10), list(11), list(12))),
     response = list(
       list(1, 2, 3), list(4, 5, 6))),
   features = c("feature1", "feature2"),
   response = "response",
   batch_size = 10L)

## End(Not run)

## Not run: 
# Construct the input function through formula interface
input_fn1 <- input_fn(mpg ~ drat + cyl, mtcars)

## End(Not run)

## Not run: 
# Construct the input function from a data.frame object
input_fn1 <- input_fn(mtcars, response = mpg, features = c(drat, cyl))

## End(Not run)

## Not run: 
# Construct the input function from a list object
input_fn1 <- input_fn(
   object = list(
     feature1 = list(
       list(list(1), list(2), list(3)),
       list(list(4), list(5), list(6))),
     feature2 = list(
       list(list(7), list(8), list(9)),
       list(list(10), list(11), list(12))),
     response = list(
       list(1, 2, 3), list(4, 5, 6))),
   features = c("feature1", "feature2"),
   response = "response",
   batch_size = 10L)

## End(Not run)

Construct an Input Layer

Description

Returns a dense tensor as input layer based on given feature_columns. At the first layer of the model, this column oriented data should be converted to a single tensor.

Usage

input_layer(
  features,
  feature_columns,
  weight_collections = NULL,
  trainable = TRUE
)
input_layer(
  features,
  feature_columns,
  weight_collections = NULL,
  trainable = TRUE
)

Arguments

`features`	A mapping from key to tensors. Feature columns look up via these keys. For example `column_numeric('price')` will look at 'price' key in this dict. Values can be a sparse tensor or tensor depends on corresponding feature column.
`feature_columns`	An iterable containing the FeatureColumns to use as inputs to your model. All items should be instances of classes derived from a dense column such as `column_numeric()`, `column_embedding()`, `column_bucketized()`, `column_indicator()`. If you have categorical features, you can wrap them with an `column_embedding()` or `column_indicator()`.
`weight_collections`	A list of collection names to which the Variable will be added. Note that, variables will also be added to collections `graph_keys()$GLOBAL_VARIABLES` and `graph_keys()$MODEL_VARIABLES`.
`trainable`	If `TRUE` also add the variable to the graph collection `graph_keys()$TRAINABLE_VARIABLES` (see `tf$Variable`).

Value

A tensor which represents input layer of a model. Its shape is (batch_size, first_layer_dimension) and its dtype is float32. first_layer_dimension is determined based on given feature_columns.

Raises

ValueError: if an item in feature_columns is not a dense column.

Keras Estimators

Description

Create an Estimator from a compiled Keras model

Usage

keras_model_to_estimator(
  keras_model = NULL,
  keras_model_path = NULL,
  custom_objects = NULL,
  model_dir = NULL,
  config = NULL
)
keras_model_to_estimator(
  keras_model = NULL,
  keras_model_path = NULL,
  custom_objects = NULL,
  model_dir = NULL,
  config = NULL
)

Arguments

`keras_model`	A keras model.
`keras_model_path`	Directory to a keras model on disk.
`custom_objects`	Dictionary for custom objects.
`model_dir`	Directory to save Estimator model parameters, graph and etc.
`config`	Configuration object.

Get the Latest Checkpoint in a Checkpoint Directory

Description

Get the Latest Checkpoint in a Checkpoint Directory

Usage

latest_checkpoint(checkpoint_dir, ...)
latest_checkpoint(checkpoint_dir, ...)

Arguments

`checkpoint_dir`	The path to the checkpoint directory.
`...`	Optional arguments passed on to `latest_checkpoint()`.

Construct a Linear Estimator

Description

Construct a linear model, which can be used to predict a continuous outcome (in the case of linear_regressor()) or a categorical outcome (in the case of linear_classifier()).

Usage

linear_regressor(
  feature_columns,
  model_dir = NULL,
  label_dimension = 1L,
  weight_column = NULL,
  optimizer = "Ftrl",
  config = NULL,
  partitioner = NULL
)

linear_classifier(
  feature_columns,
  model_dir = NULL,
  n_classes = 2L,
  weight_column = NULL,
  label_vocabulary = NULL,
  optimizer = "Ftrl",
  config = NULL,
  partitioner = NULL
)
linear_regressor(
  feature_columns,
  model_dir = NULL,
  label_dimension = 1L,
  weight_column = NULL,
  optimizer = "Ftrl",
  config = NULL,
  partitioner = NULL
)

linear_classifier(
  feature_columns,
  model_dir = NULL,
  n_classes = 2L,
  weight_column = NULL,
  label_vocabulary = NULL,
  optimizer = "Ftrl",
  config = NULL,
  partitioner = NULL
)

Arguments

`feature_columns`	An R list containing all of the feature columns used by the model (typically, generated by `feature_columns()`).
`model_dir`	Directory to save the model parameters, graph, and so on. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
`label_dimension`	Number of regression targets per example. This is the size of the last dimension of the labels and logits `Tensor` objects (typically, these have shape `⁠[batch_size, label_dimension]⁠`).
`weight_column`	A string, or a numeric column created by `column_numeric()` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the `features` argument. If it is a numeric column, then the raw tensor is fetched by key `weight_column$key`, then `weight_column$normalizer_fn` is applied on it to get weight tensor.
`optimizer`	Either the name of the optimizer to be used when training the model, or a TensorFlow optimizer instance. Defaults to the FTRL optimizer.
`config`	A run configuration created by `run_config()`, used to configure the runtime settings.
`partitioner`	An optional partitioner for the input layer.
`n_classes`	The number of label classes.
`label_vocabulary`	A list of strings represents possible label values. If given, labels must be string type and have any value in `label_vocabulary`. If it is not given, that means labels are already encoded as integer or float within `⁠[0, 1]⁠` for `n_classes == 2` and encoded as integer values in `⁠{0, 1,..., n_classes -1}⁠` for `n_classes > 2`. Also there will be errors if vocabulary is not provided and labels are string.

Canonical Metric Keys

Description

The canonical set of keys that can be used to access metrics from canned estimators.

Usage

metric_keys()
metric_keys()

Examples

## Not run: 
metrics <- metric_keys()

# Get the available keys
metrics

metrics$ACCURACY

## End(Not run)

## Not run: 
metrics <- metric_keys()

# Get the available keys
metrics

metrics$ACCURACY

## End(Not run)

Canonical Mode Keys

Description

The names for different possible modes for an estimator. The following standard keys are defined:

Usage

mode_keys()
mode_keys()

Details

`TRAIN`	Training mode.
`EVAL`	Evaluation mode.
`PREDICT`	Prediction / inference mode.

Examples

## Not run: 
modes <- mode_keys()
modes$TRAIN

## End(Not run)

## Not run: 
modes <- mode_keys()
modes$TRAIN

## End(Not run)

Model directory

Description

Get the directory where a model's artifacts are stored.

Usage

model_dir(object, ...)
model_dir(object, ...)

Arguments

`object`	Model object
`...`	Unused

Construct Input Function Containing Python Dictionaries of Numpy Arrays

Description

This returns a function outputting features and target based on the dict of numpy arrays. The dict features has the same keys as the x.

Usage

numpy_input_fn(
  x,
  y = NULL,
  batch_size = 128,
  num_epochs = 1,
  shuffle = NULL,
  queue_capacity = 1000,
  num_threads = 1
)
numpy_input_fn(
  x,
  y = NULL,
  batch_size = 128,
  num_epochs = 1,
  shuffle = NULL,
  queue_capacity = 1000,
  num_threads = 1
)

Arguments

`x`	dict of numpy array object.
`y`	numpy array object. `NULL` if absent.
`batch_size`	Integer, size of batches to return.
`num_epochs`	Integer, number of epochs to iterate over data. If `NULL` will run forever.
`shuffle`	Boolean, if `TRUE` shuffles the queue. Avoid shuffle at prediction time.
`queue_capacity`	Integer, size of queue to accumulate.
`num_threads`	Integer, number of threads used for reading and enqueueing. In order to have predicted and repeatable order of reading and enqueueing, such as in prediction and evaluation mode, `num_threads` should be 1. #'

Details

Note that this function is still experimental and should only be used if necessary, e.g. feed in data that's dictionary of numpy arrays.

Raises

ValueError: if the shape of y mismatches the shape of values in x (i.e., values in x have same shape). TypeError: x is not a dict or shuffle is not bool.

Plot training history

Description

Plots metrics recorded during training.

Usage

## S3 method for class 'tf_estimator_history'
plot(
  x,
  y,
  metrics = NULL,
  method = c("auto", "ggplot2", "base"),
  smooth = getOption("tf.estimator.plot.history.smooth", TRUE),
  theme_bw = getOption("tf.estimator.plot.history.theme_bw", FALSE),
  ...
)
## S3 method for class 'tf_estimator_history'
plot(
  x,
  y,
  metrics = NULL,
  method = c("auto", "ggplot2", "base"),
  smooth = getOption("tf.estimator.plot.history.smooth", TRUE),
  theme_bw = getOption("tf.estimator.plot.history.theme_bw", FALSE),
  ...
)

Arguments

`x`	Training history object returned from `train()`.
`y`	Unused.
`metrics`	One or more metrics to plot (e.g. `c('total_losses', 'mean_losses')`). Defaults to plotting all captured metrics.
`method`	Method to use for plotting. The default "auto" will use ggplot2 if available, and otherwise will use base graphics.
`smooth`	Whether a loess smooth should be added to the plot, only available for the `ggplot2` method. If the number of data points is smaller than ten, it is forced to false.
`theme_bw`	Use `ggplot2::theme_bw()` to plot the history in black and white.
`...`	Additional parameters to pass to the `plot()` method.

Generate Predictions with an Estimator

Description

Generate predicted labels / values for input data provided by input_fn().

Usage

## S3 method for class 'tf_estimator'
predict(
  object,
  input_fn,
  checkpoint_path = NULL,
  predict_keys = c("predictions", "classes", "class_ids", "logistic", "logits",
    "probabilities"),
  hooks = NULL,
  as_iterable = FALSE,
  simplify = TRUE,
  yield_single_examples = TRUE,
  ...
)
## S3 method for class 'tf_estimator'
predict(
  object,
  input_fn,
  checkpoint_path = NULL,
  predict_keys = c("predictions", "classes", "class_ids", "logistic", "logits",
    "probabilities"),
  hooks = NULL,
  as_iterable = FALSE,
  simplify = TRUE,
  yield_single_examples = TRUE,
  ...
)

Arguments

`object`	A TensorFlow estimator.
`input_fn`	An input function, typically generated by the `input_fn()` helper function.
`checkpoint_path`	The path to a specific model checkpoint to be used for prediction. If `NULL` (the default), the latest checkpoint in `model_dir` is used.
`predict_keys`	The types of predictions that should be produced, as an R list. When this argument is not specified (the default), all possible predicted values will be returned.
`hooks`	A list of R functions, to be used as callbacks inside the training loop. By default, `hook_history_saver(every_n_step = 10)` and `hook_progress_bar()` will be attached if not provided to save the metrics history and create the progress bar.
`as_iterable`	Boolean; should a raw Python generator be returned? When `FALSE` (the default), the predicted values will be consumed from the generator and returned as an R object.
`simplify`	Whether to simplify prediction results into a `tibble`, as opposed to a list. Defaults to `TRUE`.
`yield_single_examples`	(Available since TensorFlow v1.7) If `FALSE`, yields the whole batch as returned by the `model_fn` instead of decomposing the batch into individual elements. This is useful if `model_fn` returns some tensors with first dimension not equal to the batch size.
`...`	Optional arguments passed on to the estimator's `predict()` method.

Yields

Evaluated values of predictions tensors.

Raises

ValueError: Could not find a trained model in model_dir. ValueError: if batch length of predictions are not same. ValueError: If there is a conflict between predict_keys and predictions. For example if predict_keys is not NULL but EstimatorSpec.predictions is not a dict.

Canonical Model Prediction Keys

Description

The canonical set of keys used for models and estimators that provide different types of predicted values through their predict() method.

Usage

prediction_keys()
prediction_keys()

Examples

 ## Not run: 
keys <- prediction_keys()

# Get the available keys
keys

# Key for retrieving probabilities from prediction values
keys$PROBABILITIES

## End(Not run)

## Not run: 
keys <- prediction_keys()

# Get the available keys
keys

# Key for retrieving probabilities from prediction values
keys$PROBABILITIES

## End(Not run)

Generates Parsing Spec for TensorFlow Example to be Used with Regressors

Description

If users keep data in tf$Example format, they need to call tf$parse_example with a proper feature spec. There are two main things that this utility helps:

Users need to combine parsing spec of features with labels and weights (if any) since they are all parsed from same tf$Example instance. This utility combines these specs.
It is difficult to map expected label by a regressor such as dnn_regressor to corresponding tf$parse_example spec. This utility encodes it by getting related information from users (key, dtype).

Usage

regressor_parse_example_spec(
  feature_columns,
  label_key,
  label_dtype = tf$float32,
  label_default = NULL,
  label_dimension = 1L,
  weight_column = NULL
)
regressor_parse_example_spec(
  feature_columns,
  label_key,
  label_dtype = tf$float32,
  label_default = NULL,
  label_dimension = 1L,
  weight_column = NULL
)

Arguments

`feature_columns`	An iterable containing all feature columns. All items should be instances of classes derived from `⁠_FeatureColumn⁠`.
`label_key`	A string identifying the label. It means `tf$Example` stores labels with this key.
`label_dtype`	A `tf$dtype` identifies the type of labels. By default it is `tf$float32`.
`label_default`	used as label if label_key does not exist in given `tf$Example`. By default default_value is none, which means `tf$parse_example` will error out if there is any missing label.
`label_dimension`	Number of regression targets per example. This is the size of the last dimension of the labels and logits `Tensor` objects (typically, these have shape `⁠[batch_size, label_dimension]⁠`).
`weight_column`	A string or a `⁠_NumericColumn⁠` created by `column_numeric` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the `features`. If it is a `⁠_NumericColumn⁠`, raw tensor is fetched by key `weight_column$key`, then `weight_column$normalizer_fn` is applied on it to get weight tensor.

Value

A dict mapping each feature key to a FixedLenFeature or VarLenFeature value.

Raises

ValueError: If label is used in feature_columns.
ValueError: If weight_column is used in feature_columns.
ValueError: If any of the given feature_columns is not a ⁠_FeatureColumn⁠ instance.
ValueError: If weight_column is not a ⁠_NumericColumn⁠ instance.
ValueError: if label_key is NULL.

Run Configuration

Description

This class specifies the configurations for an Estimator run.

Usage

run_config()
run_config()

Examples

## Not run: 
config <- run_config()

# Get the properties of the config
names(config)

# Change the mutable properties of the config
config <- config$replace(tf_random_seed = 11L, save_summary_steps = 12L)

# Print config as key value pairs
print(config)

## End(Not run)

## Not run: 
config <- run_config()

# Get the properties of the config
names(config)

# Change the mutable properties of the config
config <- config$replace(tf_random_seed = 11L, save_summary_steps = 12L)

# Print config as key value pairs
print(config)

## End(Not run)

Create Session Run Arguments

Description

Create a set of session run arguments. These are used as the return values in the before_run(context) callback of a session_run_hook(), for requesting the values of specific tensor in the after_run(context, values) callback.

Usage

session_run_args(...)
session_run_args(...)

Arguments

...

A set of tensors or operations.

Create Custom Session Run Hooks

Description

Create a set of session run hooks, used to record information during training of an estimator. See Details for more information on the various hooks that can be defined.

Usage

session_run_hook(
  begin = function() { },
  after_create_session = function(session, coord) { },
  before_run = function(context) { },
  after_run = function(context, values) { },
  end = function(session) { }
)
session_run_hook(
  begin = function() { },
  after_create_session = function(session, coord) { },
  before_run = function(context) { },
  after_run = function(context, values) { },
  end = function(session) { }
)

Arguments

`begin`	`⁠function()⁠`: An R function, to be called once before using the session.
`after_create_session`	`⁠function(session, coord)⁠`: An R function, to be called once the new TensorFlow session has been created.
`before_run`	`⁠function(run_context)⁠`: An R function to be called before a run.
`after_run`	`⁠function(run_context, run_values)⁠`: An R function to be called after a run.
`end`	`⁠function(session)⁠`: An R function to be called at the end of the session. Typically, you'll want to define a `before_run()` hook that defines the set of tensors you're interested in for a particular run, and then you'll use the resulting values of those tensors in your `after_run()` hook. The tensors requested in your `before_run()` hook will be made available as part of the second argument in the `after_run()` hook (the `values` argument).

Task Types

Description

This constant class gives the constant strings for available task types used in run_config.

Usage

task_type()
task_type()

Examples

## Not run: 
task_type()$MASTER

## End(Not run)

## Not run: 
task_type()$MASTER

## End(Not run)

High-level Estimator API in TensorFlow for R

Description

This library provides an R interface to the Estimator API inside TensorFlow that's designed to streamline the process of creating, evaluating, and deploying general machine learning and deep learning models.

Details

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

The TensorFlow API is composed of a set of Python modules that enable constructing and executing TensorFlow graphs. The tensorflow package provides access to the complete TensorFlow API from within R.

For additional documentation on the tensorflow package see https://tensorflow.rstudio.com

Train and evaluate the estimator.

Description

(Available since TensorFlow v1.4)

Usage

## S3 method for class 'tf_estimator'
train_and_evaluate(object, train_spec, eval_spec, ...)
## S3 method for class 'tf_estimator'
train_and_evaluate(object, train_spec, eval_spec, ...)

Arguments

`object`	An estimator object to train and evaluate.
`train_spec`	A `TrainSpec` instance to specify the training specification.
`eval_spec`	A `EvalSpec` instance to specify the evaluation and export specification.
`...`	Not used.

Details

This utility function trains, evaluates, and (optionally) exports the model by using the given estimator. All training related specification is held in train_spec, including training input_fn and training max steps, etc. All evaluation and export related specification is held in eval_spec, including evaluation input_fn, steps, etc.

This utility function provides consistent behavior for both local (non-distributed) and distributed configurations. Currently, the only supported distributed training configuration is between-graph replication.

Overfitting: In order to avoid overfitting, it is recommended to set up the training input_fn to shuffle the training data properly. It is also recommended to train the model a little longer, say multiple epochs, before performing evaluation, as the input pipeline starts from scratch for each training. It is particularly important for local training and evaluation.

Stop condition: In order to support both distributed and non-distributed configuration reliably, the only supported stop condition for model training is train_spec.max_steps. If train_spec.max_steps is NULL, the model is trained forever. Use with care if model stop condition is different. For example, assume that the model is expected to be trained with one epoch of training data, and the training input_fn is configured to throw OutOfRangeError after going through one epoch, which stops the Estimator.train. For a three-training-worker distributed configuration, each training worker is likely to go through the whole epoch independently. So, the model will be trained with three epochs of training data instead of one epoch.

Raises

ValueError: if environment variable TF_CONFIG is incorrectly set.

Configuration for the train component of `train_and_evaluate`

Description

TrainSpec determines the input data for the training, as well as the duration. Optional hooks run at various stages of training.

Usage

train_spec(input_fn, max_steps = NULL, hooks = NULL)
train_spec(input_fn, max_steps = NULL, hooks = NULL)

Arguments

input_fn

Training input function returning a tuple of:

features - Tensor or dictionary of string feature name to Tensor.
labels - Tensor or dictionary of Tensor with labels.

max_steps

Positive number of total steps for which to train model. If NULL, train forever. The training input_fn is not expected to generate OutOfRangeError or StopIteration exceptions.

hooks

List of session run hooks to run on all workers (including chief) during training.

Base Documentation for train, evaluate, and predict.

Description

Base Documentation for train, evaluate, and predict.

Arguments

`input_fn`	An input function, typically generated by the `input_fn()` helper function.
`hooks`	A list of R functions, to be used as callbacks inside the training loop. By default, `hook_history_saver(every_n_step = 10)` and `hook_progress_bar()` will be attached if not provided to save the metrics history and create the progress bar.
`checkpoint_path`	The path to a specific model checkpoint to be used for prediction. If `NULL` (the default), the latest checkpoint in `model_dir` is used.

Train an Estimator

Description

Train an estimator on a set of input data provides by the input_fn().

Usage

## S3 method for class 'tf_estimator'
train(
  object,
  input_fn,
  steps = NULL,
  hooks = NULL,
  max_steps = NULL,
  saving_listeners = NULL,
  ...
)
## S3 method for class 'tf_estimator'
train(
  object,
  input_fn,
  steps = NULL,
  hooks = NULL,
  max_steps = NULL,
  saving_listeners = NULL,
  ...
)

Arguments

`object`	A TensorFlow estimator.
`input_fn`	An input function, typically generated by the `input_fn()` helper function.
`steps`	The number of steps for which the model should be trained on this particular `train()` invocation. If `NULL` (the default), this function will either train forever, or until the supplied `input_fn()` has provided all available data.
`hooks`	A list of R functions, to be used as callbacks inside the training loop. By default, `hook_history_saver(every_n_step = 10)` and `hook_progress_bar()` will be attached if not provided to save the metrics history and create the progress bar.
`max_steps`	The total number of steps for which the model should be trained. If set, `steps` must be `NULL`. If the estimator has already been trained a total of `max_steps` times, then no training will be performed.
`saving_listeners`	(Available since TensorFlow v1.4) A list of `CheckpointSaverListener` objects used for callbacks that run immediately before or after checkpoint savings.
`...`	Optional arguments, passed on to the estimator's `train()` method.

Value

A data.frame of the training loss history.

Get variable names and values associated with an estimator

Description

These helper functions extract the names and values of variables in the graphs associated with trained estimator models.

Usage

variable_names(object)

variable_value(object, variable = NULL)
variable_names(object)

variable_value(object, variable = NULL)

Arguments

`object`	A trained estimator model.
`variable`	(Optional) Names of variables to extract as a character vector. If not specified, values for all variables are returned.

Value

For variable_names(), a vector of variable names. For variable_values(), a named list of variable values.

Package 'tfestimators'

Help Index

Boosted Trees Estimator

Description

Usage

Arguments

See Also

Generates Parsing Spec for TensorFlow Example to be Used with Classifiers

Description

Usage

Arguments

Value

Raises

See Also

Base Documentation for Feature Column Constructors

Description

Arguments

Construct a Bucketized Column

Description

Usage

Arguments

Value

Raises

See Also

Construct a Weighted Categorical Column

Description

Usage

Arguments

Value

Raises

See Also

Represents Sparse Feature where IDs are set by Hashing

Description

Usage

Arguments

Value

Raises

See Also

Construct a Categorical Column that Returns Identity Values

Description

Usage

Arguments

Details

Value

Raises

See Also

Construct a Categorical Column with a Vocabulary File

Description

Usage

Arguments

Value

Raises

See Also

Construct a Categorical Column with In-Memory Vocabulary

Description

Usage

Arguments

Details

Value

Raises

See Also

Construct a Crossed Column

Description

Usage

Arguments

Value

Raises

See Also

Construct a Dense Column

Description

Usage

Arguments

Value

Raises

See Also

Represents Multi-Hot Representation of Given Categorical Column

Description

Usage

Arguments

Value

Configuration for the eval component of `train_and_evaluate`