R: Generates Parsing Spec for TensorFlow Example to be Used with...

classifier_parse_example_spec {tfestimators}

R Documentation

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
)

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