fit.FeatureSpec: Fits a feature specification.

In tfdatasets: Interface to 'TensorFlow' Datasets

Fits a feature specification.

Description

This function will fit the specification. Depending on the steps added to the specification it will compute for example, the levels of categorical features, normalization constants, etc.

Usage

## S3 method for class 'FeatureSpec'
fit(object, dataset = NULL, ...)

Arguments

object	A feature specification created with feature_spec().
dataset	(Optional) A TensorFlow dataset. If NULL it will use the dataset provided when initilializing the feature_spec.
...	(unused)

Value

a fitted FeatureSpec object.

See Also

• feature_spec() to initialize the feature specification.

• dataset_use_spec() to create a tensorflow dataset prepared to modeling.

• steps to a list of all implemented steps.

Other Feature Spec Functions: dataset_use_spec(), feature_spec(), step_bucketized_column(), step_categorical_column_with_hash_bucket(), step_categorical_column_with_identity(), step_categorical_column_with_vocabulary_file(), step_categorical_column_with_vocabulary_list(), step_crossed_column(), step_embedding_column(), step_indicator_column(), step_numeric_column(), step_remove_column(), step_shared_embeddings_column(), steps

Examples

## Not run: 
library(tfdatasets)
data(hearts)
hearts <- tensor_slices_dataset(hearts) %>% dataset_batch(32)

# use the formula interface
spec <- feature_spec(hearts, target ~ age) %>%
  step_numeric_column(age)

spec_fit <- fit(spec)
spec_fit

## End(Not run)

tfdatasets documentation built on June 30, 2022, 1:04 a.m.