feature_spec: Creates a feature specification.

In tfdatasets: Interface to 'TensorFlow' Datasets

Creates a feature specification.

Description

Used to create initialize a feature columns specification.

Usage

feature_spec(dataset, x, y = NULL)

Arguments

dataset	A TensorFlow dataset.
x	Features to include can use tidyselect::select_helpers() or a formula.
y	(Optional) The response variable. Can also be specified using a formula in the x argument.

Details

After creating the feature_spec object you can add steps using the step functions.

Value

a FeatureSpec object.

See Also

• fit.FeatureSpec() to fit the FeatureSpec

• 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(), fit.FeatureSpec(), 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 ~ .)

# select using `tidyselect` helpers
spec <- feature_spec(hearts, x = c(thal, age), y = target)

## End(Not run)

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