tfdatasets (version 2.17.0)

step_numeric_column: Creates a numeric column specification

Description

step_numeric_column creates a numeric column specification. It can also be used to normalize numeric columns.

Usage

step_numeric_column(
  spec,
  ...,
  shape = 1L,
  default_value = NULL,
  dtype = tf$float32,
  normalizer_fn = NULL
)

Value

a FeatureSpec object.

Arguments

spec

A feature specification created with feature_spec().

...

Comma separated list of variable names to apply the step. selectors can also be used.

shape

An iterable of integers 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 tf.Example 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

defines the type of values. 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. You can also a pre-made scaler, in this case a function will be created after fit.FeatureSpec is called on the feature specification.

See Also

steps for a complete list of allowed steps.

Other Feature Spec Functions: dataset_use_spec(), feature_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_remove_column(), step_shared_embeddings_column(), steps

Examples

if (FALSE) {
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, normalizer_fn = standard_scaler())

spec_fit <- fit(spec)
final_dataset <- hearts %>% dataset_use_spec(spec_fit)
}