library(tfdatasets)
data(hearts)
<- tensor_slices_dataset(hearts) %>% dataset_batch(32)
hearts
# use the formula interface
<- feature_spec(hearts, target ~ age) %>%
spec step_numeric_column(age, normalizer_fn = standard_scaler())
<- fit(spec)
spec_fit <- hearts %>% dataset_use_spec(spec_fit) final_dataset
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$float32,
normalizer_fn = NULL
)
Arguments
Arguments | Description |
---|---|
spec | A feature specification created with feature_spec() . |
… | Comma separated list of variable names to apply the step. selectors can also be used. |
shape | An iterable of integers specifies the shape of the Tensor. An integer can be given which means a single dimension Tensor with given width. The Tensor representing the column will have the shape of batch_size + shape . |
default_value | A single value compatible with dtype or an iterable of values compatible with dtype which the column takes on during tf.Example parsing if data is missing. A default value of NULL will cause tf.parse_example to fail if an example does not contain this column. If a single value is provided, the same value will be applied as the default value for every item. If an iterable of values is provided, the shape of the default_value should be equal to the given shape. |
dtype | defines the type of values. Default value is tf$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. |
Value
a FeatureSpec
object.
Examples
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