Use this when each of your sparse inputs has both an ID and a value. For example, if you're representing text documents as a collection of word frequencies, you can provide 2 parallel sparse input features ('terms' and 'frequencies' below).
column_categorical_weighted(
categorical_column,
weight_feature_key,
dtype = tf$float32
)A categorical column created by
column_categorical_*() functions.
String key for weight values.
Type of weights, such as tf$float32. Only float and integer
weights are supported.
A categorical column composed of two sparse features: one represents id, the other represents weight (value) of the id feature in that example.
ValueError: if dtype is not convertible to float.
Other feature column constructors:
column_bucketized(),
column_categorical_with_hash_bucket(),
column_categorical_with_identity(),
column_categorical_with_vocabulary_file(),
column_categorical_with_vocabulary_list(),
column_crossed(),
column_embedding(),
column_numeric(),
input_layer()