Construct a Weighted Categorical Column

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)

Arguments

categorical_column

A categorical column created by column_categorical_*() functions.
weight_feature_key

String key for weight values.
dtype

Type of weights, such as tf$float32. Only float and integer weights are supported.

Value

A categorical column composed of two sparse features: one represents id, the other represents weight (value) of the id feature in that example.

Raises

  • ValueError: if dtype is not convertible to float.

See also

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