datatype=one of integer[4],integer[8], or string

The CategoricalColumn(c) command creates a feature column to represent categorical data, data whose values are taken from some finite set known in advance, consisting of strings and integer values.

This function is part of the DeepLearning package, so it can be used in the short form CategoricalColumn(..) only after executing the command with(DeepLearning). However, it can always be accessed through the long form of the command by using DeepLearning[CategoricalColumn](..).

The implementation of CategoricalColumn uses the tf.feature_column.categorical_column_with_vocabulary_list command from the TensorFlow Python API Documentation. Consult the TensorFlow API documentation for tf.feature_column.categorical_column_with_vocabulary_list for more information.

$\mathrm{with}\left(\mathrm{DeepLearning}\right)\:$

$\mathrm{fc}≔\mathrm{CategoricalColumn}\left(''color''\,\left[''red''\,''white''\,''blue''\,''green''\right]\right)$

$\mathrm{fc}≔\left[\begin{array}{c}\mathrm{Feature\; Column}\\ \mathrm{VocabularyListCategoricalColumn(key=\text{'}color\text{'},\; vocabulary\_list=(\text{'}red\text{'},\; \text{'}white\text{'},\; \text{'}blue\text{'},\; \text{'}green\text{'}),\; dtype=tf.string,\; default\_value=-1,\; num\_oov\_buckets=0)}\end{array}\right]$

The DeepLearning[CategoricalColumn] command was introduced in Maple 2018.

For more information on Maple 2018 changes, see Updates in Maple 2018.