datatype=one of integer[4],integer[8],float[4],float[8]

shape=list of integers

The NumericColumn(c) command creates a feature column to represent a real numerical feature (integer or floating-point).

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

The implementation of NumericColumn uses the tf.feature_column.numeric_column function from the TensorFlow Python API Documentation. Consult the TensorFlow API documentation for tf.feature_column.numeric_column for more information.

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

$\mathrm{fc}≔\mathrm{NumericColumn}\left(''SepalLength''\,\mathrm{shape}=\left[1\right]\,\mathrm{datatype}=\mathrm{float}\left[8\right]\right)$

$\mathrm{fc}≔\left[\begin{array}{c}\mathrm{Feature\; Column}\\ \mathrm{NumericColumn(key=\text{'}SepalLength\text{'},\; shape=(1,),\; default\_value=None,\; dtype=tf.float64,\; normalizer\_fn=None)}\end{array}\right]$

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

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