Use this when your inputs are integers in the range [0, num_buckets)
, and
you want to use the input value itself as the categorical ID. Values outside
this range will result in default_value
if specified, otherwise it will
fail.
column_categorical_with_identity(..., num_buckets, default_value = NULL)
Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns.
Number of unique values.
If NULL
, this column's graph operations will fail for
out-of-range inputs. Otherwise, this value must be in the range [0, num_buckets)
, and will replace inputs in that range.
A categorical column that returns identity values.
ValueError: if num_buckets
is less than one.
ValueError: if default_value
is not in range [0, num_buckets)
.
Typically, this is used for contiguous ranges of integer indexes, but it
doesn't have to be. This might be inefficient, however, if many of IDs are
unused. Consider column_categorical_with_hash_bucket()
in that case.
For input dictionary features
, features$key
is either tensor or sparse
tensor object. If it's tensor object, missing values can be represented by -1
for
int and ''
for string. Note that these values are independent of the
default_value
argument.
Other feature column constructors:
column_bucketized()
,
column_categorical_weighted()
,
column_categorical_with_hash_bucket()
,
column_categorical_with_vocabulary_file()
,
column_categorical_with_vocabulary_list()
,
column_crossed()
,
column_embedding()
,
column_numeric()
,
input_layer()