Construct a Categorical Column with a Vocabulary File
Use this when your inputs are in string or integer format, and you have a
vocabulary file that maps each value to an integer ID. By default,
out-of-vocabulary values are ignored. Use either (but not both) of
num_oov_buckets and default_value to specify how to include
out-of-vocabulary values. 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.
column_categorical_with_vocabulary_file(..., vocabulary_file, vocabulary_size,
num_oov_buckets = 0L, default_value = NULL, dtype = tf$string)Arguments
| ... | Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns. |
| vocabulary_file | The vocabulary file name. |
| vocabulary_size | Number of the elements in the vocabulary. This must be
no greater than length of |
| num_oov_buckets | Non-negative integer, the number of out-of-vocabulary
buckets. All out-of-vocabulary inputs will be assigned IDs in the range
|
| default_value | The integer ID value to return for out-of-vocabulary
feature values, defaults to |
| dtype | The type of features. Only string and integer types are supported. |
Value
A categorical column with a vocabulary file.
Raises
ValueError:
vocabulary_fileis missing.ValueError:
vocabulary_sizeis missing or < 1.ValueError:
num_oov_bucketsis not a non-negative integer.ValueError:
dtypeis neither string nor integer.
See also
Other feature column constructors: column_bucketized,
column_categorical_weighted,
column_categorical_with_hash_bucket,
column_categorical_with_identity,
column_categorical_with_vocabulary_list,
column_crossed,
column_embedding,
column_numeric, input_layer