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)
... |
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. |
A categorical column with a vocabulary file.
ValueError: vocabulary_file is missing.
ValueError: vocabulary_size is missing or < 1.
ValueError: num_oov_buckets is not a non-negative integer.
ValueError: dtype is neither string nor integer.
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.