tf.compat.v1.feature_column.categorical_column_with_vocabulary_file

A CategoricalColumn 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 SparseTensor. If Tensor, missing values can be represented by -1 for int and '' for string, which will be dropped by this feature column.

Example with num_oov_buckets: File '/us/states.txt' contains 50 lines, each with a 2-character U.S. state abbreviation. All inputs with values in that file are assigned an ID 0-49, corresponding to its line number. All other values are hashed and assigned an ID 50-54.

import tensorflow as tf
states = tf.feature_column.categorical_column_with_vocabulary_file(
  key='states', vocabulary_file='states.txt', vocabulary_size=5,
  num_oov_buckets=1)
columns = [states]
features = {'states':tf.constant([['california', 'georgia', 'michigan',
'texas', 'new york'], ['new york', 'georgia', 'california', 'michigan',
'texas']])}
linear_prediction = tf.compat.v1.feature_column.linear_model(features,
columns)

Example with default_value: File '/us/states.txt' contains 51 lines - the first line is 'XX', and the other 50 each have a 2-character U.S. state abbreviation. Both a literal 'XX' in input, and other values missing from the file, will be assigned ID 0. All others are assigned the corresponding line number 1-50.

import tensor