# tf.contrib.feature_column.sequence_categorical_column_with_vocabulary_file

tf.contrib.feature_column.sequence_categorical_column_with_vocabulary_file(
key,
vocabulary_file,
vocabulary_size=None,
num_oov_buckets=0,
default_value=None,
dtype=tf.string
)


A sequence of categorical terms where ids use a vocabulary file.

Pass this to embedding_column or indicator_column to convert sequence categorical data into dense representation for input to sequence NN, such as RNN.

Example:

states = sequence_categorical_column_with_vocabulary_file(
key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
num_oov_buckets=5)
states_embedding = embedding_column(states, dimension=10)
columns = [states_embedding]

features = tf.parse_example(..., features=make_parse_example_spec(columns))
input_layer, sequence_length = sequence_input_layer(features, columns)

rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
outputs, state = tf.nn.dynamic_rnn(
rnn_cell, inputs=input_layer, sequence_length=sequence_length)


#### Args:

• key: A unique string identifying the input feature.
• vocabulary_file: The vocabulary file name.
• vocabulary_size: Number of the elements in the vocabulary. This must be no greater than length of vocabulary_file, if less than length, later values are ignored. If None, it is set to the length of vocabulary_file.
• 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 [vocabulary_size, vocabulary_size+num_oov_buckets) based on a hash of the input value. A positive num_oov_buckets can not be specified with default_value.
• default_value: The integer ID value to return for out-of-vocabulary feature values, defaults to -1. This can not be specified with a positive num_oov_buckets.
• dtype: The type of features. Only string and integer types are supported.

#### Returns:

A _SequenceCategoricalColumn.

#### Raises:

• ValueError: vocabulary_file is missing or cannot be opened.
• ValueError: vocabulary_size is missing or < 1.
• ValueError: num_oov_buckets is a negative integer.
• ValueError: num_oov_buckets and default_value are both specified.
• ValueError: dtype is neither string nor integer.