A sequence of categorical terms where ids use a vocabulary file.
tf.feature_column.sequence_categorical_column_with_vocabulary_file(
key, vocabulary_file, vocabulary_size=None, num_oov_buckets=0,
default_value=None, dtype=tf.dtypes.string
)
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.io.parse_example(..., features=make_parse_example_spec(columns))
sequence_feature_layer = SequenceFeatures(columns)
sequence_input, sequence_length = sequence_feature_layer(features)
sequence_length_mask = tf.sequence_mask(sequence_length)
rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size)
rnn_layer = tf.keras.layers.RNN(rnn_cell)
outputs, state = rnn_layer(sequence_input, mask=sequence_length_mask)
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.
|
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TensorFlow 1 version
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