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A sequence of categorical terms where ids use an in-memory list.
tf.feature_column.sequence_categorical_column_with_vocabulary_list(
key, vocabulary_list, dtype=None, default_value=-1, num_oov_buckets=0
)
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:
colors = sequence_categorical_column_with_vocabulary_list(
key='colors', vocabulary_list=('R', 'G', 'B', 'Y'),
num_oov_buckets=2)
colors_embedding = embedding_column(colors, dimension=3)
columns = [colors_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)
Returns | |
---|---|
A SequenceCategoricalColumn .
|