tf.feature_column.sequence_categorical_column_with_identity
Returns a feature column that represents sequences of integers.
Aliases:
tf.compat.v1.feature_column.sequence_categorical_column_with_identity
tf.compat.v2.feature_column.sequence_categorical_column_with_identity
tf.feature_column.sequence_categorical_column_with_identity(
key,
num_buckets,
default_value=None
)
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:
watches = sequence_categorical_column_with_identity(
'watches', num_buckets=1000)
watches_embedding = embedding_column(watches, dimension=10)
columns = [watches_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.num_buckets
: Range of inputs. Namely, inputs are expected to be in the range[0, num_buckets)
.default_value
: IfNone
, this column's graph operations will fail for out-of-range inputs. Otherwise, this value must be in the range[0, num_buckets)
, and will replace out-of-range inputs.
Returns:
A SequenceCategoricalColumn
.
Raises:
ValueError
: ifnum_buckets
is less than one.ValueError
: ifdefault_value
is not in range[0, num_buckets)
.