Returns a feature column that represents sequences of integers.
tf.contrib.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))
input_layer, sequence_length = sequence_input_layer(features, columns)
rnn_cell = tf.compat.v1.nn.rnn_cell.BasicRNNCell(hidden_size)
outputs, state = tf.compat.v1.nn.dynamic_rnn(
rnn_cell, inputs=input_layer, sequence_length=sequence_length)
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
|
If None , 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
|
if num_buckets is less than one.
|
ValueError
|
if default_value is not in range [0, num_buckets) .
|