tf.feature_column.sequence_categorical_column_with_identity
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Returns a feature column that represents sequences of integers. (deprecated)
tf . feature_column . sequence_categorical_column_with_identity (
key , num_buckets , default_value = None
)
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Use Keras preprocessing layers instead, either directly or via the tf.keras.utils.FeatureSpace
utility. Each of tf.feature_column.*
has a functional equivalent in tf.keras.layers
for feature preprocessing when training a Keras model.
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
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)
.
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Last updated 2024-04-26 UTC.
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{"lastModified": "Last updated 2024-04-26 UTC."}
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