tf.feature_column.categorical_column_with_identity

TensorFlow 1 version

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A CategoricalColumn that returns identity values.

tf.feature_column.categorical_column_with_identity(
    key, num_buckets, default_value=None
)

Use this when your inputs are integers in the range [0, num_buckets), and you want to use the input value itself as the categorical ID. Values outside this range will result in default_value if specified, otherwise it will fail.

Typically, this is used for contiguous ranges of integer indexes, but it doesn't have to be. This might be inefficient, however, if many of IDs are unused. Consider categorical_column_with_hash_bucket in that case.

For input dictionary features, features[key] is either Tensor or SparseTensor. If Tensor, missing values can be represented by -1 for int and '' for string, which will be dropped by this feature column.

In the following examples, each input in the range [0, 1000000) is assigned the same value. All other inputs are assigned default_value 0. Note that a literal 0 in inputs will result in the same default ID.

Linear model:

video_id = categorical_column_with_identity(
    key='video_id', num_buckets=1000000, default_value=0)
columns = [video_id, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction, _, _ = linear_model(features, columns)

Embedding for a DNN model:

columns = [embedding_column(video_id, 9),...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)