tf.feature_column.categorical_column_with_identity      View source on GitHub    A CategoricalColumn that returns identity values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.feature_column.categorical_column_with_identity  
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: import tensorflow as tf
video_id = tf.feature_column.categorical_column_with_identity(
    key='video_id', num_buckets=1000000, default_value=0)
columns = [video_id]
features = {'video_id': tf.sparse.from_dense([[2, 85, 0, 0, 0],
[33,78, 2, 73, 1]])}
linear_prediction = tf.compat.v1.feature_column.linear_model(features,
columns)
 Embedding for a DNN model: import tensorflow as tf
video_id = tf.feature_column.categorical_column_with_identity(
    key='video_id', num_buckets=1000000, default_value=0)
columns = [tf.feature_column.embedding_column(video_id, 9)]
features = {'video_id': tf.sparse.from_dense([[2, 85, 0, 0, 0],
[33,78, 2, 73, 1]])}
input_layer = tf.keras.layers.DenseFeatures(columns)
dense_tensor = input_layer(features)

 


 Args
  key   A unique string identifying the input feature. It is used as the column name and the dictionary key for feature parsing configs, feature Tensor objects, and feature columns.  
  num_buckets   Range of inputs and outputs is [0, num_buckets).  
  default_value   If set, values outside of range [0, num_buckets) will be replaced with this value. If not set, values >= num_buckets will cause a failure while values < 0 will be dropped.   
 


 Returns   A CategoricalColumn that returns identity values.  

 


 Raises
  ValueError   if num_buckets is less than one.  
  ValueError   if default_value is not in range [0, num_buckets).     

    
{'path': 'public/docs/tensorflow~2.9/feature_column/categorical_column_with_identity.html', 'source': 'DevDocs.IO'}