Difference between indicator column and categorical identity column in tensorflow
indicator_column encodes the input to a multi-hot representation, not one-hot encoding.
The example clarifies more:
name = indicator_column(categorical_column_with_vocabulary_list(
    'name', ['bob', 'george', 'wanda'])
columns = [name, ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)

dense_tensor == [[1, 0, 0]]  # If &quot;name&quot; bytes_list is [&quot;bob&quot;]
dense_tensor == [[1, 0, 1]]  # If &quot;name&quot; bytes_list is [&quot;bob&quot;, &quot;wanda&quot;]
dense_tensor == [[2, 0, 0]]  # If &quot;name&quot; bytes_list is [&quot;bob&quot;, &quot;bob&quot;] 

The last two examples describe what is meant by multi-hot encoding. for example if the input be [&quot;bob&quot;, &quot;wanda&quot;] the encoding will be [[1, 0, 1]].