Tensorflow One Hot Encoder?

Question

Does tensorflow have something similar to scikit learn\'s one hot encoder for processing categorical data? Would using a placeholder of tf.string behave as categorical data

Answer 1

Tensorflow 2.0 Compatible Answer: You can do it efficiently using Tensorflow Transform.

Code for performing One-Hot Encoding using Tensorflow Transform is shown below:

def get_feature_columns(tf_transform_output):
  """Returns the FeatureColumns for the model.

  Args:
    tf_transform_output: A `TFTransformOutput` object.

  Returns:
    A list of FeatureColumns.
  """
  # Wrap scalars as real valued columns.
  real_valued_columns = [tf.feature_column.numeric_column(key, shape=())
                         for key in NUMERIC_FEATURE_KEYS]

  # Wrap categorical columns.
  one_hot_columns = [
      tf.feature_column.categorical_column_with_vocabulary_file(
          key=key,
          vocabulary_file=tf_transform_output.vocabulary_file_by_name(
              vocab_filename=key))
      for key in CATEGORICAL_FEATURE_KEYS]

  return real_valued_columns + one_hot_columns

For more information, refer this Tutorial on TF_Transform.