feature_columns.py

fc = tf.feature_column
CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck', 
                       'embark_town', 'alone']
NUMERIC_COLUMNS = ['age', 'fare']
  
def one_hot_cat_column(feature_name, vocab):
  return tf.feature_column.indicator_column(
      tf.feature_column.categorical_column_with_vocabulary_list(feature_name,
                                                                vocab))
feature_columns = []
for feature_name in CATEGORICAL_COLUMNS:
  # Need to one-hot encode categorical features.
  vocabulary = dftrain[feature_name].unique()
  feature_columns.append(one_hot_cat_column(feature_name, vocabulary))
  
for feature_name in NUMERIC_COLUMNS:
  feature_columns.append(tf.feature_column.numeric_column(feature_name,
                                                          dtype=tf.float32))