def my_input_fn(data_file, num_epochs, batch_size):
    dataset = tf.data.experimental.make_csv_dataset(
        data_file,
        batch_size=batch_size,
        column_names=_CSV_COLUMNS, # ['int1', 'int2', 'int3', 'int4']
        label_name='int4',
        na_value="?",
        num_epochs=num_epochs,
        ignore_errors=True)
    return dataset

train_inpf = functools.partial(my_input_fn, train_file, num_epochs=2, shuffle=True, batch_size=32)
test_inpf = functools.partial(my_input_fn, test_file, num_epochs=1, shuffle=False, batch_size=1)

col1 = tf.feature_column.categorical_column_with_vocabulary_list(
    'int1', column_uniques_lists['int1'], dtype=tf.int64)

col2 = tf.feature_column.categorical_column_with_vocabulary_list(
    'int2', column_uniques_lists['int2'], dtype=tf.int64)


col3 = tf.feature_column.categorical_column_with_vocabulary_list(
    'int3', column_uniques_lists['int3'], dtype=tf.int64)

my_categorical_columns = [col1,col2,col3]

classifier = tf.estimator.LinearClassifier(feature_columns=my_categorical_columns,                                           n_classes=len(column_uniques_lists['int4']), model_dir='.\SaveLC\model_dir')