Single node tensorflow model

lr-single.py

import tensorflow as tf


_CSV_COLUMNS = [
    'age', 'workclass', 'fnlwgt', 'education', 'education_num',
    'marital_status', 'occupation', 'relationship', 'race', 'gender',
    'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',
    'income_bracket'
]

_CSV_COLUMN_DEFAULTS = [[0], [''], [0], [''], [0], [''], [''], [''], [''], [''],
                        [0], [0], [0], [''], ['']]

_NUM_EXAMPLES = {
    'train': 32561,
    'validation': 16281,
}


def build_model_columns():
    """Builds a set of wide and deep feature columns."""
    # Continuous columns
    age = tf.feature_column.numeric_column('age')
    education_num = tf.feature_column.numeric_column('education_num')
    capital_gain = tf.feature_column.numeric_column('capital_gain')
    capital_loss = tf.feature_column.numeric_column('capital_loss')
    hours_per_week = tf.feature_column.numeric_column('hours_per_week')

    education = tf.feature_column.categorical_column_with_vocabulary_list(
        'education', [
            'Bachelors', 'HS-grad', '11th', 'Masters', '9th', 'Some-college',
            'Assoc-acdm', 'Assoc-voc', '7th-8th', 'Doctorate', 'Prof-school',
            '5th-6th', '10th', '1st-4th', 'Preschool', '12th'])

    marital_status = tf.feature_column.categorical_column_with_vocabulary_list(
        'marital_status', [
            'Married-civ-spouse', 'Divorced', 'Married-spouse-absent',
            'Never-married', 'Separated', 'Married-AF-spouse', 'Widowed'])

    relationship = tf.feature_column.categorical_column_with_vocabulary_list(
        'relationship', [
            'Husband', 'Not-in-family', 'Wife', 'Own-child', 'Unmarried',
            'Other-relative'])

    workclass = tf.feature_column.categorical_column_with_vocabulary_list(
        'workclass', [
            'Self-emp-not-inc', 'Private', 'State-gov', 'Federal-gov',
            'Local-gov', '?', 'Self-emp-inc', 'Without-pay', 'Never-worked'])

    # To show an example of hashing:
    occupation = tf.feature_column.categorical_column_with_hash_bucket(
        'occupation', hash_bucket_size=1000)

    # Transformations.
    age_buckets = tf.feature_column.bucketized_column(
        age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])

    # Wide columns and deep columns.
    base_columns = [
        education, marital_status, relationship, workclass, occupation,
        age_buckets,
    ]

    crossed_columns = [
        tf.feature_column.crossed_column(
            ['education', 'occupation'], hash_bucket_size=1000),
        tf.feature_column.crossed_column(
            [age_buckets, 'education', 'occupation'], hash_bucket_size=1000),
    ]

    wide_columns = base_columns + crossed_columns

    deep_columns = [
        age,
        education_num,
        capital_gain,
        capital_loss,
        hours_per_week,
        tf.feature_column.indicator_column(workclass),
        tf.feature_column.indicator_column(education),
        tf.feature_column.indicator_column(marital_status),
        tf.feature_column.indicator_column(relationship),
        # To show an example of embedding
        tf.feature_column.embedding_column(occupation, dimension=8),
    ]

    return wide_columns, deep_columns


def input_fn(data_file, num_epochs=None, shuffle=True, batch_size=128):
    """Generate an input function for the Estimator."""
    assert tf.gfile.Exists(data_file), (
            '%s not found. Please make sure you have run data_download.py and '
            'set the --data_dir argument to the correct path.' % data_file)

    def parse_csv(value):
        print('Parsing', data_file)
        columns = tf.decode_csv(value, record_defaults=_CSV_COLUMN_DEFAULTS)
        features = dict(zip(_CSV_COLUMNS, columns))
        labels = features.pop('income_bracket')
        return features, tf.equal(labels, '>50K')

    # Extract lines from input files using the Dataset API.
    dataset = tf.data.TextLineDataset(data_file)

    if shuffle:
        dataset = dataset.shuffle(buffer_size=_NUM_EXAMPLES['train'])

    dataset = dataset.map(parse_csv, num_parallel_calls=5)

    # We call repeat after shuffling, rather than before, to prevent separate
    # epochs from blending together.
    dataset = dataset.repeat(num_epochs)
    dataset = dataset.batch(batch_size)
    return dataset


def build_model(filename):
    wide_columns, deep_columns = build_model_columns()
    features, labels = input_fn(filename).make_one_shot_iterator().get_next()
    cols_to_vars = {}
    logits = tf.feature_column.linear_model(features=features, feature_columns=wide_columns, cols_to_vars=cols_to_vars)
    predictions = tf.reshape(tf.nn.sigmoid(logits), (-1,))
    loss = tf.losses.log_loss(labels=labels, predictions=predictions)
    optimizer = tf.train.FtrlOptimizer(learning_rate=0.1, l1_regularization_strength=0.1, l2_regularization_strength=0.1)
    train_op = optimizer.minimize(loss)

    return {
        'train': {
            'train_op': train_op,
            'loss': loss
        },
        'init': {
            'global': [tf.global_variables_initializer()],
            'local': [tf.local_variables_initializer(), tf.tables_initializer()]
        },
        'cols_to_vars': cols_to_vars
    }


def main():
    # build graph
    model = build_model(filename='census_data/adult.data')

    # inspect graph variables
    for col, var in model['cols_to_vars'].items():
        print('Column:  ', col)
        print('Variable:', var)
        print('-' * 50)

    # create session
    with tf.Session() as sess:
        sess.run(model['init'])

        for step in range(1, 1000):
            result = sess.run(model['train'])
            print('step =', step, 'loss =', result['loss'])


if __name__ == '__main__':
    main()