Wide and Deep on TensorFlow (notebook style)

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Imports and constants

First we'll import our libraries and set up some strings for column names. We also print out the version of TensorFlow we are running.



In [10]:

    
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import time

import tensorflow as tf

tf.logging.set_verbosity(tf.logging.ERROR)

print("TensorFlow version %s" % (tf.__version__))

CATEGORICAL_COLUMNS = ["workclass", "education", "marital_status", "occupation", 
                       "relationship", "race", "gender", "native_country"]

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

train_file = "gs://tf-ml-workshop/widendeep/adult.data"
test_file = "gs://tf-ml-workshop/widendeep/adult.test"
train_steps = 1000


model_dir = 'models/model_' + str(int(time.time()))
print("model directory = %s" % model_dir)









    



TensorFlow version 0.12.1
model directory = models/model_1484225310

Input file parsing

This section puts the file into a Reader which reads from the file one batch at a time.

We set up the Tensors to be a dictionary of features mapping from their string name to the tensor value.

Note that the function is wrapped, enabling it to be used for different files.



In [2]:

    
def generate_input_fn(filename):
  def _input_fn():
    BATCH_SIZE = 40
    filename_queue = tf.train.string_input_producer([filename])
    reader = tf.TextLineReader()
    key, value = reader.read_up_to(filename_queue, num_records=BATCH_SIZE)

    record_defaults = [[0], [" "], [0], [" "], [0],
                       [" "], [" "], [" "], [" "], [" "],
                       [0], [0], [0], [" "], [" "]]
    columns = tf.decode_csv(
      value, record_defaults=record_defaults)

    features, income_bracket = dict(zip(COLUMNS, columns[:-1])), columns[-1]

    # remove the fnlwgt key, which is not used
    features.pop('fnlwgt', 'fnlwgt key not found')

    # works in 0.12 only
    for feature_name in CATEGORICAL_COLUMNS:
      features[feature_name] = tf.expand_dims(features[feature_name], -1)

    income_int = tf.to_int32(tf.equal(income_bracket, " >50K"))

    return features, income_int

  return _input_fn

Create Feature Columns

This section configures the model with the information about the model. There are many parameters here to experiment with to see how they affect the accuracy.

Sparse Columns

First we build the sparse columns. Use sparse_column_with_keys() for columns that we know all possible values for. Use sparse_column_with_hash_bucket() for columns that we want the the library to automatically map values for us.



In [5]:

    
# Sparse base columns.
gender = tf.contrib.layers.sparse_column_with_keys(column_name="gender",
                                                 keys=["female", "male"])
race = tf.contrib.layers.sparse_column_with_keys(column_name="race",
                                               keys=["Amer-Indian-Eskimo",
                                                     "Asian-Pac-Islander",
                                                     "Black", "Other",
                                                     "White"])

education = tf.contrib.layers.sparse_column_with_hash_bucket(
  "education", hash_bucket_size=1000)
marital_status = tf.contrib.layers.sparse_column_with_hash_bucket(
  "marital_status", hash_bucket_size=100)
relationship = tf.contrib.layers.sparse_column_with_hash_bucket(
  "relationship", hash_bucket_size=100)
workclass = tf.contrib.layers.sparse_column_with_hash_bucket(
  "workclass", hash_bucket_size=100)
occupation = tf.contrib.layers.sparse_column_with_hash_bucket(
  "occupation", hash_bucket_size=1000)
native_country = tf.contrib.layers.sparse_column_with_hash_bucket(
  "native_country", hash_bucket_size=1000)

Continuous columns

Second, configure the real-valued columns.



In [6]:

    
# Continuous base columns.
age = tf.contrib.layers.real_valued_column("age")
education_num = tf.contrib.layers.real_valued_column("education_num")
capital_gain = tf.contrib.layers.real_valued_column("capital_gain")
capital_loss = tf.contrib.layers.real_valued_column("capital_loss")
hours_per_week = tf.contrib.layers.real_valued_column("hours_per_week")

Transformations

We do a few combined features (feature crosses) here. You can add your own to improve on the model!



In [7]:

    
# Transformations.
age_buckets = tf.contrib.layers.bucketized_column(age,
            boundaries=[ 18, 25, 30, 35, 40, 45, 50, 55, 60, 65 ])
education_occupation = tf.contrib.layers.crossed_column([education, occupation], hash_bucket_size=int(1e4))
age_race_occupation = tf.contrib.layers.crossed_column( [age_buckets, race, occupation], hash_bucket_size=int(1e6))
country_occupation = tf.contrib.layers.crossed_column([native_country, occupation], hash_bucket_size=int(1e4))

Combine feature columns into 2 objects



In [8]:

    
# Wide columns and deep columns.
wide_columns = [gender, native_country,
      education, occupation, workclass,
      marital_status, relationship,
      age_buckets, education_occupation,
      age_race_occupation, country_occupation]

deep_columns = [
  tf.contrib.layers.embedding_column(workclass, dimension=8),
  tf.contrib.layers.embedding_column(education, dimension=8),
  tf.contrib.layers.embedding_column(marital_status, dimension=8),
  tf.contrib.layers.embedding_column(gender, dimension=8),
  tf.contrib.layers.embedding_column(relationship, dimension=8),
  tf.contrib.layers.embedding_column(race, dimension=8),
  tf.contrib.layers.embedding_column(native_country, dimension=8),
  tf.contrib.layers.embedding_column(occupation, dimension=8),
  age,
  education_num,
  capital_gain,
  capital_loss,
  hours_per_week,
]

Create the model, and fit it



In [11]:

    
# LinearClassifier
# m = tf.contrib.learn.LinearClassifier(model_dir=model_dir, feature_columns=wide_columns)

# Deep Neural Net Classifier
#  m = tf.contrib.learn.DNNClassifier(
#         model_dir=model_dir,
#         feature_columns=deep_columns,
#         hidden_units=[100, 50])

# Combined Linear and Deep Classifier
m = tf.contrib.learn.DNNLinearCombinedClassifier(
     model_dir=model_dir,
     linear_feature_columns=wide_columns,
     dnn_feature_columns=deep_columns,
     dnn_hidden_units=[100, 70, 50, 25])
print('estimator built')

m.fit(input_fn=generate_input_fn(train_file), steps=train_steps)
print('fit done')









    



estimator built
fit done

Evaluate the accuracy of the model



In [13]:

    
results = m.evaluate(input_fn=generate_input_fn(test_file), steps=1)
print('evaluate done')

print('Accuracy: %s' % results['accuracy'])









    



evaluate done
Accuracy: 0.75