Remove wide_n_deep_tutorial.py in tensorflow/examples/learn in favor of wide_deep.py in the TensorFlow official models

PiperOrigin-RevId: 175728483

4 files changed, 1 insertions, 262 deletions

diff --git a/tensorflow/examples/learn/BUILD b/tensorflow/examples/learn/BUILD
index 23a42a60ba..aba7f600b5 100644
--- a/tensorflow/examples/learn/BUILD
+++ b/tensorflow/examples/learn/BUILD
@@ -114,13 +114,6 @@ py_binary(
 )
 
 py_binary(
-    name = "wide_n_deep_tutorial",
-    srcs = ["wide_n_deep_tutorial.py"],
-    srcs_version = "PY2AND3",
-    deps = ["//tensorflow:tensorflow_py"],
-)
-
-py_binary(
     name = "mnist",
     srcs = ["mnist.py"],
     srcs_version = "PY2AND3",
@@ -153,7 +146,6 @@ sh_test(
         ":text_classification_character_cnn",
         ":text_classification_character_rnn",
         ":text_classification_cnn",
-        ":wide_n_deep_tutorial",
     ],
     tags = [
         "manual",
diff --git a/tensorflow/examples/learn/README.md b/tensorflow/examples/learn/README.md
index 70d9db85ee..b74a8f39d9 100644
--- a/tensorflow/examples/learn/README.md
+++ b/tensorflow/examples/learn/README.md
@@ -23,7 +23,7 @@ processing (`pip install -U pandas`).
 
 ## Specialized Models
 * [Building a Random Forest Model](https://www.tensorflow.org/code/tensorflow/examples/learn/random_forest_mnist.py)
-* [Building a Wide & Deep Model](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py)
+* [Building a Wide & Deep Model](https://github.com/tensorflow/models/tree/master/official/wide_deep/wide_deep.py)
 * [Building a Residual Network Model](https://www.tensorflow.org/code/tensorflow/examples/learn/resnet.py)
 
 ## Text classification
diff --git a/tensorflow/examples/learn/examples_test.sh b/tensorflow/examples/learn/examples_test.sh
index b8763de471..ef5e8a5de2 100755
--- a/tensorflow/examples/learn/examples_test.sh
+++ b/tensorflow/examples/learn/examples_test.sh
@@ -56,4 +56,3 @@ test text_classification_builtin_rnn_model --test_with_fake_data
 test text_classification_character_cnn --test_with_fake_data
 test text_classification_character_rnn --test_with_fake_data
 test text_classification_cnn --test_with_fake_data
-test wide_n_deep_tutorial
diff --git a/tensorflow/examples/learn/wide_n_deep_tutorial.py b/tensorflow/examples/learn/wide_n_deep_tutorial.py
deleted file mode 100644
index 072353392a..0000000000
--- a/tensorflow/examples/learn/wide_n_deep_tutorial.py
+++ /dev/null
@@ -1,252 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Example code for TensorFlow Wide & Deep Tutorial using TF High Level API.
-
-This example uses APIs in Tensorflow 1.4 or above.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import shutil
-import sys
-import tempfile
-
-import pandas as pd
-from six.moves import urllib
-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"
-]
-
-gender = tf.feature_column.categorical_column_with_vocabulary_list(
-    "gender", ["Female", "Male"])
-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)
-native_country = tf.feature_column.categorical_column_with_hash_bucket(
-    "native_country", hash_bucket_size=1000)
-
-# Continuous base 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")
-
-# 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 = [
-    gender, education, marital_status, relationship, workclass, occupation,
-    native_country, 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),
-    tf.feature_column.crossed_column(
-        ["native_country", "occupation"], hash_bucket_size=1000)
-]
-
-deep_columns = [
-    tf.feature_column.indicator_column(workclass),
-    tf.feature_column.indicator_column(education),
-    tf.feature_column.indicator_column(gender),
-    tf.feature_column.indicator_column(relationship),
-    # To show an example of embedding
-    tf.feature_column.embedding_column(native_country, dimension=8),
-    tf.feature_column.embedding_column(occupation, dimension=8),
-    age,
-    education_num,
-    capital_gain,
-    capital_loss,
-    hours_per_week,
-]
-
-
-FLAGS = None
-
-
-def maybe_download(train_data, test_data):
-  """Maybe downloads training data and returns train and test file names."""
-  if train_data:
-    train_file_name = train_data
-  else:
-    train_file = tempfile.NamedTemporaryFile(delete=False)
-    urllib.request.urlretrieve(
-        "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
-        train_file.name)  # pylint: disable=line-too-long
-    train_file_name = train_file.name
-    train_file.close()
-    print("Training data is downloaded to %s" % train_file_name)
-
-  if test_data:
-    test_file_name = test_data
-  else:
-    test_file = tempfile.NamedTemporaryFile(delete=False)
-    urllib.request.urlretrieve(
-        "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test",
-        test_file.name)  # pylint: disable=line-too-long
-    test_file_name = test_file.name
-    test_file.close()
-    print("Test data is downloaded to %s"% test_file_name)
-
-  return train_file_name, test_file_name
-
-
-def build_estimator(model_dir, model_type):
-  """Build an estimator."""
-  if model_type == "wide":
-    m = tf.estimator.LinearClassifier(
-        model_dir=model_dir, feature_columns=base_columns + crossed_columns)
-  elif model_type == "deep":
-    m = tf.estimator.DNNClassifier(
-        model_dir=model_dir,
-        feature_columns=deep_columns,
-        hidden_units=[100, 50])
-  else:
-    m = tf.estimator.DNNLinearCombinedClassifier(
-        model_dir=model_dir,
-        linear_feature_columns=crossed_columns,
-        dnn_feature_columns=deep_columns,
-        dnn_hidden_units=[100, 50])
-  return m
-
-
-def input_fn(data_file, num_epochs, shuffle):
-  """Returns an `input_fn` required by Estimator train/evaluate.
-
-  Args:
-    data_file: The file path to the dataset.
-    num_epochs: Number of epochs to iterate over data. If `None`, `input_fn`
-      will generate infinite stream of data.
-    shuffle: bool, whether to read the data in random order.
-  """
-  df_data = pd.read_csv(
-      tf.gfile.Open(data_file),
-      names=CSV_COLUMNS,
-      skipinitialspace=True,
-      engine="python",
-      skiprows=1)
-  # remove NaN elements
-  df_data = df_data.dropna(how="any", axis=0)
-  labels = df_data["income_bracket"].apply(lambda x: ">50K" in x).astype(int)
-
-  return tf.estimator.inputs.pandas_input_fn(
-      x=df_data,
-      y=labels,
-      batch_size=100,
-      num_epochs=num_epochs,
-      shuffle=shuffle,
-      num_threads=1)
-
-
-def main(_):
-  tf.logging.set_verbosity(tf.logging.INFO)
-
-  train_file_name, test_file_name = maybe_download(FLAGS.train_data,
-                                                   FLAGS.test_data)
-
-  # Specify file path below if want to find the output easily
-  model_dir = FLAGS.model_dir if FLAGS.model_dir else tempfile.mkdtemp()
-
-  estimator = build_estimator(model_dir, FLAGS.model_type)
-
-  # `tf.estimator.TrainSpec`, `tf.estimator.EvalSpec`, and
-  # `tf.estimator.train_and_evaluate` API are available in TF 1.4.
-  train_spec = tf.estimator.TrainSpec(
-      input_fn=input_fn(train_file_name, num_epochs=None, shuffle=True),
-      max_steps=FLAGS.train_steps)
-
-  eval_spec = tf.estimator.EvalSpec(
-      input_fn=input_fn(test_file_name, num_epochs=1, shuffle=False),
-      # set steps to None to run evaluation until all data consumed.
-      steps=None)
-
-  tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
-
-  # Manual cleanup
-  shutil.rmtree(model_dir)
-
-
-if __name__ == "__main__":
-  parser = argparse.ArgumentParser()
-  parser.register("type", "bool", lambda v: v.lower() == "true")
-  parser.add_argument(
-      "--model_dir",
-      type=str,
-      default="",
-      help="Base directory for output models."
-  )
-  parser.add_argument(
-      "--model_type",
-      type=str,
-      default="wide_n_deep",
-      help="Valid model types: {'wide', 'deep', 'wide_n_deep'}."
-  )
-  parser.add_argument(
-      "--train_steps",
-      type=int,
-      default=2000,
-      help="Number of training steps."
-  )
-  parser.add_argument(
-      "--train_data",
-      type=str,
-      default="",
-      help="Path to the training data."
-  )
-  parser.add_argument(
-      "--test_data",
-      type=str,
-      default="",
-      help="Path to the test data."
-  )
-  FLAGS, unparsed = parser.parse_known_args()
-  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)