diff --git a/tensorflow/examples/get_started/regression/BUILD b/tensorflow/examples/get_started/regression/BUILD
deleted file mode 100644
index c04934b7d6d..00000000000
--- a/tensorflow/examples/get_started/regression/BUILD
+++ /dev/null
@@ -1,27 +0,0 @@
-package(
-    licenses = ["notice"],  # Apache 2.0
-)
-
-exports_files(["LICENSE"])
-
-py_test(
-    name = "test",
-    size = "medium",
-    srcs = [
-        "custom_regression.py",
-        "dnn_regression.py",
-        "imports85.py",
-        "linear_regression_categorical.py",
-        "test.py",
-    ],
-    python_version = "PY2",
-    srcs_version = "PY2AND3",
-    tags = [
-        "manual",
-        "notap",
-    ],
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//third_party/py/numpy",
-    ],
-)
diff --git a/tensorflow/examples/get_started/regression/__init__.py b/tensorflow/examples/get_started/regression/__init__.py
deleted file mode 100644
index b81f4789f58..00000000000
--- a/tensorflow/examples/get_started/regression/__init__.py
+++ /dev/null
@@ -1,20 +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.
-# ==============================================================================
-
-"""A collection of regression examples using `Estimators`."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
diff --git a/tensorflow/examples/get_started/regression/custom_regression.py b/tensorflow/examples/get_started/regression/custom_regression.py
deleted file mode 100644
index 7b7cbb78666..00000000000
--- a/tensorflow/examples/get_started/regression/custom_regression.py
+++ /dev/null
@@ -1,162 +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.
-# ==============================================================================
-"""Regression using the DNNRegressor Estimator."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-import imports85  # pylint: disable=g-bad-import-order
-
-STEPS = 1000
-PRICE_NORM_FACTOR = 1000
-
-
-def my_dnn_regression_fn(features, labels, mode, params):
-  """A model function implementing DNN regression for a custom Estimator."""
-
-  # Extract the input into a dense layer, according to the feature_columns.
-  top = tf.feature_column.input_layer(features, params["feature_columns"])
-
-  # Iterate over the "hidden_units" list of layer sizes, default is [20].
-  for units in params.get("hidden_units", [20]):
-    # Add a hidden layer, densely connected on top of the previous layer.
-    top = tf.layers.dense(inputs=top, units=units, activation=tf.nn.relu)
-
-  # Connect a linear output layer on top.
-  output_layer = tf.layers.dense(inputs=top, units=1)
-
-  # Reshape the output layer to a 1-dim Tensor to return predictions
-  predictions = tf.squeeze(output_layer, 1)
-
-  if mode == tf.estimator.ModeKeys.PREDICT:
-    # In `PREDICT` mode we only need to return predictions.
-    return tf.estimator.EstimatorSpec(
-        mode=mode, predictions={"price": predictions})
-
-  # Calculate loss using mean squared error
-  average_loss = tf.losses.mean_squared_error(labels, predictions)
-
-  # Pre-made estimators use the total_loss instead of the average,
-  # so report total_loss for compatibility.
-  batch_size = tf.shape(labels)[0]
-  total_loss = tf.to_float(batch_size) * average_loss
-
-  if mode == tf.estimator.ModeKeys.TRAIN:
-    optimizer = params.get("optimizer", tf.train.AdamOptimizer)
-    optimizer = optimizer(params.get("learning_rate", None))
-    train_op = optimizer.minimize(
-        loss=average_loss, global_step=tf.train.get_global_step())
-
-    return tf.estimator.EstimatorSpec(
-        mode=mode, loss=total_loss, train_op=train_op)
-
-  # In evaluation mode we will calculate evaluation metrics.
-  assert mode == tf.estimator.ModeKeys.EVAL
-
-  # Calculate root mean squared error
-  rmse = tf.metrics.root_mean_squared_error(labels, predictions)
-
-  # Add the rmse to the collection of evaluation metrics.
-  eval_metrics = {"rmse": rmse}
-
-  return tf.estimator.EstimatorSpec(
-      mode=mode,
-      # Report sum of error for compatibility with pre-made estimators
-      loss=total_loss,
-      eval_metric_ops=eval_metrics)
-
-
-def main(argv):
-  """Builds, trains, and evaluates the model."""
-  assert len(argv) == 1
-  (train, test) = imports85.dataset()
-
-  # Switch the labels to units of thousands for better convergence.
-  def normalize_price(features, labels):
-    return features, labels / PRICE_NORM_FACTOR
-
-  train = train.map(normalize_price)
-  test = test.map(normalize_price)
-
-  # Build the training input_fn.
-  def input_train():
-    return (
-        # Shuffling with a buffer larger than the data set ensures
-        # that the examples are well mixed.
-        train.shuffle(1000).batch(128)
-        # Repeat forever
-        .repeat())
-
-  # Build the validation input_fn.
-  def input_test():
-    return test.shuffle(1000).batch(128)
-
-  # The first way assigns a unique weight to each category. To do this you must
-  # specify the category's vocabulary (values outside this specification will
-  # receive a weight of zero). Here we specify the vocabulary using a list of
-  # options. The vocabulary can also be specified with a vocabulary file (using
-  # `categorical_column_with_vocabulary_file`). For features covering a
-  # range of positive integers use `categorical_column_with_identity`.
-  body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
-  body_style = tf.feature_column.categorical_column_with_vocabulary_list(
-      key="body-style", vocabulary_list=body_style_vocab)
-  make = tf.feature_column.categorical_column_with_hash_bucket(
-      key="make", hash_bucket_size=50)
-
-  feature_columns = [
-      tf.feature_column.numeric_column(key="curb-weight"),
-      tf.feature_column.numeric_column(key="highway-mpg"),
-      # Since this is a DNN model, convert categorical columns from sparse
-      # to dense.
-      # Wrap them in an `indicator_column` to create a
-      # one-hot vector from the input.
-      tf.feature_column.indicator_column(body_style),
-      # Or use an `embedding_column` to create a trainable vector for each
-      # index.
-      tf.feature_column.embedding_column(make, dimension=3),
-  ]
-
-  # Build a custom Estimator, using the model_fn.
-  # `params` is passed through to the `model_fn`.
-  model = tf.estimator.Estimator(
-      model_fn=my_dnn_regression_fn,
-      params={
-          "feature_columns": feature_columns,
-          "learning_rate": 0.001,
-          "optimizer": tf.train.AdamOptimizer,
-          "hidden_units": [20, 20]
-      })
-
-  # Train the model.
-  model.train(input_fn=input_train, steps=STEPS)
-
-  # Evaluate how the model performs on data it has not yet seen.
-  eval_result = model.evaluate(input_fn=input_test)
-
-  # Print the Root Mean Square Error (RMSE).
-  print("\n" + 80 * "*")
-  print("\nRMS error for the test set: ${:.0f}"
-        .format(PRICE_NORM_FACTOR * eval_result["rmse"]))
-
-  print()
-
-
-if __name__ == "__main__":
-  # The Estimator periodically generates "INFO" logs; make these logs visible.
-  tf.logging.set_verbosity(tf.logging.INFO)
-  tf.app.run(main=main)
diff --git a/tensorflow/examples/get_started/regression/dnn_regression.py b/tensorflow/examples/get_started/regression/dnn_regression.py
deleted file mode 100644
index 94669a5082b..00000000000
--- a/tensorflow/examples/get_started/regression/dnn_regression.py
+++ /dev/null
@@ -1,105 +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.
-# ==============================================================================
-"""Regression using the DNNRegressor Estimator."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-import imports85  # pylint: disable=g-bad-import-order
-
-STEPS = 5000
-PRICE_NORM_FACTOR = 1000
-
-
-def main(argv):
-  """Builds, trains, and evaluates the model."""
-  assert len(argv) == 1
-  (train, test) = imports85.dataset()
-
-  # Switch the labels to units of thousands for better convergence.
-  def normalize_price(features, labels):
-    return features, labels / PRICE_NORM_FACTOR
-
-  train = train.map(normalize_price)
-  test = test.map(normalize_price)
-
-  # Build the training input_fn.
-  def input_train():
-    return (
-        # Shuffling with a buffer larger than the data set ensures
-        # that the examples are well mixed.
-        train.shuffle(1000).batch(128)
-        # Repeat forever
-        .repeat())
-
-  # Build the validation input_fn.
-  def input_test():
-    return test.shuffle(1000).batch(128)
-
-  # The first way assigns a unique weight to each category. To do this you must
-  # specify the category's vocabulary (values outside this specification will
-  # receive a weight of zero). Here we specify the vocabulary using a list of
-  # options. The vocabulary can also be specified with a vocabulary file (using
-  # `categorical_column_with_vocabulary_file`). For features covering a
-  # range of positive integers use `categorical_column_with_identity`.
-  body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
-  body_style = tf.feature_column.categorical_column_with_vocabulary_list(
-      key="body-style", vocabulary_list=body_style_vocab)
-  make = tf.feature_column.categorical_column_with_hash_bucket(
-      key="make", hash_bucket_size=50)
-
-  feature_columns = [
-      tf.feature_column.numeric_column(key="curb-weight"),
-      tf.feature_column.numeric_column(key="highway-mpg"),
-      # Since this is a DNN model, convert categorical columns from sparse
-      # to dense.
-      # Wrap them in an `indicator_column` to create a
-      # one-hot vector from the input.
-      tf.feature_column.indicator_column(body_style),
-      # Or use an `embedding_column` to create a trainable vector for each
-      # index.
-      tf.feature_column.embedding_column(make, dimension=3),
-  ]
-
-  # Build a DNNRegressor, with 2x20-unit hidden layers, with the feature columns
-  # defined above as input.
-  model = tf.estimator.DNNRegressor(
-      hidden_units=[20, 20], feature_columns=feature_columns)
-
-  # Train the model.
-  model.train(input_fn=input_train, steps=STEPS)
-
-  # Evaluate how the model performs on data it has not yet seen.
-  eval_result = model.evaluate(input_fn=input_test)
-
-  # The evaluation returns a Python dictionary. The "average_loss" key holds the
-  # Mean Squared Error (MSE).
-  average_loss = eval_result["average_loss"]
-
-  # Convert MSE to Root Mean Square Error (RMSE).
-  print("\n" + 80 * "*")
-  print("\nRMS error for the test set: ${:.0f}"
-        .format(PRICE_NORM_FACTOR * average_loss**0.5))
-
-  print()
-
-
-if __name__ == "__main__":
-  # The Estimator periodically generates "INFO" logs; make these logs visible.
-  tf.logging.set_verbosity(tf.logging.INFO)
-  tf.app.run(main=main)
diff --git a/tensorflow/examples/get_started/regression/imports85.py b/tensorflow/examples/get_started/regression/imports85.py
deleted file mode 100644
index 4fdaceea9af..00000000000
--- a/tensorflow/examples/get_started/regression/imports85.py
+++ /dev/null
@@ -1,204 +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.
-# ==============================================================================
-"""A dataset loader for imports85.data."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-
-import numpy as np
-import tensorflow as tf
-
-try:
-  import pandas as pd  # pylint: disable=g-import-not-at-top
-except ImportError:
-  pass
-
-
-URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data"
-
-# Order is important for the csv-readers, so we use an OrderedDict here.
-defaults = collections.OrderedDict([
-    ("symboling", [0]),
-    ("normalized-losses", [0.0]),
-    ("make", [""]),
-    ("fuel-type", [""]),
-    ("aspiration", [""]),
-    ("num-of-doors", [""]),
-    ("body-style", [""]),
-    ("drive-wheels", [""]),
-    ("engine-location", [""]),
-    ("wheel-base", [0.0]),
-    ("length", [0.0]),
-    ("width", [0.0]),
-    ("height", [0.0]),
-    ("curb-weight", [0.0]),
-    ("engine-type", [""]),
-    ("num-of-cylinders", [""]),
-    ("engine-size", [0.0]),
-    ("fuel-system", [""]),
-    ("bore", [0.0]),
-    ("stroke", [0.0]),
-    ("compression-ratio", [0.0]),
-    ("horsepower", [0.0]),
-    ("peak-rpm", [0.0]),
-    ("city-mpg", [0.0]),
-    ("highway-mpg", [0.0]),
-    ("price", [0.0])
-])  # pyformat: disable
-
-
-types = collections.OrderedDict((key, type(value[0]))
-                                for key, value in defaults.items())
-
-
-def _get_imports85():
-  path = tf.contrib.keras.utils.get_file(URL.split("/")[-1], URL)
-  return path
-
-
-def dataset(y_name="price", train_fraction=0.7):
-  """Load the imports85 data as a (train,test) pair of `Dataset`.
-
-  Each dataset generates (features_dict, label) pairs.
-
-  Args:
-    y_name: The name of the column to use as the label.
-    train_fraction: A float, the fraction of data to use for training. The
-        remainder will be used for evaluation.
-  Returns:
-    A (train,test) pair of `Datasets`
-  """
-  # Download and cache the data
-  path = _get_imports85()
-
-  # Define how the lines of the file should be parsed
-  def decode_line(line):
-    """Convert a csv line into a (features_dict,label) pair."""
-    # Decode the line to a tuple of items based on the types of
-    # csv_header.values().
-    items = tf.decode_csv(line, list(defaults.values()))
-
-    # Convert the keys and items to a dict.
-    pairs = zip(defaults.keys(), items)
-    features_dict = dict(pairs)
-
-    # Remove the label from the features_dict
-    label = features_dict.pop(y_name)
-
-    return features_dict, label
-
-  def has_no_question_marks(line):
-    """Returns True if the line of text has no question marks."""
-    # split the line into an array of characters
-    chars = tf.string_split(line[tf.newaxis], "").values
-    # for each character check if it is a question mark
-    is_question = tf.equal(chars, "?")
-    any_question = tf.reduce_any(is_question)
-    no_question = ~any_question
-
-    return no_question
-
-  def in_training_set(line):
-    """Returns a boolean tensor, true if the line is in the training set."""
-    # If you randomly split the dataset you won't get the same split in both
-    # sessions if you stop and restart training later. Also a simple
-    # random split won't work with a dataset that's too big to `.cache()` as
-    # we are doing here.
-    num_buckets = 1000000
-    bucket_id = tf.string_to_hash_bucket_fast(line, num_buckets)
-    # Use the hash bucket id as a random number that's deterministic per example
-    return bucket_id < int(train_fraction * num_buckets)
-
-  def in_test_set(line):
-    """Returns a boolean tensor, true if the line is in the training set."""
-    # Items not in the training set are in the test set.
-    # This line must use `~` instead of `not` because `not` only works on python
-    # booleans but we are dealing with symbolic tensors.
-    return ~in_training_set(line)
-
-  base_dataset = (
-      tf.data
-      # Get the lines from the file.
-      .TextLineDataset(path)
-      # drop lines with question marks.
-      .filter(has_no_question_marks))
-
-  train = (base_dataset
-           # Take only the training-set lines.
-           .filter(in_training_set)
-           # Decode each line into a (features_dict, label) pair.
-           .map(decode_line)
-           # Cache data so you only decode the file once.
-           .cache())
-
-  # Do the same for the test-set.
-  test = (base_dataset.filter(in_test_set).cache().map(decode_line))
-
-  return train, test
-
-
-def raw_dataframe():
-  """Load the imports85 data as a pd.DataFrame."""
-  # Download and cache the data
-  path = _get_imports85()
-
-  # Load it into a pandas dataframe
-  df = pd.read_csv(path, names=types.keys(), dtype=types, na_values="?")
-
-  return df
-
-
-def load_data(y_name="price", train_fraction=0.7, seed=None):
-  """Get the imports85 data set.
-
-  A description of the data is available at:
-    https://archive.ics.uci.edu/ml/datasets/automobile
-
-  The data itself can be found at:
-    https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data
-
-  Args:
-    y_name: the column to return as the label.
-    train_fraction: the fraction of the dataset to use for training.
-    seed: The random seed to use when shuffling the data. `None` generates a
-      unique shuffle every run.
-  Returns:
-    a pair of pairs where the first pair is the training data, and the second
-    is the test data:
-    `(x_train, y_train), (x_test, y_test) = get_imports85_dataset(...)`
-    `x` contains a pandas DataFrame of features, while `y` contains the label
-    array.
-  """
-  # Load the raw data columns.
-  data = raw_dataframe()
-
-  # Delete rows with unknowns
-  data = data.dropna()
-
-  # Shuffle the data
-  np.random.seed(seed)
-
-  # Split the data into train/test subsets.
-  x_train = data.sample(frac=train_fraction, random_state=seed)
-  x_test = data.drop(x_train.index)
-
-  # Extract the label from the features dataframe.
-  y_train = x_train.pop(y_name)
-  y_test = x_test.pop(y_name)
-
-  return (x_train, y_train), (x_test, y_test)
diff --git a/tensorflow/examples/get_started/regression/linear_regression_categorical.py b/tensorflow/examples/get_started/regression/linear_regression_categorical.py
deleted file mode 100644
index 5312272a959..00000000000
--- a/tensorflow/examples/get_started/regression/linear_regression_categorical.py
+++ /dev/null
@@ -1,110 +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.
-# ==============================================================================
-"""Linear regression with categorical features."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-import imports85  # pylint: disable=g-bad-import-order
-
-STEPS = 1000
-PRICE_NORM_FACTOR = 1000
-
-
-def main(argv):
-  """Builds, trains, and evaluates the model."""
-  assert len(argv) == 1
-  (train, test) = imports85.dataset()
-
-  # Switch the labels to units of thousands for better convergence.
-  def normalize_price(features, labels):
-    return features, labels / PRICE_NORM_FACTOR
-
-  train = train.map(normalize_price)
-  test = test.map(normalize_price)
-
-  # Build the training input_fn.
-  def input_train():
-    return (
-        # Shuffling with a buffer larger than the data set ensures
-        # that the examples are well mixed.
-        train.shuffle(1000).batch(128)
-        # Repeat forever
-        .repeat())
-
-  # Build the validation input_fn.
-  def input_test():
-    return test.shuffle(1000).batch(128)
-
-  # The following code demonstrates two of the ways that `feature_columns` can
-  # be used to build a model with categorical inputs.
-
-  # The first way assigns a unique weight to each category. To do this, you must
-  # specify the category's vocabulary (values outside this specification will
-  # receive a weight of zero).
-  # Alternatively, you can define the vocabulary in a file (by calling
-  # `categorical_column_with_vocabulary_file`) or as a range of positive
-  # integers (by calling `categorical_column_with_identity`)
-  body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
-  body_style_column = tf.feature_column.categorical_column_with_vocabulary_list(
-      key="body-style", vocabulary_list=body_style_vocab)
-
-  # The second way, appropriate for an unspecified vocabulary, is to create a
-  # hashed column. It will create a fixed length list of weights, and
-  # automatically assign each input category to a weight. Due to the
-  # pseudo-randomness of the process, some weights may be shared between
-  # categories, while others will remain unused.
-  make_column = tf.feature_column.categorical_column_with_hash_bucket(
-      key="make", hash_bucket_size=50)
-
-  feature_columns = [
-      # This model uses the same two numeric features as `linear_regressor.py`
-      tf.feature_column.numeric_column(key="curb-weight"),
-      tf.feature_column.numeric_column(key="highway-mpg"),
-      # This model adds two categorical colums that will adjust the price based
-      # on "make" and "body-style".
-      body_style_column,
-      make_column,
-  ]
-
-  # Build the Estimator.
-  model = tf.estimator.LinearRegressor(feature_columns=feature_columns)
-
-  # Train the model.
-  # By default, the Estimators log output every 100 steps.
-  model.train(input_fn=input_train, steps=STEPS)
-
-  # Evaluate how the model performs on data it has not yet seen.
-  eval_result = model.evaluate(input_fn=input_test)
-
-  # The evaluation returns a Python dictionary. The "average_loss" key holds the
-  # Mean Squared Error (MSE).
-  average_loss = eval_result["average_loss"]
-
-  # Convert MSE to Root Mean Square Error (RMSE).
-  print("\n" + 80 * "*")
-  print("\nRMS error for the test set: ${:.0f}"
-        .format(PRICE_NORM_FACTOR * average_loss**0.5))
-
-  print()
-
-
-if __name__ == "__main__":
-  # The Estimator periodically generates "INFO" logs; make these logs visible.
-  tf.logging.set_verbosity(tf.logging.INFO)
-  tf.app.run(main=main)
diff --git a/tensorflow/examples/get_started/regression/test.py b/tensorflow/examples/get_started/regression/test.py
deleted file mode 100644
index 1c37e4a671b..00000000000
--- a/tensorflow/examples/get_started/regression/test.py
+++ /dev/null
@@ -1,89 +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.
-# ==============================================================================
-"""A simple smoke test that runs these examples for 1 training iteration."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import sys
-
-import pandas as pd
-
-from six.moves import StringIO
-
-import tensorflow.examples.get_started.regression.imports85 as imports85
-
-sys.modules["imports85"] = imports85
-
-# pylint: disable=g-bad-import-order,g-import-not-at-top
-import tensorflow.data as data
-
-import tensorflow.examples.get_started.regression.dnn_regression as dnn_regression
-import tensorflow.examples.get_started.regression.linear_regression_categorical as linear_regression_categorical
-import tensorflow.examples.get_started.regression.custom_regression as custom_regression
-
-from tensorflow.python.platform import googletest
-from tensorflow.python.platform import test
-# pylint: disable=g-bad-import-order,g-import-not-at-top
-
-
-# pylint: disable=line-too-long
-FOUR_LINES = "\n".join([
-    "1,?,alfa-romero,gas,std,two,hatchback,rwd,front,94.50,171.20,65.50,52.40,2823,ohcv,six,152,mpfi,2.68,3.47,9.00,154,5000,19,26,16500",
-    "2,164,audi,gas,std,four,sedan,fwd,front,99.80,176.60,66.20,54.30,2337,ohc,four,109,mpfi,3.19,3.40,10.00,102,5500,24,30,13950",
-    "2,164,audi,gas,std,four,sedan,4wd,front,99.40,176.60,66.40,54.30,2824,ohc,five,136,mpfi,3.19,3.40,8.00,115,5500,18,22,17450",
-    "2,?,audi,gas,std,two,sedan,fwd,front,99.80,177.30,66.30,53.10,2507,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,25,15250",
-])
-
-# pylint: enable=line-too-long
-
-
-def four_lines_dataframe():
-  text = StringIO(FOUR_LINES)
-
-  return pd.read_csv(
-      text, names=imports85.types.keys(), dtype=imports85.types, na_values="?")
-
-
-def four_lines_dataset(*args, **kwargs):
-  del args, kwargs
-  return data.Dataset.from_tensor_slices(FOUR_LINES.split("\n"))
-
-
-class RegressionTest(googletest.TestCase):
-  """Test the regression examples in this directory."""
-
-  @test.mock.patch.dict(data.__dict__, {"TextLineDataset": four_lines_dataset})
-  @test.mock.patch.dict(imports85.__dict__, {"_get_imports85": (lambda: None)})
-  @test.mock.patch.dict(linear_regression_categorical.__dict__, {"STEPS": 1})
-  def test_linear_regression_categorical(self):
-    linear_regression_categorical.main([""])
-
-  @test.mock.patch.dict(data.__dict__, {"TextLineDataset": four_lines_dataset})
-  @test.mock.patch.dict(imports85.__dict__, {"_get_imports85": (lambda: None)})
-  @test.mock.patch.dict(dnn_regression.__dict__, {"STEPS": 1})
-  def test_dnn_regression(self):
-    dnn_regression.main([""])
-
-  @test.mock.patch.dict(data.__dict__, {"TextLineDataset": four_lines_dataset})
-  @test.mock.patch.dict(imports85.__dict__, {"_get_imports85": (lambda: None)})
-  @test.mock.patch.dict(custom_regression.__dict__, {"STEPS": 1})
-  def test_custom_regression(self):
-    custom_regression.main([""])
-
-
-if __name__ == "__main__":
-  googletest.main()