Add examples supporting docs in get_started.

PiperOrigin-RevId: 163994110

tensorflow/BUILD

@@ -372,6 +372,7 @@ filegroup(
         "//tensorflow/core/util/tensor_bundle:all_files",
         "//tensorflow/examples/android:all_files",
         "//tensorflow/examples/benchmark:all_files",
+        "//tensorflow/examples/get_started/regression:all_files",
         "//tensorflow/examples/how_tos/reading_data:all_files",
         "//tensorflow/examples/image_retraining:all_files",
         "//tensorflow/examples/label_image:all_files",

tensorflow/examples/get_started/__init__.py

@@ -0,0 +1,19 @@
+# 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 "getting started" examples."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function

tensorflow/examples/get_started/regression/BUILD

@@ -0,0 +1,37 @@
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
+
+py_test(
+    name = "test",
+    size = "medium",
+    srcs = [
+        "dnn_regression.py",
+        "imports85.py",
+        "linear_regression.py",
+        "linear_regression_categorical.py",
+        "test.py",
+    ],
+    srcs_version = "PY2AND3",
+    tags = [
+        "manual",
+        "notap",
+    ],
+    deps = [
+        "//tensorflow:tensorflow_py",
+        "//third_party/py/numpy",
+        "//third_party/py/pandas",
+    ],
+)

tensorflow/examples/get_started/regression/__init__.py

@@ -0,0 +1,20 @@
+# 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

tensorflow/examples/get_started/regression/dnn_regression.py

@@ -0,0 +1,91 @@
+# 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
+
+
+def main(argv):
+  """Builds, trains, and evaluates the model."""
+  assert len(argv) == 1
+  (x_train, y_train), (x_test, y_test) = imports85.load_data()
+
+  # Build the training input_fn.
+  input_train = tf.estimator.inputs.pandas_input_fn(
+      x=x_train, y=y_train, num_epochs=None, shuffle=True)
+
+  # Build the validation input_fn.
+  input_test = tf.estimator.inputs.pandas_input_fn(
+      x=x_test, y=y_test, shuffle=True)
+
+  # 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(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)

tensorflow/examples/get_started/regression/imports85.py

@@ -0,0 +1,107 @@
+# 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 pandas as pd
+import tensorflow as tf
+
+header = collections.OrderedDict([
+    ("symboling", np.int32),
+    ("normalized-losses", np.float32),
+    ("make", str),
+    ("fuel-type", str),
+    ("aspiration", str),
+    ("num-of-doors", str),
+    ("body-style", str),
+    ("drive-wheels", str),
+    ("engine-location", str),
+    ("wheel-base", np.float32),
+    ("length", np.float32),
+    ("width", np.float32),
+    ("height", np.float32),
+    ("curb-weight", np.float32),
+    ("engine-type", str),
+    ("num-of-cylinders", str),
+    ("engine-size", np.float32),
+    ("fuel-system", str),
+    ("bore", np.float32),
+    ("stroke", np.float32),
+    ("compression-ratio", np.float32),
+    ("horsepower", np.float32),
+    ("peak-rpm", np.float32),
+    ("city-mpg", np.float32),
+    ("highway-mpg", np.float32),
+    ("price", np.float32)
+])  # pyformat: disable
+
+
+def raw():
+  """Get the imports85 data and load it as a pd.DataFrame."""
+  url = "https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data"  # pylint: disable=line-too-long
+  # Download and cache the data.
+  path = tf.contrib.keras.utils.get_file(url.split("/")[-1], url)
+
+  # Load the CSV data into a pandas dataframe.
+  df = pd.read_csv(path, names=header.keys(), dtype=header, na_values="?")
+
+  return df
+
+
+def load_data(y_name="price", train_fraction=0.7, seed=None):
+  """Returns the imports85 shuffled and split into train and test subsets.
+
+  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()
+
+  # 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)

tensorflow/examples/get_started/regression/linear_regression.py

@@ -0,0 +1,96 @@
+# 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 using the LinearRegressor Estimator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+import imports85  # pylint: disable=g-bad-import-order
+
+STEPS = 1000
+
+
+def main(argv):
+  """Builds, trains, and evaluates the model."""
+  assert len(argv) == 1
+  (x_train, y_train), (x_test, y_test) = imports85.load_data()
+
+  # Build the training input_fn.
+  input_train = tf.estimator.inputs.pandas_input_fn(
+      x=x_train,
+      y=y_train,
+      # Setting `num_epochs` to `None` lets the `inpuf_fn` generate data
+      # indefinitely, leaving the call to `Estimator.train` in control.
+      num_epochs=None,
+      shuffle=True)
+
+  # Build the validation input_fn.
+  input_test = tf.estimator.inputs.pandas_input_fn(
+      x=x_test, y=y_test, shuffle=True)
+
+  feature_columns = [
+      # "curb-weight" and "highway-mpg" are numeric columns.
+      tf.feature_column.numeric_column(key="curb-weight"),
+      tf.feature_column.numeric_column(key="highway-mpg"),
+  ]
+
+  # 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(average_loss**0.5))
+
+  # Run the model in prediction mode.
+  input_dict = {
+      "curb-weight": np.array([2000, 3000]),
+      "highway-mpg": np.array([30, 40])
+  }
+  predict_input_fn = tf.estimator.inputs.numpy_input_fn(
+      input_dict, shuffle=False)
+  predict_results = model.predict(input_fn=predict_input_fn)
+
+  # Print the prediction results.
+  print("\nPrediction results:")
+  for i, prediction in enumerate(predict_results):
+    msg = ("Curb weight: {: 4d}lbs, "
+           "Highway: {: 0d}mpg, "
+           "Prediction: ${: 9.2f}")
+    msg = msg.format(input_dict["curb-weight"][i], input_dict["highway-mpg"][i],
+                     prediction["predictions"][0])
+
+    print("    " + msg)
+  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)

tensorflow/examples/get_started/regression/linear_regression_categorical.py

@@ -0,0 +1,101 @@
+# 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
+
+
+def main(argv):
+  """Builds, trains, and evaluates the model."""
+  assert len(argv) == 1
+  (x_train, y_train), (x_test, y_test) = imports85.load_data()
+
+  # Build the training input_fn.
+  input_train = tf.estimator.inputs.pandas_input_fn(
+      x=x_train,
+      y=y_train,
+      # Setting `num_epochs` to `None` lets the `inpuf_fn` generate data
+      # indefinitely, leaving the call to `Estimator.train` in control.
+      num_epochs=None,
+      shuffle=True)
+
+  # Build the validation input_fn.
+  input_test = tf.estimator.inputs.pandas_input_fn(
+      x=x_test, y=y_test, shuffle=True)
+
+  # 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 categort 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(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)

tensorflow/examples/get_started/regression/test.py

@@ -0,0 +1,73 @@
+# 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 iteraton."""
+
+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
+
+import tensorflow.examples.get_started.regression.dnn_regression as dnn_regression         # pylint: disable=g-bad-import-order,g-import-not-at-top
+import tensorflow.examples.get_started.regression.linear_regression as linear_regression
+import tensorflow.examples.get_started.regression.linear_regression_categorical as linear_regression_categorical
+
+from tensorflow.python.platform import googletest
+from tensorflow.python.platform import test
+
+
+def four_lines():
+  # pylint: disable=line-too-long
+  text = StringIO("""
+      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
+
+  return pd.read_csv(text, names=imports85.header.keys(),
+                     dtype=imports85.header, na_values='?')
+
+
+class RegressionTest(googletest.TestCase):
+  """Test the regression examples in this directory."""
+
+  @test.mock.patch.dict(imports85.__dict__, {'raw': four_lines})
+  @test.mock.patch.dict(linear_regression.__dict__, {'STEPS': 1})
+  @test.mock.patch.dict(sys.modules, {'imports85': imports85})
+  def test_linear_regression(self):
+    linear_regression.main([])
+
+  @test.mock.patch.dict(imports85.__dict__, {'raw': four_lines})
+  @test.mock.patch.dict(linear_regression_categorical.__dict__, {'STEPS': 1})
+  @test.mock.patch.dict(sys.modules, {'imports85': imports85})
+  def test_linear_regression_categorical(self):
+    linear_regression_categorical.main([])
+
+  @test.mock.patch.dict(imports85.__dict__, {'raw': four_lines})
+  @test.mock.patch.dict(dnn_regression.__dict__, {'STEPS': 1})
+  @test.mock.patch.dict(sys.modules, {'imports85': imports85})
+  def test_dnn_regression(self):
+    dnn_regression.main([])
+
+
+if __name__ == '__main__':
+  googletest.main()