content: #  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.
  """DNNRegressor with custom input_fn for Housing dataset."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import itertools
  
  import pandas as pd
  import tensorflow as tf
  
  tf.logging.set_verbosity(tf.logging.INFO)
  
  COLUMNS = ["crim", "zn", "indus", "nox", "rm", "age",
             "dis", "tax", "ptratio", "medv"]
  FEATURES = ["crim", "zn", "indus", "nox", "rm",
              "age", "dis", "tax", "ptratio"]
  LABEL = "medv"
  
  
  def get_input_fn(data_set, num_epochs=None, shuffle=True):
    return tf.estimator.inputs.pandas_input_fn(
        x=pd.DataFrame({k: data_set[k].values for k in FEATURES}),
        y=pd.Series(data_set[LABEL].values),
        num_epochs=num_epochs,
        shuffle=shuffle)
  
  
  def main(unused_argv):
    # Load datasets
    training_set = pd.read_csv("boston_train.csv", skipinitialspace=True,
                               skiprows=1, names=COLUMNS)
    test_set = pd.read_csv("boston_test.csv", skipinitialspace=True,
                           skiprows=1, names=COLUMNS)
  
    # Set of 6 examples for which to predict median house values
    prediction_set = pd.read_csv("boston_predict.csv", skipinitialspace=True,
                                 skiprows=1, names=COLUMNS)
  
    # Feature cols
    feature_cols = [tf.feature_column.numeric_column(k) for k in FEATURES]
  
    # Build 2 layer fully connected DNN with 10, 10 units respectively.
    regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols,
                                          hidden_units=[10, 10],
                                          model_dir="/tmp/boston_model")
  
    # Train
    regressor.train(input_fn=get_input_fn(training_set), steps=5000)
  
    # Evaluate loss over one epoch of test_set.
    ev = regressor.evaluate(
        input_fn=get_input_fn(test_set, num_epochs=1, shuffle=False))
    loss_score = ev["loss"]
    print("Loss: {0:f}".format(loss_score))
  
    # Print out predictions over a slice of prediction_set.
    y = regressor.predict(
        input_fn=get_input_fn(prediction_set, num_epochs=1, shuffle=False))
    # .predict() returns an iterator of dicts; convert to a list and print
    # predictions
    predictions = list(p["predictions"] for p in itertools.islice(y, 6))
    print("Predictions: {}".format(str(predictions)))
  
  if __name__ == "__main__":
    tf.app.run()
  