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.
  # ==============================================================================
  """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)
  