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.
  # ==============================================================================
  """Multi-output tests."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import random
  
  import numpy as np
  
  import tensorflow as tf
  from tensorflow.contrib.learn.python import learn
  from tensorflow.contrib.learn.python.learn.estimators._sklearn import mean_squared_error
  
  
  class MultiOutputTest(tf.test.TestCase):
    """Multi-output tests."""
  
    def testMultiRegression(self):
      random.seed(42)
      rng = np.random.RandomState(1)
      x = np.sort(200 * rng.rand(100, 1) - 100, axis=0)
      y = np.array([np.pi * np.sin(x).ravel(), np.pi * np.cos(x).ravel()]).T
      regressor = learn.TensorFlowLinearRegressor(
          feature_columns=learn.infer_real_valued_columns_from_input(x),
          learning_rate=0.01, target_dimension=2)
      regressor.fit(x, y)
      score = mean_squared_error(regressor.predict(x), y)
      self.assertLess(score, 10, "Failed with score = {0}".format(score))
  
  
  if __name__ == "__main__":
    tf.test.main()
  