title: Loss reduction in canned TF estimators
body: <p>I use a TensorFlow canned estimator (<code>LinearClassifier</code>) to predict game actions from situations favourizing best scores. Scores are included in <code>train_data</code> and used as weight and passed as weight column in the estimator.</p>
  
  <p>I know weight values are multiplicated with loss (MSE in this case) but I want to know if loss minimization is done or if I have to define optimizer as:</p>
  
  <pre><code>optimizer=tf.train.AdamOptimizer(learning_rate=0.001, beta1= 0.9,beta2=0.99, epsilon = 1e-08,use_locking=False).minimize(loss),
  </code></pre>
  
  
  
  <pre><code>model = tf.estimator.LinearClassifier(feature_columns=feature_columns,
                                      optimizer=tf.train.AdamOptimizer(learning_rate=0.001, beta1= 0.9,beta2=0.99, epsilon = 1e-08,use_locking=False),
                                      weight_column=weights,
  #                                    dropout=0.1,
  #                                    activation_fn=tf.nn.softmax,
                                      n_classes=10,
                                      label_vocabulary=Action_vocab,
                                      model_dir='./Models/ActionPlayerModel20/',
                                      loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE,
                                      config=tf.estimator.RunConfig().replace(save_summary_steps=10))
  </code></pre>