title: How to set parts of positive samples weight in TensorFlow for binary classfication
body: <p>I want to set the same weight for parts of positive samples. However,<code>tf.nn.weighted_cross_entropy_with_logits</code> can only set the weight for all positive samples in my opinion. </p>
  
  <p>for example, in the ctr predicition, I want set 10 weights for the order samples, and the weight of click samples and the unclick sample is still 1.</p>
  
  <p>Here is my unweighted code</p>
  
  <pre><code>def my_model(features, labels, mode, params):
      net = tf.feature_column.input_layer(features, params['feature_columns'])
      for units in params['hidden_units']:
         net = tf.layers.dense(net, units=units, activation=params["activation"])  
      logits = tf.layers.dense(net, params['n_classes'], activation=None)
  
      predicted_classes = tf.argmax(logits, 1)
      if mode == tf.estimator.ModeKeys.PREDICT:
         predictions = {
          'class_ids': predicted_classes, #predicted_classes[:, tf.newaxis],
          'probabilities': tf.nn.softmax(logits),
          'logits': logits,
         }
         return tf.estimator.EstimatorSpec(mode, predictions=predictions)
  
      loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
  
      metrics = {'auc': tf.metrics.auc(labels=labels, predictions=tf.nn.softmax(logits)[:,1])}
  
      if mode == tf.estimator.ModeKeys.EVAL:
         return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics)
  
      assert mode == tf.estimator.ModeKeys.TRAIN
      optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
      train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) 
      return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
  </code></pre>
  
  <h2>Train</h2>
  
  <pre><code>train_input_fn = tf.estimator.inputs.pandas_input_fn(x=data_train, y=data_train_click, batch_size = 1024, num_epochs=1, shuffle=False)
  classifier.train(input_fn=train_input_fn)
  </code></pre>
  
  <p>Here  <code>data_train_click</code> is a Series, which the click samples are 1 and the unclicked samples are 0. And I have a Series named <code>data_train_order</code>, which the order samples are 1 and the others are 0</p>