title: tf.nn.sigmoid_cross_entropy_with_logits companies about arguments from documentation
body: <p>So I have the following model that I am wanting to test out an idea with. I am particularly interested in <a href="https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits" rel="nofollow noreferrer">tf.nn.sigmoid_cross_entropy_with_logits()</a>  because my labels are not mutually exclusive. </p>
  
  <pre><code>import tensorflow as tf
  
  from tensorflow.examples.tutorials.mnist import input_data
  mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
  
  x  = tf.placeholder(tf.float32, shape=[None, 784])
  y_ = tf.placeholder(tf.float32, shape=[None, 10])
  
  w1 = tf.get_variable("w1", shape=[784, 512], initializer=tf.contrib.layers.xavier_initializer())
  b1 = tf.Variable(tf.zeros([512], dtype=tf.float32))
  w2 = tf.Variable(tf.zeros([512, 10], dtype=tf.float32))
  b2 = tf.Variable(tf.zeros([10], dtype=tf.float32))
  
  h = tf.nn.relu(tf.matmul(x, w1) + b1)
  y = tf.matmul(h, w2) + b2
  
  cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_, logits=y)
  train_step = tf.train.AdamOptimizer().minimize(cross_entropy)
  
  with tf.Session() as sess:
  
      sess.run(tf.initialize_all_variables())
      start = time.time()
  
      for i in range(20000):
          batch = mnist.train.next_batch(50)
          train_step.run(feed_dict={x: batch[0], y_: batch[1]})
  </code></pre>
  
  <p>However, I am getting the following error repeatedly, which seems to be contradicting the tensor flow documentation. </p>
  
  <pre><code>Traceback (most recent call last):
  File "mnist_test.py", line 19, in &lt;module&gt;
  cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_, logits=y)
  TypeError: sigmoid_cross_entropy_with_logits() got an unexpected keyword argument 'labels'
  </code></pre>
  
  <p>Please help!!</p>