title: How should I handle input data with nan values in TensorFlow?
body: <p>I have a multilayer perceptron with a sigmoid loss (<code>tf.nn.sigmoid_cross_entropy_with_logits</code>) and an Adam optimizer (<code>tf.train.AdamOptimizer</code>). My input data has several features and some nan feature-values.  When I replace the nan values with 0, I get a result, however, when I do not replace the nan values, I get loss=nan.</p>
  
  <p>What is the best way to handle nan values in TensorFlow, and how can I use my input data with nan values without replacing them with 0?</p>