title: Multi label classification with unbalanced labels
body: <p>I am building multi label classification network.
  My GTs are vectors of length <code>512</code> <code>[0,0,0,1,0,1,0,...,0,0,0,1]</code>
  Most of the time they are <code>zeroes</code>, each vector has about <code>5 ones</code>, and rest are zeros .</p>
  <p>I am thinking to do:</p>
  <p>Use <code>sigmoid</code> for activation for output layer.</p>
  <p>Use <code>binary_crossentropy</code> for loss function.</p>
  <p>But how I can solve the unbalance issue ?
  Network can learn to predict <code>always zeros</code> and still have really low learning loss score.</p>
  <p>How I can make it actually learn to predict ones...</p>