title: Use custom loss value while training in tensorflow
body: <p>I would like to train my neural network using a custom loss value of my own. Therefore, I would like to perform feed forward propagation for one mini batch to store the activations in the memory, and then perform back propagation using a my own loss value. This is to be done using tensorflow. </p>
  
  <p>Finally, I need to do something like:</p>
  
  <pre><code>sess.run(optimizer, feed_dict={x: training_data, loss: my_custom_loss_value}
  </code></pre>
  
  <p>Is that possible? I am assuming that the optimizer depends on the loss which by itself depends on the input. Therefore, I want to inputs to be fed into the graph, but I want to use my value for the loss.</p>