title: How to train the network only on one output when there are multiple outputs?
body: <p>I am using a multiple output model in Keras</p>
  
  <pre><code>model1 = Model(input=x, output=[y2, y3])
  
  model1.compile((optimizer='sgd', loss=cutom_loss_function)
  </code></pre>
  
  <p>my <code>custom_loss</code> function is</p>
  
  <pre><code>def custom_loss(y_true, y_pred):
     y2_pred = y_pred[0]
     y2_true = y_true[0]
  
     loss = K.mean(K.square(y2_true - y2_pred), axis=-1)
     return loss
  </code></pre>
  
  <p>I only want to train the network on output <code>y2</code>.</p>
  
  <p>What is the shape/structure of the <code>y_pred</code> and <code>y_true</code> argument in loss function when multiple outputs are used?
  Can I access them as above? Is it <code>y_pred[0]</code> or <code>y_pred[:,0]</code>?</p>