title: How to interpret the summary of a keras model with multiple outputs?
body: <p>Given the following the code:</p>
  
  <pre><code>input&lt;-layer_input(shape=2)
  hidden&lt;-layer_dense(input,units=4,activation="sigmoid",use_bias=T)
  outputs&lt;- list(
    layer_dense(hidden,units=1,activation="linear",use_bias=F),
    layer_dense(hidden,units=1,activation="linear",use_bias=F)
  )
  model&lt;-keras_model(inputs=input, outputs=outputs)
  summary(model)
  </code></pre>
  
  <p>The output of the summary call is the following:</p>
  
  <pre><code>Model
  Model: "model_63"
  ____________________________________________________________________________________________________________________________________________________
  Layer (type)                                    Output Shape                     Param #           Connected to                                     
  ====================================================================================================================================================
  input_63 (InputLayer)                           [(None, 2)]                      0                                                                  
  ____________________________________________________________________________________________________________________________________________________
  dense_85 (Dense)                                (None, 4)                        12                input_63[0][0]                                   
  ____________________________________________________________________________________________________________________________________________________
  dense_86 (Dense)                                (None, 1)                        4                 dense_85[0][0]                                   
  ____________________________________________________________________________________________________________________________________________________
  dense_87 (Dense)                                (None, 1)                        4                 dense_85[0][0]                                   
  ====================================================================================================================================================
  Total params: 20
  Trainable params: 20
  Non-trainable params: 0
  </code></pre>
  
  <p>Somehow (not shown here) the network does not behave as expected. I suspect that something with the architecture is wrong. What I want is a network with two input neurons, 4 hidden neurons and 2 linear output neurons.
  What do the number in the brackets in the connected-to column mean?</p>