title: keras model produces results different with what I intended
body: <p>My model is as below:</p>
  <pre><code>model = tf.keras.Sequential()
  model.add(Dense(10, 'relu', input_shape = (1,)))
  model.add(Dense(6, 'relu'))
  model.add(Dense(2, 'linear'))
  </code></pre>
  <p>I want to make my model to get [10,5] regardless of the input.</p>
  <p>So I customized the loss as:</p>
  <pre><code>def custom_loss1(y_true,y_pred):
    return tf.reduce_mean(tf.square(10-y_pred[0]))+ tf.reduce_mean(tf.square(5-y_pred[1]))
  </code></pre>
  <p>I used data as
  input = (random num) / output = (random num)</p>
  <p>I intended the model to get a result as (10,5) regardless of input.</p>
  <p>However, the model does not learn anything.</p>
  <p>What is the problem??</p>