title: keras model.evaluate() does not show loss
body: <p>I've created a neural network of the following form in <code>keras</code>:</p>
  
  <pre><code>from keras.layers import Dense, Activation, Input
  from keras import Model
  
  input_dim_v = 3
  hidden_dims=[100, 100, 100]
  
  inputs = Input(shape=(input_dim_v,))
  net = inputs
  
  for h_dim in hidden_dims:
      net = Dense(h_dim)(net)
      net = Activation("elu")(net)
  
  outputs = Dense(self.output_dim_v)(net)
  model_v = Model(inputs=inputs, outputs=outputs)
  model_v.compile(optimizer='adam', loss='mean_squared_error', metrics=['mse'])
  </code></pre>
  
  <p>Later, I train it on single examples using <code>model_v.train_on_batch(X[i],y[i])</code>. </p>
  
  <p>To test, whether the neural network is becoming a better function approximator, I wanted to evaluate the model on the accumulated <code>X</code> and <code>y</code> (in my case, <code>X</code> and <code>y</code> grow over time) periodically. However, when I call <code>model_v.evaluate(X, y)</code>, only the characteristic progress bars appear in the console, but neither the loss value nor the mse-metric (which are the same in this case) are printed. </p>
  
  <p>How can I change that?</p>