title: ValueError: Output tensors to a Model must be the output of a TensorFlow `Layer`
body: <p>I'm building a model in Keras using some tensorflow function (reduce_sum and l2_normalize) in the last layer while encountered this problem. I have searched for a solution but all of it related to "Keras tensor".</p>
  
  <p>Here is my code:</p>
  
  <pre><code>import tensorflow as tf;
  from tensorflow.python.keras import backend as K
  
  vgg16_model = VGG16(weights = 'imagenet', include_top = False, input_shape = input_shape);
  
  fire8 = extract_layer_from_model(vgg16_model, layer_name = 'block4_pool');
  
  pool8 = MaxPooling2D((3,3), strides = (2,2), name = 'pool8')(fire8.output);
  
  fc1 = Conv2D(64, (6,6), strides= (1, 1), padding = 'same', name = 'fc1')(pool8);
  
  fc1 = Dropout(rate = 0.5)(fc1);
  
  fc2 = Conv2D(3, (1, 1), strides = (1, 1), padding = 'same', name = 'fc2')(fc1);
  
  fc2 = Activation('relu')(fc2);
  
  fc2 = Conv2D(3, (15, 15), padding = 'valid', name = 'fc_pooling')(fc2);
  
  fc2_norm = K.l2_normalize(fc2, axis = 3);
  
  est = tf.reduce_sum(fc2_norm, axis = (1, 2));
  est = K.l2_normalize(est);
  
  FC_model = Model(inputs = vgg16_model.input, outputs = est);
  </code></pre>
  
  <p>and then the error: </p>
  
  <blockquote>
    <p>ValueError: Output tensors to a Model must be the output of a
    TensorFlow <code>Layer</code> (thus holding past layer metadata). Found:
    Tensor("l2_normalize_3:0", shape=(?, 3), dtype=float32)</p>
  </blockquote>
  
  <p>I noticed that without passing fc2 layer to these functions, the model works fine:</p>
  
  <pre><code>FC_model = Model(inputs = vgg16_model.input, outputs = fc2);
  </code></pre>
  
  <p>Can someone please explain to me this problem and some suggestion on how to fix it?</p>