title: Tensorflow model.summary() not showing layers information
body: <p>I have an issue when using Keras's functional API to perform transfer learning. The summary() function does not show layers of the information of the new model.
  Here is the code that I run to import the model:</p>
  <pre><code>import tensorflow as tf
  from tensorflow import keras 
  from keras.models import Model
  model = tf.keras.applications.VGG16()
  model.summary()
  </code></pre>
  <p>As expected, the output is correct:</p>
  <pre><code>Model: &quot;vgg16&quot;
  _________________________________________________________________
  Layer (type)                 Output Shape              Param #   
  =================================================================
  input_4 (InputLayer)         [(None, 224, 224, 3)]     0         
  _________________________________________________________________
  block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
  _________________________________________________________________
  block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
  _________________________________________________________________
  block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
  _________________________________________________________________
  block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
  _________________________________________________________________
  block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
  _________________________________________________________________
  block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
  _________________________________________________________________
  block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
  _________________________________________________________________
  block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
  _________________________________________________________________
  block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
  _________________________________________________________________
  block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
  _________________________________________________________________
  block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
  _________________________________________________________________
  block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
  _________________________________________________________________
  block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
  _________________________________________________________________
  block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
  _________________________________________________________________
  block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
  _________________________________________________________________
  block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
  _________________________________________________________________
  block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
  _________________________________________________________________
  block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
  _________________________________________________________________
  flatten (Flatten)            (None, 25088)             0         
  _________________________________________________________________
  fc1 (Dense)                  (None, 4096)              102764544 
  _________________________________________________________________
  fc2 (Dense)                  (None, 4096)              16781312  
  _________________________________________________________________
  predictions (Dense)          (None, 1000)              4097000   
  =================================================================
  Total params: 138,357,544
  Trainable params: 138,357,544
  Non-trainable params: 0
  _________________________________________________________________
  </code></pre>
  <p>Now here is the code I use to perform transfer learning by removing the model's last 2 layers:</p>
  <pre><code>model2 = Model(model.input, model.layers[-2].output)
  model2.summary()
  </code></pre>
  <p>Here is the output:</p>
  <pre><code>Model: &quot;model_8&quot;
  _________________________________________________________________
  Layer (type)                 Output Shape              Param #   
  =================================================================
  Total params: 134,260,544
  Trainable params: 134,260,544
  Non-trainable params: 0
  _________________________________________________________________
  </code></pre>
  <p>All information relative to the layers has disappeared... Is this normal behavior from the functional API?</p>
  <p>Thanks in advance.</p>