title: Difference of calling the Keras pretrained model without including top layers
body: <p>What is the difference of calling the  VGG16 model with or without including top layers of the model? I wonder, why the input parameters to the layers are not shown in the model summary when the model is called without including the top layers. I used the VGG16 model in the following two ways:</p>
  
  <pre><code>from keras.applications import vgg16
  model = vgg16.VGG16(weights='imagenet', include_top=False)
  print(model.summary)
  </code></pre>
  
  <p>The shape of the layers in the model does not show any inputs  i.e.(None, None, None,64), please see below</p>
  
  <pre><code>Layer (type)                 Output Shape              Param 
  ===================================================================   
  block1_conv1 (Conv2D)        (None, None, None, 64)    1792      
  block1_conv2 (Conv2D)        (None, None, None, 64)    36928     
  block1_pool (MaxPooling2D)   (None, None, None, 64)    0         
  </code></pre>
  
  <p>However, the following code returns the input parameters</p>
  
  <pre><code>from keras.applications import vgg16
  model = vgg16.VGG16()
  print(model.summary)
  </code></pre>
  
  <p>The shape of the layers, in this case, return the input parameters</p>
  
  <pre><code>Layer (type)                 Output Shape              Param   
  ==================================================================   
  block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
  block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
  block1_pool (MaxPooling2D)   (None, 112, 112, 64)       0    
  </code></pre>
  
  <p>I seek to understand why it is like this, Please comment</p>