title: output feature map dimension of VGG16 model
body: <p>I saw the example of feature extraction in the <a href="https://keras.io/applications/" rel="nofollow noreferrer">keras doc</a> and used the following code to extract feature from input image</p>
  
  <pre><code>input_shape = (224, 224, 3)
  model = VGG16(weights = 'imagenet', input_shape = (input_shape[0], 
  input_shape[1], input_shape[2]), pooling = 'max', include_top = False)
  img = image.load_img(img_path, target_size=(input_shape[0], 
  input_shape[1]))
  img = image.img_to_array(img)
  img = np.expand_dims(img, axis=0)
  img = preprocess_input(img)
  feature =  model.predict(img)
  </code></pre>
  
  <p>Then when I output the shape of the <code>feature</code> variable, I found it is (1, 512). Why it is this dimension? 
  The <code>print model.summary()</code> shows the shape of last conv layer's output after maxpooling is (7, 7, 512), this is the dimension that I expect <code>feature</code> should be. </p>