title: Can you do transfer learning on SSD mobilnetv2 via tensorflow? is functional API compatible?
body: <p>been surfing on Doc + lot of tutorials but i dont quite get what im doing wrong.
  What im atempting to do is relativly simple: trying to do transfer learning on the Ssd mobilnetv2 network. I've followed this tutorial <a href="https://www.tensorflow.org/tutorials/images/transfer_learning" rel="nofollow noreferrer">here</a> , but when i arrrive to this <a href="https://www.tensorflow.org/tutorials/images/transfer_learning#create_the_base_model_from_the_pre-trained_convnets" rel="nofollow noreferrer">step</a> , the model i want to use is not the mobilenet v2 but Ssd Mobilenet v2. It doesnt seems to exist a tf.keras.application.ssdmobilnetv2 , so i've decided to use the model that is aviable on tensorFlow hub   ( <a href="https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2" rel="nofollow noreferrer">this one</a> ) . so i've researched on how to get a Tensorflow python Model object to be able to follow the tutorial steps, and i've encountered this other <a href="https://www.tensorflow.org/tutorials/images/transfer_learning_with_hub" rel="nofollow noreferrer">tutorial</a> . the thing is, apparently ssd MobilnetV2 has multiple outputs, so i cant call tf.keras.Sequential    with the kerasLayer obtained. Then  the documentation says that when having multiple outputs, you need to use the Functional API. the thing is, how can i call the Functional API with my ssd_mobilnetv2 downloaded on THUB? there seems to be no examples on the web. here is the Python sample of code im trying to make work</p>
  <pre><code>feature_extractor_layer = hub.KerasLayer(
      &quot;https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2&quot;,
      input_shape=(300, 300, 3),
      dtype=tf.uint8,
      trainable=False)
  
  
  num_classes = len(class_names)
  print(num_classes)
  
  #### using the Functional api ( doesnt work)
  model= tf.keras.Model(
                feature_extractor_layer,
                tf.keras.layers.Dense(num_classes))
  #### using keras.Sequential, doesnt work too
  model = tf.keras.Sequential([
                 feature_extractor_layer,
                tf.keras.layers.Dense(num_classes)
                ])
  
  model.summary()
  </code></pre>
  <p>i would be glad if someone can help me out!</p>
  <p>best regards</p>