title: TensorflowHub pretrained MobileNetV2 change input shape and transfer learning
body: <p>I'm creating a NN using MobileNetV2 140 224 from Tensorflow Hub as pretrained convnet. Now I would like to change input layer size, I'd like to input 500x500 images.
  Is this possible? What is the best way to achieve this?</p>
  
  <p>This is my code:</p>
  
  <pre><code>IMG_SHAPE = (224, 224, 3)
  base_model = hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/feature_vector/4', input_shape=IMG_SHAPE)
  base_model.trainable = False
  model = Sequential([
    base_model,
    Dropout(0.25),
    Dense(3, activation='softmax')
  ])
  adam = Adam(learning_rate=0.0001)
  model.compile(optimizer=adam,
                loss='sparse_categorical_crossentropy',
                metrics=['accuracy'])
  </code></pre>
  
  <p>Can I do it only by changing <code>IMG_SHAPE = (500, 500, 3)</code> or I have to add input layers or something else?</p>