title: Tensorflow Hub Image Modules: Clarity on Preprocessing and Output values
body: <p>Many thanks for support!
  I currently use TF Slim - and TF Hub seems like a very useful addition for transfer learning. However the following things are not clear from the documentation:<BR><BR>
  <strong>1. Is preprocessing done implicitly?</strong> Is this based on "trainable=True/False" parameter in constructor of module?</p>
  
  <pre><code>module = hub.Module("https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", trainable=True)
  </code></pre>
  
  <p>When I use Tf-slim I use the preprocess method:<BR></p>
  
  <pre><code>inception_preprocessing.preprocess_image(image, img_height, img_width, is_training)
  </code></pre>
  
  <p><strong>2.How to get access to AuxLogits for an inception model?</strong> Seems to be missing:</p>
  
  <pre><code>import tensorflow_hub as hub
  import tensorflow as tf
  
  img = tf.random_uniform([10,299,299,3])
  module = hub.Module("https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", trainable=True)
  outputs = module(dict(images=img), signature="image_feature_vector", as_dict=True)
  </code></pre>
  
  <p>The output is</p>
  
  <pre><code>dict_keys(['InceptionV3/Mixed_6b', 'InceptionV3/MaxPool_5a_3x3', 'InceptionV3/Mixed_6c', 'InceptionV3/Mixed_6d', 'InceptionV3/Mixed_6e', 'InceptionV3/Mixed_7a', 'InceptionV3/Mixed_7b', 'InceptionV3/Conv2d_2a_3x3', 'InceptionV3/Mixed_7c', 'InceptionV3/Conv2d_4a_3x3', 'InceptionV3/Conv2d_1a_3x3', 'InceptionV3/global_pool', 'InceptionV3/MaxPool_3a_3x3', 'InceptionV3/Conv2d_2b_3x3', 'InceptionV3/Conv2d_3b_1x1', 'default', 'InceptionV3/Mixed_5b', 'InceptionV3/Mixed_5c', 'InceptionV3/Mixed_5d', 'InceptionV3/Mixed_6a'])
  </code></pre>