title: Transfer Learning with Tensorflow Problem
body: <p>I am trying to solve a problem for a deep learning class and the block of code i have to modify looks like this</p>
  <pre><code>def alpaca_model(image_shape=IMG_SIZE, data_augmentation=data_augmenter()):
      &quot;&quot;&quot; Define a tf.keras model for binary classification out of the MobileNetV2 model
      Arguments:
          image_shape -- Image width and height
          data_augmentation -- data augmentation function
      Returns:
          tf.keras.model
      &quot;&quot;&quot;
      
      
      input_shape = image_shape + (3,)
      
      # START CODE HERE
  
  
      base_model=tf.keras.applications.MobileNetV2(input_shape=input_shape, include_top=False, weights=&quot;imagenet&quot;)
  
      # Freeze the base model by making it non trainable
      base_model.trainable = None 
  
      # create the input layer (Same as the imageNetv2 input size)
      inputs = tf.keras.Input(shape=None) 
      
      # apply data augmentation to the inputs
      x = None
      
      # data preprocessing using the same weights the model was trained on
      x = preprocess_input(None) 
      
      # set training to False to avoid keeping track of statistics in the batch norm layer
      x = base_model(None, training=None) 
      
      # Add the new Binary classification layers
      # use global avg pooling to summarize the info in each channel
      x = None()(x) 
      #include dropout with probability of 0.2 to avoid overfitting
      x = None(None)(x)
          
      # create a prediction layer with one neuron (as a classifier only needs one)
      prediction_layer = None
      
      # END CODE HERE
      
      outputs = prediction_layer(x) 
      model = tf.keras.Model(inputs, outputs)
      
      return model
  
  IMG_SIZE = (160, 160)
  def data_augmentation():
      data = tl.keras.Sequential()
      data.add(RandomFlip(&quot;horizontal&quot;)
      data.add(RandomRotation(0.2)
      return data
  </code></pre>
  <p>I tried 3 times starting from that template following the directions and a lot of trial and error. I don't know what I am missing. I have gotten it to the point where it train a model and I can get the summary of it, but the summary is not correct.</p>
  <p>Please help, I am going crazy trying to figure this out. I know it is super simple, but its the simple problems that trip me up.</p>