anchor.diff:          resized_inputs = self._ensure_same_size(layer_inputs)
   
           # Compute sigmoid weighted inputs
  -        weighted_inputs = [resized_inputs[i] * K.sigmoid(self.W[i]) for i in range(len(layer_inputs))]
  +        stacked = K.concatenate(resized_inputs, axis=-1)
  +        weighted = stacked * K.sigmoid(self.W)
   
           # Merge according to provided merge strategy.
  -        merged = self._merge(weighted_inputs)
  +        merged = self._merge(weighted)
   
           # Cache this for use in `get_output_shape_for`
           self._out_shape = K.int_shape(merged)
  
positive.code: 
  == Discussion
  NOTE: Experimentation is still a work in progress. Check back for updates.
  
  === Experiment1: DenseNet Style merge
  
  
negative.code: import tensorflow as tf
  
  from keras import backend as K
  from keras.layers import Lambda, Layer
  
  class Connection(Layer):
      """Takes a list of inputs, resizes them to the same shape, and outputs a weighted merge.
      """
      def __init__(self, init_value=0.5, merge_mode='concat',
                   resize_interpolation=tf.image.ResizeMethod.BILINEAR,
                   shared_weights=True):
          """Creates a connection that encapsulates weighted connection of input feature maps.
  
          :param init_value: The init value for connection weight
          :param merge_mode: Defines how feature maps from inputs are aggregated.
          :param resize_interpolation: The downscaling interpolation to use. Instance of `tf.image.ResizeMethod`.
              Note that ResizeMethod.AREA will fail as its gradient is not yet implemented.
          :param shared_weights: True to share the same weight for all feature maps from inputs[i].
          False creates a separate weight per feature map.
          """
          self.init_value = init_value
          self.merge_mode = merge_mode
          self.resize_interpolation = resize_interpolation
          self.shared_weights = shared_weights
          super(Connection, self).__init__()
  
      def _ensure_same_size(self, inputs):
          """Ensures that all inputs match last input size.
  