text: # image_input the test image being fed into the network to produce the feature maps
  # tf_activation should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer
  # Note that to get access to tf_activation, the session should be interactive which can be achieved with the following commands.
  # sess = tf.InteractiveSession()
  # sess.as_default()
  
  # activation_minmax can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and    max values of the output
  # plt_num used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry
  
  def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1)
      # Here make sure to preprocess your image_input in a way your network expects
      # with size, normalization, ect if needed
      # image_input =
      # Note x should be the same name as your network's tensorflow data placeholder variable
      # If you get an error tf_activation is not defined it maybe having trouble accessing the variable from inside a function
      activation = tf_activation.eval(session=sess,feed_dict={x  image_input})
      featuremaps = activation.shape[3]
      plt.figure(plt_num, figsize=(15,15))
      for featuremap in range(featuremaps)
          plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column
          plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number
          if activation_min != -1 & activation_max != -1
              plt.imshow(activation[0,,, featuremap], interpolation=nearest, vmin =activation_min, vmax=activation_max, cmap=gray)
          elif activation_max != -1
              plt.imshow(activation[0,,, featuremap], interpolation=nearest, vmax=activation_max, cmap=gray)
          elif activation_min !=-1
              plt.imshow(activation[0,,, featuremap], interpolation=nearest, vmin=activation_min, cmap=gray)
          else
              plt.imshow(activation[0,,, featuremap], interpolation=nearest, cmap=gray)