def grid_visual(data):
  """
  This function displays a grid of images to show full misclassification
  :param data: grid data of the form;
      [nb_classes : nb_classes : img_rows : img_cols : nb_channels]
  :return: if necessary, the matplot figure to reuse
  """
  import matplotlib.pyplot as plt

  # Ensure interactive mode is disabled and initialize our graph
  plt.ioff()
  figure = plt.figure()
  figure.canvas.set_window_title('Cleverhans: Grid Visualization')

  # Add the images to the plot
  num_cols = data.shape[0]
  num_rows = data.shape[1]
  num_channels = data.shape[4]
  for y in range(num_rows):
    for x in range(num_cols):
      figure.add_subplot(num_rows, num_cols, (x + 1) + (y * num_cols))
      plt.axis('off')

      if num_channels == 1:
        plt.imshow(data[x, y, :, :, 0], cmap='gray')
      else:
        plt.imshow(data[x, y, :, :, :])

  # Draw the plot and return
  plt.show()
  return figure
def grid_visual ( data ) : import matplotlib . pyplot as plt plt . ioff ( ) figure = plt . figure ( ) figure . canvas . set_window_title ( 'Cleverhans: Grid Visualization' ) num_cols = data . shape [ 0 ] num_rows = data . shape [ 1 ] num_channels = data . shape [ 4 ] for y in range ( num_rows ) : for x in range ( num_cols ) : figure . add_subplot ( num_rows , num_cols , ( x + 1 ) + ( y * num_cols ) ) plt . axis ( 'off' ) if num_channels == 1 : plt . imshow ( data [ x , y , : , : , 0 ] , cmap = 'gray' ) else : plt . imshow ( data [ x , y , : , : , : ] ) plt . show ( ) return figure
This function displays a grid of images to show full misclassification
  :param data: grid data of the form;
      [nb_classes : nb_classes : img_rows : img_cols : nb_channels]
  :return: if necessary, the matplot figure to reuse