Visualizing convolution filters

Debugging CNNs is notoriously difficult. One of the ways to check if the convolutional layers learned anything meaningful is to visualize their outputs using Keras-vis package:

from vis.utils import utils 
from vis.visualization import visualize_class_activation, get_num_filters 

We have to convert grayscale images to rgb to use them with keras-vis:

def to_rgb(im): 
    # I think this will be slow 
    w, h = im.shape 
    ret = np.empty((w, h, 3), dtype=np.uint8) 
    ret[:, :, 0] = im 
    ret[:, :, 1] = im 
    ret[:, :, 2] = im 
    return ret 

Names of the layers we want to visualize (consult model structure for exact layer names):

layer_names = ['conv2d_1', 'conv2d_2',  
               'conv2d_3', 'conv2d_4',  
               'conv2d_5', 'conv2d_6'] 
 
layer_sizes = [(80, 20), (80...