Machine Learning with Swift

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, ...