Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Table of Contents (18 chapters)
Title Page
Packt Upsell

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, 20),  
               (80, 40), (80, 40),  
               (80, 80), (80, 80)] 
stitched_figs = [] 
for (layer_name, layer_size) in zip(layer_names, layer_sizes):