SubhadityaMukherjee

calc_loss

• We take the image and the model as inputs
• expand dims basically adds an extra dimension to our input along the x axis to make it work with inception
• for every activation in our layers, we calculate the loss and append it to a list
• reduce_mean() and reduce_sum() are approximately the mean and sum equivalent for tensors instead of just plain arrays
• Thus the sum is the total loss we get

def calc_loss(img, model):
 img_batch = tf.expand_dims(img, axis=0)

SubhadityaMukherjee

Pre trained

• We use Inception Net v3 which is a pretrained network that already has some idea of the world.
• We use imagenet weights which basically allows us to use transfer learning on the network
• Instead of training from scratch we can just cherry pick layers and use our neural network on it

base_model = tf.keras.applications.InceptionV3(include_top=False,
                                               weights='imagenet')

Model

SubhadityaMukherjee

Download

• We first download the image
• Resize it for faster computation

url = 'https://nicolekessler.files.wordpress.com/2013/04/hellish_demons.jpg?w=1024'
def download(url, max_dim=None):
    name = "demons.jpg"
 image_path = tf.keras.utils.get_file(name, origin=url)

SubhadityaMukherjee

url = 'https://nicolekessler.files.wordpress.com/2013/04/hellish_demons.jpg?w=1024'

Download

• We first download the image
• Resize it for faster computation

def download(url, max_dim=None): name = "demons.jpg" image_path = tf.keras.utils.get_file(name, origin=url) img = PIL.Image.open(image_path)

SubhadityaMukherjee

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
import tensorflow as tf
import IPython.display as display
import matplotlib as mpl
import numpy as np
import PIL.Image
from tensorflow.keras.preprocessing import image

SubhadityaMukherjee

url = 'https://nicolekessler.files.wordpress.com/2013/04/hellish_demons.jpg?w=1024'

Download

• We first download the image
• Resize it for faster computation

def download(url, max_dim=None):
    name = "demons.jpg"
 image_path = tf.keras.utils.get_file(name, origin=url)

SubhadityaMukherjee

def get_content_loss(base_content, target):
    return tf.reduce_mean(tf.square(base_content - target))