As we discussed, SRGAN has three neural networks, a generator, a discriminator, and a pre-trained VGG19 network on the Imagenet dataset. In this section, we will write the implementation for all the networks. Let's start by implementing the generator network.
Before starting to write the implementations, create a Python file called main.py and import the essential modules, as follows:
import glob
import os
import numpy as np
import tensorflow as tf
from keras import Input
from keras.applications import VGG19
from keras.callbacks import TensorBoard
from keras.layers import BatchNormalization, Activation, LeakyReLU, Add, Dense, PReLU, Flatten
from keras.layers.convolutional import Conv2D, UpSampling2D
from keras.models import Model
from keras.optimizers import Adam
from keras_preprocessing.image import img_to_array, load_img
from scipy.misc import imsave...