DLSS Using Generative Adversarial Networks

# Train the Generative Adversarial Network
    def train(self, epochs, batch_size, save_interval):
        
        # Prevent script from crashing from bad user input
        if(epochs <= 0):
            epochs = 1
        
        if(batch_size <= 0):
            batch_size = 1

        # Load the dataset
        X_train, Y_train = self.load_data()
        
        # Normalizing data to be between 0 and 1
        X_train = X_train / 255
        Y_train = Y_train / 255

        # Adversarial ground truths
        valid = np.ones((batch_size, 1))
        fake = np.zeros((batch_size, 1))
        
        # Placeholder arrays for Loss function values
        g_loss_epochs = np.zeros((epochs, 1))
        d_loss_epochs = np.zeros((epochs, 1))
        
        # Training the GAN
        for epoch in range(1, epochs + 1):
            
            # Initialize indexes for training data
            start = 0
            end = start + batch_size
            
            # Array to sum up all loss function values
            discriminator_loss_real = []
            discriminator_loss_fake = []
            generator_loss = []
            
            # Iterate through dataset training one batch at a time
            for i in range(int(len(X_train)/batch_size)):
                
                # Get batch of images
                imgs_output = Y_train[start:end]
                imgs_input = X_train[start:end]

                # Train Discriminator

                # Make predictions on current batch using generator
                gen_imgs = self.generator.predict(imgs_input)

                # Train the discriminator (real classified as ones and generated as zero)
                d_loss_real = self.discriminator.train_on_batch(imgs_output, valid)
                d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
                d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

                #  Train Generator

                # Train the generator (wants discriminator to mistake images as real)
                g_loss = self.combined.train_on_batch(imgs_input, valid)
                
                # Add loss for current batch to sum over entire epoch
                discriminator_loss_real.append(d_loss[0])
                discriminator_loss_fake.append(d_loss[1])
                generator_loss.append(g_loss)
                
                # Increment image indexes
                start = start + batch_size
                end = end + batch_size