During each epoch, the function below plots 9 generated images. For comparison, it will also plot the corresponding 9 original target images and 9 noised input images. We need to use the upscale function we've defined above when plotting to make sure the images are scaled to range between 0 and 255 so that you do not encounter issues when plotting.  A sample output of this function is shown below after the code snippet.

def generated_images_plot(original, noised_data, generator):
    
    print('NOISED')
    for i in range(9):
        plt.subplot(331 + i)
        plt.axis('off')
        plt.imshow(upscale(np.squeeze(noised_data[i])), cmap='gray') # upscale for plotting
    plt.show()
    
    print('GENERATED')
    for i in range(9):
        pred = generator.predict(noised_data[i:i+1], verbose=0)
        plt.subplot(331 + i)
        plt.axis('off')
        plt.imshow(upscale(np.squeeze(pred[0])), cmap='gray') # upscale to avoid plotting errors
    plt.show()
    
    print('ORIGINAL')
    for i in range(9):
        plt.subplot(331 + i)
        plt.axis('off')
        plt.imshow(upscale(np.squeeze(original[i])), cmap='gray') # upscale for plotting
    plt.show()