utils.py

import PIL
import matplotlib.pyplot as plt
from IPython import display
from tensorflow.keras.models import Sequential, Model, load_model

# Generate and save images
def generate_and_save_images(model, epoch, test_input):
  # Notice `training` is set to False.
  # This is so all layers run in inference mode (batchnorm).
  predictions = model(test_input, training=False)

  fig = plt.figure(figsize=(4,4))

  for i in range(predictions.shape[0]):
      plt.subplot(4, 4, i+1)
      plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
      plt.axis('off')

  plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
  plt.show()

# Plotting generator losses and discriminator losses
def plot_loss(gen,disc):
  plt.figure(figsize=(10,5))
  plt.title("Generator and Discriminator Loss During Training")
  plt.plot(gen,label="G")
  plt.plot(disc,label="D")
  plt.xlabel("iterations")
  plt.ylabel("Loss")
  plt.legend()
  plt.savefig('loss_plot.png')
  plt.show()