training_loop.py

optimizer = keras.optimizers.SGD(
    keras.optimizers.schedules.ExponentialDecay(
        initial_learning_rate=100.0, decay_steps=500, decay_rate=0.96
    )
)

base_image = preprocess_image('yuvnish malhotra photo.jpg')
style_reference_image = preprocess_image('style_image3.jpg')
combination_image = tf.Variable(preprocess_image('yuvnish malhotra photo.jpg'))
print(base_image.shape)
print(style_reference_image.shape)
print(combination_image.shape)
losses = []
iter = []
# combination_image = tf.Variable(content_img)

iterations = 8000
for i in range(1, iterations + 1):
    loss, grads = compute_loss_and_grads(
        combination_image, base_image, style_reference_image
    )
    optimizer.apply_gradients([(grads, combination_image)])
    losses.append(loss)
    iter.append(i)
    if i % 100 == 0:
        print("Iteration %d: loss=%.2f" % (i, loss))
    if i == 8000:
        img = deprocess_image(combination_image.numpy())
        fname = result_prefix + "_at_iteration_%d.png" % i
#         keras.preprocessing.image.save_img(fname, img)
        cv2.imwrite(fname, img)