for i in range(iterations):
    print('Start of iteration', i)
    start_time = time.time()
    x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),
                                     fprime=evaluator.grads, maxfun=20)
    print('Current loss value:', min_val)
    end_time = time.time()
    print('Iteration %d completed in %ds' % (i, end_time - start_time))

    y = x.reshape((h, w, 3))
    y = y[:, :, ::-1]
    y[:, :, 0] += 103.939
    y[:, :, 1] += 116.779
    y[:, :, 2] += 123.68
    y = np.clip(y, 0, 255).astype('uint8')

    oneImg = Image.fromarray(y)
    oneImg.save('generated4-' + str(i) + '.jpg')

x = x.reshape((h, w, 3))
x = x[:, :, ::-1]
x[:, :, 0] += 103.939
x[:, :, 1] += 116.779
x[:, :, 2] += 123.68
x = np.clip(x, 0, 255).astype('uint8')

finishedImg = Image.fromarray(x)
finishedImg.save('generated4.jpg')