def run_style_transfer(content_image_filename,
                      style_image_filename,
                      initialImage=None,
                      alpha=10,beta=1e-1,
                      epochs=1200,
                      learning_rate=5.0,
                      prefix=None):

  if prefix is None:
    print('Output file prefix not defined...')
    return

  content_image = load_image(content_image_filename)
  style_image = load_image(style_image_filename)

  weights_dict = getModelWeightsAsDict()
  tf.reset_default_graph()
  layers = get_nst_model(weights_dict)

  print('Model graph generated...\n')

  # Calculate tensor for content loss
  J_content = 0.0
  with tf.Session() as sess:
    for content_layer_name, weight in CONTENT_LAYERS:
      content_layer = layers[content_layer_name]
      content_target = sess.run(content_layer,feed_dict={layers['input']:content_image})
      J_content = J_content + weight * getContentLoss(layers,content_target, content_layer_name)
  print('Content loss defined...\n')


  # Calculate tensor for style loss
  J_style = 0.0
  with tf.Session() as sess:
    for style_layer_name, weight in STYLE_LAYERS:
        style_layer = layers[style_layer_name]
        style_target = sess.run(style_layer,feed_dict={layers['input']:style_image})
        J_style = J_style + weight * getStyleLoss(layers,style_target,style_layer_name)
  print('Style loss defined...\n')

  J_total = getTotalLoss(J_content,J_style,alpha=alpha,beta=beta)

  optimizer = tf.train.AdamOptimizer(learning_rate).minimize(J_total)

  print('Losses defined...\n')

  print('Trainable variables:\n',tf.trainable_variables())

  with tf.Session() as sess:

    # Initialize image
    sess.run(tf.global_variables_initializer())
    if initialImage is not None:
      sess.run(layers['input'].assign(initialImage))
    else:
      sess.run(layers['input'].assign(generate_noise_image()))

    for epoch in range(epochs):
      epoch_loss, epoch_content_loss, epoch_style_loss, _ = sess.run([J_total,J_content,J_style,optimizer])

      if (epoch+1) % 100 == 0:
        generated_image = sess.run(layers['input'])
        generated_image = save_image(generated_image,prefix + '_' + str(epoch+1) + '.jpg')
        print('Loss after epoch %d: \nT: %f, \nC: %f, \nS: %f'%(epoch,epoch_loss,epoch_content_loss,epoch_style_loss))
        generated_image = cv2.cvtColor(generated_image, cv2.COLOR_RGB2BGR)
        cv2_imshow(generated_image)