人工智能（AI）与创意产业的结合已经成为一种趋势

```python
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
 
# 加载预训练的深度学习模型
model = tf.keras.applications.VGG19(weights='imagenet', include_top=False)
 
# 加载一张图片
content_path = 'content.jpg'
style_path = 'style.jpg'
 
def load_img(path_to_img):
    max_dim = 512
    img = tf.io.read_file(path_to_img)
    img = tf.image.decode_image(img, channels=3)
    img = tf.image.convert_image_dtype(img, tf.float32)
 
    shape = tf.cast(tf.shape(img)[:-1], tf.float32)
    long_dim = max(shape)
    scale = max_dim / long_dim
 
    new_shape = tf.cast(shape * scale, tf.int32)
 
    img = tf.image.resize(img, new_shape)
    img = img[tf.newaxis, :]
    return img
 
def imshow(image, title=None):
    if len(image.shape) > 3:
        image = tf.squeeze(image, axis=0)
 
    plt.imshow(image)
    if title:
        plt.title(title)
 
content_image = load_img(content_path)
style_image = load_img(style_path)
 
plt.subplot(1, 2, 1)
imshow(content_image, 'Content Image')
 
plt.subplot(1, 2, 2)
imshow(style_image, 'Style Image')
plt.show()
 
# 定义内容和风格特征提取模型
content_layers = ['block5_conv2']
style_layers = ['block1_conv1',
                'block2_conv1',
                'block3_conv1',
                'block4_conv1',
                'block5_conv1']
 
num_content_layers = len(content_layers)
num_style_layers = len(style_layers)
 
def vgg_layers(layer_names):
    vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')
    vgg.trainable = False
    outputs = [vgg.get_layer(name).output for name in layer_names]
    model = tf.keras.Model([vgg.input], outputs)
    return model
 
style_extractor = vgg_layers(style_layers)
style_outputs = style_extractor(style_image * 255)
 
# 计算格拉姆矩阵
def gram_matrix(input_tensor):
    result = tf.linalg.einsum('bijc,bijd->bcd', input_tensor, input_tensor)
    input_shape = tf.shape(input_tensor)
    num_locations = tf.cast(input_shape[1]*input_shape[2], tf.float32)
    return result/(num_locations)
 
# 提取风格特征并计算格拉姆矩阵
class StyleContentModel(tf.keras.models.Model):
    def __init__(self, style_layers, content_layers):
        super(StyleContentModel, self).__init__()
        self.vgg = vgg_layers(style_layers + content_layers)
        self.style_layers = style_layers
        self.content_layers = content_layers
        self.num_style_layers = len(style_layers)
        self.vgg.trainable = False
 
    def call(self, inputs):
        inputs = inputs * 255.0
        preprocessed_input = tf.keras.applications.vgg19.preprocess_input(inputs)
        outputs = self.vgg(preprocessed_input)
        style_outputs, content_outputs = (outputs[:self.num_style_layers],
                                          outputs[self.num_style_layers:])
 
        style_outputs = [gram_matrix(style_output)
                         for style_output in style_outputs]
 
        content_dict = {content_name: value
                        for content_name, value
                        in zip(self.content_layers, content_outputs)}
 
        style_dict = {style_name: value
                      for style_name, value
                      in zip(self.style_layers, style_outputs)}
 
        return {'content': content_dict, 'style': style_dict}
 
extractor = StyleContentModel(style_layers, content_layers)
 
# 定义损失函数
style_targets = extractor(style_image)['style']
content_targets = extractor(content_image)['content']
 
image = tf.Variable(content_image)
 
def clip_0_1(image):
    return tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=1.0)
 
opt = tf.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)
 
style_weight = 1e-2
content_weight = 1e4
 
def style_content_loss(outputs):
    style_outputs = outputs['style']
    content_outputs = outputs['content']
    style_loss = tf.add_n([tf.reduce_mean((style_outputs[name]-style_targets[name])**2)
                           for name in style_outputs.keys()])
    style_loss *= style_weight / num_style_layers
 
    content_loss = tf.add_n([tf.reduce_mean((content_outputs[name]-content_targets[name])**2)
                             for name in content_outputs.keys()])
    content_loss *= content_weight / num_content_layers
    loss = style_loss + content_loss
    return loss
 
@tf.function()
def train_step(image):
    with tf.GradientTape() as tape:
        outputs = extractor(image)
        loss = style_content_loss(outputs)
 
    grad = tape.gradient(loss, image)
    opt.apply_gradients([(grad, image)])
    image.assign(clip_0_1(image))
 
import time
start = time.time()
 
epochs = 10
steps_per_epoch = 100
 
step = 0
for n in range(epochs):
    for m in range(steps_per_epoch):
        step += 1
        train_step(image)
    print("Train step: {}".format(step))
 
end = time.time()
print("Total time: {:.1f}".format(end-start))
 
# 显示生成的图像
plt.imshow(image.read_value()[0])
plt.title('Generated Image')
plt.show()
```