在下列代码中加入总变差损失

下面是一个基于TensorFlow的图像风格迁移算法的代码示例，我们可以在代码中加入总变差损失：

import tensorflow as tf
import numpy as np
import PIL.Image

# 加载VGG19模型
vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')
vgg.trainable = False

# 定义内容层和风格层
content_layers = ['block5_conv2']
style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1']

# 定义图像预处理函数
def preprocess_image(image):
    image = tf.keras.applications.vgg19.preprocess_input(image)
    return image

# 定义图像还原函数
def deprocess_image(image):
    image = image.reshape((image.shape[1], image.shape[2], 3))
    image[:,:,0] += 103.939
    image[:,:,1] += 116.779
    image[:,:,2] += 123.68
    image = np.clip(image, 0, 255).astype('uint8')
    return image

# 定义内容损失函数
def content_loss(content, target):
    return tf.reduce_mean(tf.square(content - target))

# 定义风格损失函数
def gram_matrix(input_tensor):
    channels = int(input_tensor.shape[-1])
    a = tf.reshape(input_tensor, [-1, channels])
    n = tf.shape(a)[0]
    gram = tf.matmul(a, a, transpose_a=True)
    return gram / tf.cast(n, tf.float32)

def style_loss(style, target):
    style_gram = gram_matrix(style)
    target_gram = gram_matrix(target)
    return tf.reduce_mean(tf.square(style_gram - target_gram))

# 定义总变差损失函数
def total_variation_loss(image):
    x_deltas, y_deltas = tf.image.image_gradients(image)
    return tf.reduce_mean(tf.abs(x_deltas)) + tf.reduce_mean(tf.abs(y_deltas))

# 加载内容图像和风格图像
content_image = PIL.Image.open('content.jpg')
content_image = preprocess_image(np.array(content_image))
style_image = PIL.Image.open('style.jpg')
style_image = preprocess_image(np.array(style_image))

# 初始化生成图像
generated_image = tf.Variable(content_image, dtype=tf.float32)

# 计算内容损失和风格损失
content_outputs = [vgg.get_layer(name).output for name in content_layers]
style_outputs = [vgg.get_layer(name).output for name in style_layers]
outputs = content_outputs + style_outputs

features = tf.keras.models.Model(inputs=vgg.input, outputs=outputs)

content_targets = features(content_image)[len(style_layers):]
style_targets = features(style_image)[:len(style_layers)]
target_values = content_targets + style_targets

content_weight = 1e4
style_weight = 1e-2
total_variation_weight = 30

loss = tf.zeros(shape=())
for i, target in enumerate(target_values):
    if i < len(content_layers):
        loss += content_weight * content_loss(target, content_targets[i])
    else:
        loss += style_weight * style_loss(target, style_targets[i - len(content_layers)])

# 加入总变差损失
loss += total_variation_weight * total_variation_loss(generated_image)

# 计算梯度并更新生成图像
var_list = [generated_image]
optimizer = tf.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)
grads = optimizer.get_gradients(loss, var_list)
grads = tf.clip_by_value(grads, -1, 1)
optimizer.apply_gradients(zip(grads, var_list))

# 显示生成图像
PIL.Image.fromarray(deprocess_image(generated_image.numpy())).show()

在上面的代码中，我们在损失函数中加入了总变差损失，可以通过调整 `total_variation_weight` 参数的值来控制总变差损失的权重。