引言： 我们知道清明上河图是我国国画的代表作之一，是中国十大传世名画之一。为北宋风俗画，北宋画家张择端仅见的存世精品，属国宝级文物，现藏于北京故宫博物院。 清明上河图宽24.8厘米、长528.7厘米 ，绢本设色。作品以长卷形式，采用散点透视构图法，生动记录了中国十二世纪北宋都城东京(又称汴京，今河南开封)的城市面貌和当时社会各阶层人民的生活状况，是北宋时期都城汴京当年繁荣的见证，也是北宋城市经济情况的写照。 这在中国乃至世界绘画史上都是独一无二的。在五米多长的画卷里，共绘了数量庞大的各色人物，牛、骡、驴等牲畜，车、轿、大小船只，房屋、桥梁、城楼等各有特色，体现了宋代建筑的特征。具有很高的历史价值和艺术价值。 《清明上河图》虽然场面热闹，但表现的并非繁荣市景，而是一幅带有忧患意识的"盛世危图"，官兵懒散税务重。
而我们今天的项目就是通过对算法的改造，实现属于自己的清明上河图。 下面我们将利用vgg19模型训练画作，详细步骤如下，并且我在每个代码上面都注释了方便查看： 首先我们导入先关的库：
import tensorflow as tf import numpy as np import scipy.io import scipy.misc import os import time接着定义一些变量方便调用：
CONTENT_IMG = '1.png' STYLE_IMG = 'sty.jpg' OUTPUT_DIR = 'neural_style_transfer_tensorflow/'再创建一个目录用来保存图片：
if not os.path.exists(OUTPUT_DIR): os.mkdir(OUTPUT_DIR)定义生成图像的长宽通道等信息
IMAGE_W = 400 IMAGE_H = 300 COLOR_C = 3 NOISE_RATIO = 0.7 BETA = 5 ALPHA = 100再接着定义模型路径
VGG_MODEL = 'imagenet-vgg-verydeep-19.mat'生成一个参数矩阵，作为图像的处理过程之一，对像素值运算：
MEAN_VALUES = np.array([123.68, 116.779, 103.939]).reshape((1, 1, 1, 3))再接着定义读取模型函数，下面我都有所注解：
def load_vgg_model(path): ''' Details of the VGG19 model: - 0 is conv1_1 (3, 3, 3, 64) - 1 is relu - 2 is conv1_2 (3, 3, 64, 64) - 3 is relu - 4 is maxpool - 5 is conv2_1 (3, 3, 64, 128) - 6 is relu - 7 is conv2_2 (3, 3, 128, 128) - 8 is relu - 9 is maxpool - 10 is conv3_1 (3, 3, 128, 256) - 11 is relu - 12 is conv3_2 (3, 3, 256, 256) - 13 is relu - 14 is conv3_3 (3, 3, 256, 256) - 15 is relu - 16 is conv3_4 (3, 3, 256, 256) - 17 is relu - 18 is maxpool - 19 is conv4_1 (3, 3, 256, 512) - 20 is relu - 21 is conv4_2 (3, 3, 512, 512) - 22 is relu - 23 is conv4_3 (3, 3, 512, 512) - 24 is relu - 25 is conv4_4 (3, 3, 512, 512) - 26 is relu - 27 is maxpool - 28 is conv5_1 (3, 3, 512, 512) - 29 is relu - 30 is conv5_2 (3, 3, 512, 512) - 31 is relu - 32 is conv5_3 (3, 3, 512, 512) - 33 is relu - 34 is conv5_4 (3, 3, 512, 512) - 35 is relu - 36 is maxpool - 37 is fullyconnected (7, 7, 512, 4096) - 38 is relu - 39 is fullyconnected (1, 1, 4096, 4096) - 40 is relu - 41 is fullyconnected (1, 1, 4096, 1000) - 42 is softmax ''' vgg = scipy.io.loadmat(path) vgg_layers = vgg['layers'] #加载vgg模型获取模型各层参数和名称 def _weights(layer, expected_layer_name): W = vgg_layers[0][layer][0][0][2][0][0] b = vgg_layers[0][layer][0][0][2][0][1] layer_name = vgg_layers[0][layer][0][0][0][0] assert layer_name == expected_layer_name return W, b #将加载的变量初始化成tf可运算的张量类型，函数返回值为激活函数的输出 def _conv2d_relu(prev_layer, layer, layer_name): W, b = _weights(layer, layer_name) W = tf.constant(W) b = tf.constant(np.reshape(b, (b.size))) return tf.nn.relu(tf.nn.conv2d(prev_layer, filter=W, strides=[1, 1, 1, 1], padding='SAME') + b) #定义池化层函数 def _avgpool(prev_layer): return tf.nn.avg_pool(prev_layer, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #将各层输出值都放到列表中方便加载，形成字典 graph = {} graph['input'] = tf.Variable(np.zeros((1, IMAGE_H, IMAGE_W, COLOR_C)), dtype='float32') #定义['conv1_1']为vgg模型的第0层，输入层为上一层的['input' ] graph['conv1_1'] = _conv2d_relu(graph['input'], 0, 'conv1_1') graph['conv1_2'] = _conv2d_relu(graph['conv1_1'], 2, 'conv1_2') graph['avgpool1'] = _avgpool(graph['conv1_2']) graph['conv2_1'] = _conv2d_relu(graph['avgpool1'], 5, 'conv2_1') graph['conv2_2'] = _conv2d_relu(graph['conv2_1'], 7, 'conv2_2') graph['avgpool2'] = _avgpool(graph['conv2_2']) graph['conv3_1'] = _conv2d_relu(graph['avgpool2'], 10, 'conv3_1') graph['conv3_2'] = _conv2d_relu(graph['conv3_1'], 12, 'conv3_2') graph['conv3_3'] = _conv2d_relu(graph['conv3_2'], 14, 'conv3_3') graph['conv3_4'] = _conv2d_relu(graph['conv3_3'], 16, 'conv3_4') graph['avgpool3'] = _avgpool(graph['conv3_4']) graph['conv4_1'] = _conv2d_relu(graph['avgpool3'], 19, 'conv4_1') graph['conv4_2'] = _conv2d_relu(graph['conv4_1'], 21, 'conv4_2') graph['conv4_3'] = _conv2d_relu(graph['conv4_2'], 23, 'conv4_3') graph['conv4_4'] = _conv2d_relu(graph['conv4_3'], 25, 'conv4_4') graph['avgpool4'] = _avgpool(graph['conv4_4']) graph['conv5_1'] = _conv2d_relu(graph['avgpool4'], 28, 'conv5_1') graph['conv5_2'] = _conv2d_relu(graph['conv5_1'], 30, 'conv5_2') graph['conv5_3'] = _conv2d_relu(graph['conv5_2'], 32, 'conv5_3') graph['conv5_4'] = _conv2d_relu(graph['conv5_3'], 34, 'conv5_4') graph['avgpool5'] = _avgpool(graph['conv5_4']) return graph为了实现自己的项目效果，设定损失函数：
#定义内容损失函数，变量为tf计算图和vgg模型参数，返回值为损失值 def content_loss_func(sess, model): #p就是model['conv4_2'])参数，x是model['conv4_2']) def _content_loss(p, x): #p的值为Tensor("Relu_9:0", shape=(1, 75, 100, 512), dtype=float32)，故N为512，M为75*100，分别为卷积核个数，卷积核大小的宽*100 N = p.shape[3] M = p.shape[1] * p.shape[2] return (1 / (4 * N * M)) * tf.reduce_sum(tf.pow(x - p, 2)) return _content_loss(sess.run(model['conv4_2']), model['conv4_2']) STYLE_LAYERS = [('conv1_1', 0.5), ('conv2_1', 1.0), ('conv3_1', 1.5), ('conv4_1', 3.0), ('conv5_1', 4.0)] #返回值为_style_loss的值*0.5,1,1.5,4的加和 def style_loss_func(sess, model): def _gram_matrix(F, N, M): Ft = tf.reshape(F, (M, N)) return tf.matmul(tf.transpose(Ft), Ft) #a,x都为'conv1_1', conv2_1', 'conv3_1', 'conv4_1'，'conv5_1'中的参数遍历 def _style_loss(a, x): #同内容损失函数 N = a.shape[3] M = a.shape[1] * a.shape[2] A = _gram_matrix(a, N, M) G = _gram_matrix(x, N, M) return (1 / (4 * N ** 2 * M ** 2)) * tf.reduce_sum(tf.pow(G - A, 2)) return sum([_style_loss(sess.run(model[layer_name]), model[layer_name]) * w for layer_name, w in STYLE_LAYERS])再定义生成图片，读取图片，保存图片函数：
#产生噪声图片 def generate_noise_image(content_image, noise_ratio=NOISE_RATIO): #随机产生矩阵图片，矩阵元素内容符合标准正太分布 noise_image = np.random.uniform(-20, 20, (1, IMAGE_H, IMAGE_W, COLOR_C)).astype('float32') #将产生的矩阵内各元素与神经网络加和 input_image = noise_image * noise_ratio + content_image * (1 - noise_ratio) return input_image #读取图片，改变尺寸，变成1行多列矩阵，将矩阵与初始值相减返回 def load_image(path): image = scipy.misc.imread(path) image = scipy.misc.imresize(image, (IMAGE_H, IMAGE_W)) #image.shape为[800,600,3],则(1, ) + image.shape)为[1,800,600,3] image = np.reshape(image, ((1, ) + image.shape)) #MEAN_VALUES = np.array([123.68, 116.779, 103.939]).reshape((1, 1, 1, 3)) #其中image为三通道矩阵，MEAN_VALUES为三维矩阵可以相减 image = image - MEAN_VALUES return image #保存图片 def save_image(path, image): image = image + MEAN_VALUES #参见上面图像加载时多加了1维，故形成时要减少维度， image = image[0] #截取所有数值在0-255之间的，因为像素值必须是这个范围。而参数运算后可能会超过这个值 image = np.clip(image, 0, 255).astype('uint8') #保存 scipy.misc.imsave(path, image)下面是训练加载：
#启动计算图 with tf.Session() as sess: #读取图片，返回值为减去MEAN_VALUES的矩阵，矩阵形状为[1,800,600,3] content_image = load_image(CONTENT_IMG) style_image = load_image(STYLE_IMG) #加载vgg19模型，返回值为一个字典，里面为各网络层参数，输入和输出 model = load_vgg_model(VGG_MODEL) #产生噪声图片，返回值为随机矩阵加上网络层参数的新矩阵 input_image = generate_noise_image(content_image) #变量初始化 sess.run(tf.global_variables_initializer()) #从网络层input层开始运算内容图片矩阵 sess.run(model['input'].assign(content_image)) content_loss = content_loss_func(sess, model) # 从网络层input层开始运算内容图片矩阵 sess.run(model['input'].assign(style_image)) style_loss = style_loss_func(sess, model) #总损失为内容损失加上风格损失 total_loss = BETA * content_loss + ALPHA * style_loss #建立优化器以调整参数 optimizer = tf.train.AdamOptimizer(2.0) #优化器调整参数，使得损失为最小 train = optimizer.minimize(total_loss) sess.run(tf.global_variables_initializer()) # 从网络层input层开始运算形成新的图片 sess.run(model['input'].assign(input_image)) ITERATIONS = 2000 #训练2000轮 for i in range(ITERATIONS): sess.run(train) print('Iteration %d' % i) print('Cost: ', sess.run(total_loss)) if i % 100 == 0: #每一百次加载一次网络参数以保存图片 output_image = sess.run(model['input']) print('Iteration %d' % i) print('Cost: ', sess.run(total_loss)) save_image(os.path.join(OUTPUT_DIR, 'output_%d.jpg' % i), output_image)最终得到的效果如图所示：
左边是电视里找的图片，右边是模拟的图片，由此可见生成的效果还是可以的。而这个程序的主要思路就是在一个生成随机矩阵的基础上，通过加载网络层训练参数，然后生成的矩阵值按比例乘以网络参数，然后把矩阵保存为图片即可达到模拟生成的效果。而其中参数的调整是基于深层次网络提取的图像特征按公式运算，通过优化器优化参数，通过训练次数的增加，参数也在逐渐改善，最终形成自己需要的图片效果。 欢迎大家关注公众号，后台回复“风格迁移”
