參考來源：
https://github.com/ckmarkoh/neuralart_tensorflow
https://github.com/log0/neural-style-painting/blob/master/TensorFlow%20Implementation%20of%20A%20Neural%20Algorithm%20of%20Artistic%20St

import os import sys import numpy as np import scipy.io import scipy.misc import tensorflow as tf# Output folder for the images. OUTPUT_DIR = 'output/' # Style image to use. STYLE_IMAGE = '/images/ocean.jpg' # Content image to use. CONTENT_IMAGE = '/images/Taipei101.jpg' # Image dimensions constants. IMAGE_WIDTH = 800 IMAGE_HEIGHT = 600 COLOR_CHANNELS = 3############################################################################### # Algorithm constants ############################################################################### # 設置隨機噪聲圖像與內容圖像的比率 NOISE_RATIO = 0.6 # 設置迭代次數 ITERATIONS = 1000 # 設置內容圖像與風格圖像的權重 alpha = 1 beta = 500 # 加載VGG-19 MODEL及設定均值 VGG_Model = 'Downloads/imagenet-vgg-verydeep-19.mat' MEAN_VALUES = np.array([123.68, 116.779, 103.939]).reshape((1, 1, 1, 3)) # 設置需要用到的卷積層 CONTENT_LAYERS = [('conv4_2', 1.)] STYLE_LAYERS = [('conv1_1', 0.2), ('conv2_1', 0.2), ('conv3_1', 0.2), ('conv4_1', 0.2), ('conv5_1', 0.2)]# 生成隨機噪聲圖，與content圖以一定比率融合 def generate_noise_image(content_image, noise_ratio = NOISE_RATIO):"""Returns a noise image intermixed with the content image at a certain ratio."""noise_image = np.random.uniform(-20, 20,(1, IMAGE_HEIGHT, IMAGE_WIDTH, COLOR_CHANNELS)).astype('float32')# White noise image from the content representation. Take a weighted average# of the valuesimg = noise_image * noise_ratio + content_image * (1 - noise_ratio)return imgdef load_image(path):image = scipy.misc.imread(path)# Resize the image for convnet input, there is no change but just# add an extra dimension.image = np.reshape(image, ((1,) + image.shape))# Input to the VGG net expects the mean to be subtracted.image = image - MEAN_VALUESreturn imagedef save_image(path, image):# Output should add back the mean.image = image + MEAN_VALUES# Get rid of the first useless dimension, what remains is the image.image = image[0]image = np.clip(image, 0, 255).astype('uint8')scipy.misc.imsave(path, image)def build_net(ntype, nin, nwb=None):if ntype == 'conv':return tf.nn.relu(tf.nn.conv2d(nin, nwb[0], strides=[1, 1, 1, 1], padding='SAME') + nwb[1])elif ntype == 'pool':return tf.nn.avg_pool(nin, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')def get_weight_bias(vgg_layers, i):weights = vgg_layers[i][0][0][2][0][0]weights = tf.constant(weights)bias = vgg_layers[i][0][0][2][0][1]bias = tf.constant(np.reshape(bias, (bias.size)))return weights, biasdef build_vgg19(path):net = {}vgg_rawnet = scipy.io.loadmat(path)vgg_layers = vgg_rawnet['layers'][0]net['input'] = tf.Variable(np.zeros((1, IMAGE_HEIGHT, IMAGE_WIDTH, 3)).astype('float32'))net['conv1_1'] = build_net('conv', net['input'], get_weight_bias(vgg_layers, 0))net['conv1_2'] = build_net('conv', net['conv1_1'], get_weight_bias(vgg_layers, 2))net['pool1'] = build_net('pool', net['conv1_2'])net['conv2_1'] = build_net('conv', net['pool1'], get_weight_bias(vgg_layers, 5))net['conv2_2'] = build_net('conv', net['conv2_1'], get_weight_bias(vgg_layers, 7))net['pool2'] = build_net('pool', net['conv2_2'])net['conv3_1'] = build_net('conv', net['pool2'], get_weight_bias(vgg_layers, 10))net['conv3_2'] = build_net('conv', net['conv3_1'], get_weight_bias(vgg_layers, 12))net['conv3_3'] = build_net('conv', net['conv3_2'], get_weight_bias(vgg_layers, 14))net['conv3_4'] = build_net('conv', net['conv3_3'], get_weight_bias(vgg_layers, 16))net['pool3'] = build_net('pool', net['conv3_4'])net['conv4_1'] = build_net('conv', net['pool3'], get_weight_bias(vgg_layers, 19))net['conv4_2'] = build_net('conv', net['conv4_1'], get_weight_bias(vgg_layers, 21))net['conv4_3'] = build_net('conv', net['conv4_2'], get_weight_bias(vgg_layers, 23))net['conv4_4'] = build_net('conv', net['conv4_3'], get_weight_bias(vgg_layers, 25))net['pool4'] = build_net('pool', net['conv4_4'])net['conv5_1'] = build_net('conv', net['pool4'], get_weight_bias(vgg_layers, 28))net['conv5_2'] = build_net('conv', net['conv5_1'], get_weight_bias(vgg_layers, 30))net['conv5_3'] = build_net('conv', net['conv5_2'], get_weight_bias(vgg_layers, 32))net['conv5_4'] = build_net('conv', net['conv5_3'], get_weight_bias(vgg_layers, 34))net['pool5'] = build_net('pool', net['conv5_4'])return netdef content_layer_loss(p, x):M = p.shape[1] * p.shape[2]N = p.shape[3]loss = (1. / (2 * N * M)) * tf.reduce_sum(tf.pow((x - p), 2))return lossdef content_loss_func(sess, net):layers = CONTENT_LAYERStotal_content_loss = 0.0for layer_name, weight in layers:p = sess.run(net[layer_name])x = net[layer_name]total_content_loss += content_layer_loss(p, x)*weighttotal_content_loss /= float(len(layers))return total_content_lossdef gram_matrix(x, area, depth):x1 = tf.reshape(x, (area, depth))g = tf.matmul(tf.transpose(x1), x1)return gdef style_layer_loss(a, x):M = a.shape[1] * a.shape[2]N = a.shape[3]A = gram_matrix(a, M, N)G = gram_matrix(x, M, N)loss = (1. / (4 * N ** 2 * M ** 2)) * tf.reduce_sum(tf.pow((G - A), 2))return lossdef style_loss_func(sess, net):layers = STYLE_LAYERStotal_style_loss = 0.0for layer_name, weight in layers:a = sess.run(net[layer_name])x = net[layer_name]total_style_loss += style_layer_loss(a, x) * weighttotal_style_loss /= float(len(layers))return total_style_lossdef main():net = build_vgg19(VGG_Model)sess = tf.Session()sess.run(tf.initialize_all_variables())content_img = load_image(CONTENT_IMAGE)style_img = load_image(STYLE_IMAGE)sess.run([net['input'].assign(content_img)])cost_content = content_loss_func(sess, net)sess.run([net['input'].assign(style_img)])cost_style = style_loss_func(sess, net)total_loss = alpha * cost_content + beta * cost_styleoptimizer = tf.train.AdamOptimizer(2.0)init_img = generate_noise_image(content_img)train_op = optimizer.minimize(total_loss)sess.run(tf.initialize_all_variables())sess.run(net['input'].assign(init_img))for it in range(ITERATIONS):sess.run(train_op)if it % 100 == 0:# Print every 100 iteration.mixed_image = sess.run(net['input'])print('Iteration %d' % (it))print('sum : ', sess.run(tf.reduce_sum(mixed_image)))print('cost: ', sess.run(total_loss))if not os.path.exists(OUTPUT_DIR):os.mkdir(OUTPUT_DIR)filename = 'output/%d.png' % (it)save_image(filename, mixed_image)if __name__ == '__main__':main()

提取密碼fwat

總結

以上是生活随笔為你收集整理的tensorflow 风格迁移的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。