前面介紹了利用卷積神經網絡實現圖像風格遷移的算法原理和基于TensroFlow 的代碼實現，這篇博客對前面的代碼做了一些改變，設置了一個 image resize 函數，這樣可以處理任意size的 input image，而且我們嘗試利用 L-BFGS 優化算法替代之前的 Adam 優化算法，對卷積層以及pooling層函數做了修改。

轉載出處

import numpy as np import scipy.io import scipy.misc from scipy.misc import imresize, imread import tensorflow as tf############################################################################### # Constants for the image input and output. ################################################################################ Output folder for the images. OUTPUT_DIR = 'output/' # Style image to use. STYLE_IMAGE = 'images/the_scream.jpg' # Content image to use. CONTENT_IMAGE = 'images/Taipei101.jpg' # Image dimensions constants. IMAGE_WIDTH = 600 IMAGE_HEIGHT = 400 COLOR_CHANNELS = 3############################################################################### # Algorithm constants ############################################################################### # Noise ratio. Percentage of weight of the noise for intermixing with the # content image. NOISE_RATIO = 0.5 # Number of iterations to run. ITERATIONS = 500 # Constant to put more emphasis on content loss. alpha = 1 # Constant to put more emphasis on style loss. beta = 500 VGG_Model = 'imagenet-vgg-verydeep-19.mat' MEAN_VALUES = np.array([123.68, 116.779, 103.939]).reshape((1, 1, 1, 3))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)]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 = imread(path)image = imresize(image, (IMAGE_HEIGHT, IMAGE_WIDTH))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 get_weight_bias(vgg_layers, layer_i):weights = vgg_layers[layer_i][0][0][2][0][0]w = tf.constant(weights)bias = vgg_layers[layer_i][0][0][2][0][1]b = tf.constant(np.reshape(bias, (bias.size)))layer_name = vgg_layers[layer_i][0][0][0]print layer_namereturn w, bdef conv_relu_layer(layer_input, nwb):conv_val = tf.nn.conv2d(layer_input, nwb[0], strides=[1, 1, 1, 1], padding='SAME')relu_val = tf.nn.relu(conv_val + nwb[1])return relu_valdef pool_layer(pool_style, layer_input):if pool_style == 'avg':return tf.nn.avg_pool(layer_input, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')elif pool_style == 'max':return tf.nn.max_pool(layer_input, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')def 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'] = conv_relu_layer(net['input'], get_weight_bias(vgg_layers, 0))net['conv1_2'] = conv_relu_layer(net['conv1_1'], get_weight_bias(vgg_layers, 2))net['pool1'] = pool_layer('avg', net['conv1_2'])net['conv2_1'] = conv_relu_layer(net['pool1'], get_weight_bias(vgg_layers, 5))net['conv2_2'] = conv_relu_layer(net['conv2_1'], get_weight_bias(vgg_layers, 7))net['pool2'] = pool_layer('max', net['conv2_2'])net['conv3_1'] = conv_relu_layer(net['pool2'], get_weight_bias(vgg_layers, 10))net['conv3_2'] = conv_relu_layer(net['conv3_1'], get_weight_bias(vgg_layers, 12))net['conv3_3'] = conv_relu_layer(net['conv3_2'], get_weight_bias(vgg_layers, 14))net['conv3_4'] = conv_relu_layer(net['conv3_3'], get_weight_bias(vgg_layers, 16))net['pool3'] = pool_layer('avg', net['conv3_4'])net['conv4_1'] = conv_relu_layer(net['pool3'], get_weight_bias(vgg_layers, 19))net['conv4_2'] = conv_relu_layer(net['conv4_1'], get_weight_bias(vgg_layers, 21))net['conv4_3'] = conv_relu_layer(net['conv4_2'], get_weight_bias(vgg_layers, 23))net['conv4_4'] = conv_relu_layer(net['conv4_3'], get_weight_bias(vgg_layers, 25))net['pool4'] = pool_layer('max', net['conv4_4'])net['conv5_1'] = conv_relu_layer(net['pool4'], get_weight_bias(vgg_layers, 28))net['conv5_2'] = conv_relu_layer(net['conv5_1'], get_weight_bias(vgg_layers, 30))net['conv5_3'] = conv_relu_layer(net['conv5_2'], get_weight_bias(vgg_layers, 32))net['conv5_4'] = conv_relu_layer(net['conv5_3'], get_weight_bias(vgg_layers, 34))net['pool5'] = pool_layer('avg', 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.contrib.opt.ScipyOptimizerInterface(total_loss, method='L-BFGS-B',options={'maxiter': ITERATIONS,'disp': 0})init_img = generate_noise_image(content_img)sess.run(tf.initialize_all_variables())sess.run(net['input'].assign(init_img))optimizer.minimize(sess)mixed_img = sess.run(net['input'])filename = 'output/out.png'save_image(filename, mixed_img)if __name__ == '__main__':main()

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