TensorFlow练习24: GANs-生成对抗网络 (生成明星脸)

http://blog.topspeedsnail.com/archives/10977

从2D图片生成3D模型（3D-GAN） https://blog.csdn.net/u014365862/article/details/54783209

GANs是Generative Adversarial Networks的简写，中文翻译为生成对抗网络，它最早出现在2014年Goodfellow发表的论文中：Generative Adversarial Networks。GANs是目前深度学习领域最火的网络模型，苹果最近发布的第一篇论文就是关于GANs的：SimGAN。

简单来说，GANs会学着生成和训练数据相似的数据，一个最典型的应用是生成图像。假设你有一堆猫的图片，你使用这些图片训练GANs，之后它会生成和训练数据相类似的猫的图片（它习的了猫的特征）。

GANs用到机器学习的两种模型：Generative生成模型和Discriminative判别模型。

GANs类比：假设G是大伪艺术家，以制作古董赝品为生，G的终极目标是以假乱真。但是呢，又有一些人以鉴宝为生（D）。开始你给D展示了一些古董真品，告诉D这是正品。然后G开始制作赝品，想骗过D，让他分辨不出真假。随着D看到越来越多的真品，G要骗过D就越来越难，当然，G也不是吃闲饭的，它会加倍努力的试图骗过D。随着这种对抗的持续，不仅D鉴宝的本领提高了，G也会越来越擅长制作赝品。这就是名字中生成-对抗的意思。

判别模型可以判断数据属于哪一类，例如<TensorFlow练习23: 恶作剧>训练的CNN模型可以判断一张脸是不是我的脸。相反，生成模型不用预先知道分类，它可生成最符合训练样本分布的新样本。例如高斯混合模型，经过训练，它生成的随机数据符合训练样本的分布。

GANs简单图示：

GAN相关代码实现：

• DCGAN TensorFlow实现
• 根据文本描述生成图像（反过来的: 看图说话Show and Tell）
• 图像补全，叫你在打码
• TF-VAE-GAN-DRAW
• Auxiliary Classifier GAN
• InfoGAN时间序列数据分类
• 生成视频
• Generative Models (OpenAI)

一个TensorFlow代码示例（生成明星脸-EBGAN）

使用的数据集：Large-scale CelebFaces Attributes (CelebA) Dataset，这个数据集包含20万明星脸，可用来做人脸检测、人脸特征识别等等任务。

下载地址：Google Drive或Baidu云。

• Energy Based Generative Adversarial Networks (EBGAN)

代码：

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254

# -*- coding: utf-8 -*- """ Energy Based Generative Adversarial Networks (EBGAN): https://arxiv.org/pdf/1609.03126v2.pdf <blog.topspeedsnail.com> 由于我把Python升级到了3.6破坏了开发环境, 暂时先使用Python 2.7 """ import os import random import numpy as np import tensorflow as tf from PIL import Image #import cv2 import scipy.misc as misc   CELEBA_DATE_DIR= 'img_align_celeba'   train_images = [] for image_filename in os.listdir(CELEBA_DATE_DIR): if image_filename.endswith('.jpg'): train_images.append(os.path.join(CELEBA_DATE_DIR, image_filename))   random.shuffle(train_images)   batch_size = 64 num_batch = len(train_images) // batch_size   # 图像大小和channel IMAGE_SIZE = 64 IMAGE_CHANNEL = 3   def get_next_batch(pointer): image_batch = [] images = train_images[pointer*batch_size:(pointer+1)*batch_size] for img in images: arr = Image.open(img) arr = arr.resize((IMAGE_SIZE, IMAGE_SIZE)) arr = np.array(arr) arr = arr.astype('float32') / 127.5 - 1 #image = cv2.imread(img) #image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE)) #image = image.astype('float32') / 127.5 - 1 image_batch.append(arr) return image_batch   # noise z_dim = 100 noise = tf.placeholder(tf.float32, [None, z_dim], name='noise')   X = tf.placeholder(tf.float32, [batch_size, IMAGE_SIZE, IMAGE_SIZE, IMAGE_CHANNEL], name='X') # 是否在训练阶段 train_phase = tf.placeholder(tf.bool)   # http://stackoverflow.com/a/34634291/2267819 def batch_norm(x, beta, gamma, phase_train, scope='bn', decay=0.9, eps=1e-5): with tf.variable_scope(scope): #beta = tf.get_variable(name='beta', shape=[n_out], initializer=tf.constant_initializer(0.0), trainable=True) #gamma = tf.get_variable(name='gamma', shape=[n_out], initializer=tf.random_normal_initializer(1.0, stddev), trainable=True) batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments') ema = tf.train.ExponentialMovingAverage(decay=decay)   def mean_var_with_update(): ema_apply_op = ema.apply([batch_mean, batch_var]) with tf.control_dependencies([ema_apply_op]): return tf.identity(batch_mean), tf.identity(batch_var)   mean, var = tf.cond(phase_train, mean_var_with_update, lambda: (ema.average(batch_mean), ema.average(batch_var))) normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, eps) return normed   # 重用变量出了点问题, 先用dict generator_variables_dict = { "W_1": tf.Variable(tf.truncated_normal([z_dim, 2 * IMAGE_SIZE * IMAGE_SIZE], stddev=0.02), name='Generator/W_1'), "b_1": tf.Variable(tf.constant(0.0, shape=[2 * IMAGE_SIZE * IMAGE_SIZE]), name='Generator/b_1'), 'beta_1': tf.Variable(tf.constant(0.0, shape=[512]), name='Generator/beta_1'), 'gamma_1': tf.Variable(tf.random_normal(shape=[512], mean=1.0, stddev=0.02), name='Generator/gamma_1'),   "W_2": tf.Variable(tf.truncated_normal([5, 5, 256, 512], stddev=0.02), name='Generator/W_2'), "b_2": tf.Variable(tf.constant(0.0, shape=[256]), name='Generator/b_2'), 'beta_2': tf.Variable(tf.constant(0.0, shape=[256]), name='Generator/beta_2'), 'gamma_2': tf.Variable(tf.random_normal(shape=[256], mean=1.0, stddev=0.02), name='Generator/gamma_2'),   "W_3": tf.Variable(tf.truncated_normal([5, 5, 128, 256], stddev=0.02), name='Generator/W_3'), "b_3": tf.Variable(tf.constant(0.0, shape=[128]), name='Generator/b_3'), 'beta_3': tf.Variable(tf.constant(0.0, shape=[128]), name='Generator/beta_3'), 'gamma_3': tf.Variable(tf.random_normal(shape=[128], mean=1.0, stddev=0.02), name='Generator/gamma_3'),   "W_4": tf.Variable(tf.truncated_normal([5, 5, 64, 128], stddev=0.02), name='Generator/W_4'), "b_4": tf.Variable(tf.constant(0.0, shape=[64]), name='Generator/b_4'), 'beta_4': tf.Variable(tf.constant(0.0, shape=[64]), name='Generator/beta_4'), 'gamma_4': tf.Variable(tf.random_normal(shape=[64], mean=1.0, stddev=0.02), name='Generator/gamma_4'),   "W_5": tf.Variable(tf.truncated_normal([5, 5, IMAGE_CHANNEL, 64], stddev=0.02), name='Generator/W_5'), "b_5": tf.Variable(tf.constant(0.0, shape=[IMAGE_CHANNEL]), name='Generator/b_5') } # Generator def generator(noise): with tf.variable_scope("Generator"): out_1 = tf.matmul(noise, generator_variables_dict["W_1"]) + generator_variables_dict['b_1'] out_1 = tf.reshape(out_1, [-1, IMAGE_SIZE//16, IMAGE_SIZE//16, 512]) out_1 = batch_norm(out_1, generator_variables_dict["beta_1"], generator_variables_dict["gamma_1"], train_phase, scope='bn_1') out_1 = tf.nn.relu(out_1, name='relu_1')   out_2 = tf.nn.conv2d_transpose(out_1, generator_variables_dict['W_2'],  output_shape=tf.pack([tf.shape(out_1)[0], IMAGE_SIZE//8, IMAGE_SIZE//8, 256]), strides=[1, 2, 2, 1], padding='SAME') out_2 = tf.nn.bias_add(out_2, generator_variables_dict['b_2']) out_2 = batch_norm(out_2, generator_variables_dict["beta_2"], generator_variables_dict["gamma_2"], train_phase, scope='bn_2') out_2 = tf.nn.relu(out_2, name='relu_2')   out_3 = tf.nn.conv2d_transpose(out_2, generator_variables_dict['W_3'],  output_shape=tf.pack([tf.shape(out_2)[0], IMAGE_SIZE//4, IMAGE_SIZE//4, 128]), strides=[1, 2, 2, 1], padding='SAME') out_3 = tf.nn.bias_add(out_3, generator_variables_dict['b_3']) out_3 = batch_norm(out_3, generator_variables_dict["beta_3"], generator_variables_dict["gamma_3"], train_phase, scope='bn_3') out_3 = tf.nn.relu(out_3, name='relu_3')   out_4 = tf.nn.conv2d_transpose(out_3, generator_variables_dict['W_4'],  output_shape=tf.pack([tf.shape(out_3)[0], IMAGE_SIZE//2, IMAGE_SIZE//2, 64]), strides=[1, 2, 2, 1], padding='SAME') out_4 = tf.nn.bias_add(out_4, generator_variables_dict['b_4']) out_4 = batch_norm(out_4, generator_variables_dict["beta_4"], generator_variables_dict["gamma_4"], train_phase, scope='bn_4') out_4 = tf.nn.relu(out_4, name='relu_4')   out_5 = tf.nn.conv2d_transpose(out_4, generator_variables_dict['W_5'],  output_shape=tf.pack([tf.shape(out_4)[0], IMAGE_SIZE, IMAGE_SIZE, IMAGE_CHANNEL]), strides=[1, 2, 2, 1], padding='SAME') out_5 = tf.nn.bias_add(out_5, generator_variables_dict['b_5']) out_5 = tf.nn.tanh(out_5, name='tanh_5')   return out_5   discriminator_variables_dict = { "W_1": tf.Variable(tf.truncated_normal([4, 4, IMAGE_CHANNEL, 32], stddev=0.002), name='Discriminator/W_1'), "b_1": tf.Variable(tf.constant(0.0, shape=[32]), name='Discriminator/b_1'), 'beta_1': tf.Variable(tf.constant(0.0, shape=[32]), name='Discriminator/beta_1'), 'gamma_1': tf.Variable(tf.random_normal(shape=[32], mean=1.0, stddev=0.02), name='Discriminator/gamma_1'),   "W_2": tf.Variable(tf.truncated_normal([4, 4, 32, 64], stddev=0.002), name='Discriminator/W_2'), "b_2": tf.Variable(tf.constant(0.0, shape=[64]), name='Discriminator/b_2'), 'beta_2': tf.Variable(tf.constant(0.0, shape=[64]), name='Discriminator/beta_2'), 'gamma_2': tf.Variable(tf.random_normal(shape=[64], mean=1.0, stddev=0.02), name='Discriminator/gamma_2'),   "W_3": tf.Variable(tf.truncated_normal([4, 4, 64, 128], stddev=0.002), name='Discriminator/W_3'), "b_3": tf.Variable(tf.constant(0.0, shape=[128]), name='Discriminator/b_3'), 'beta_3': tf.Variable(tf.constant(0.0, shape=[128]), name='Discriminator/beta_3'), 'gamma_3': tf.Variable(tf.random_normal(shape=[128], mean=1.0, stddev=0.02), name='Discriminator/gamma_3'),   "W_4": tf.Variable(tf.truncated_normal([4, 4, 64, 128], stddev=0.002), name='Discriminator/W_4'), "b_4": tf.Variable(tf.constant(0.0, shape=[64]), name='Discriminator/b_4'), 'beta_4': tf.Variable(tf.constant(0.0, shape=[64]), name='Discriminator/beta_4'), 'gamma_4': tf.Variable(tf.random_normal(shape=[64], mean=1.0, stddev=0.02), name='Discriminator/gamma_4'),   "W_5": tf.Variable(tf.truncated_normal([4, 4, 32, 64], stddev=0.002), name='Discriminator/W_5'), "b_5": tf.Variable(tf.constant(0.0, shape=[32]), name='Discriminator/b_5'), 'beta_5': tf.Variable(tf.constant(0.0, shape=[32]), name='Discriminator/beta_5'), 'gamma_5': tf.Variable(tf.random_normal(shape=[32], mean=1.0, stddev=0.02), name='Discriminator/gamma_5'),   "W_6": tf.Variable(tf.truncated_normal([4, 4, 3, 32], stddev=0.002), name='Discriminator/W_6'), "b_6": tf.Variable(tf.constant(0.0, shape=[3]), name='Discriminator/b_6') } # Discriminator def discriminator(input_images): with tf.variable_scope("Discriminator"): # Encoder out_1 = tf.nn.conv2d(input_images, discriminator_variables_dict['W_1'], strides=[1, 2, 2, 1], padding='SAME') out_1 = tf.nn.bias_add(out_1, discriminator_variables_dict['b_1']) out_1 = batch_norm(out_1, discriminator_variables_dict['beta_1'], discriminator_variables_dict['gamma_1'], train_phase, scope='bn_1') out_1 = tf.maximum(0.2 * out_1, out_1, 'leaky_relu_1')   out_2 = tf.nn.conv2d(out_1, discriminator_variables_dict['W_2'], strides=[1, 2, 2, 1], padding='SAME') out_2 = tf.nn.bias_add(out_2, discriminator_variables_dict['b_2']) out_2 = batch_norm(out_2, discriminator_variables_dict['beta_2'], discriminator_variables_dict['gamma_2'], train_phase, scope='bn_2') out_2 = tf.maximum(0.2 * out_2, out_2, 'leaky_relu_2')   out_3 = tf.nn.conv2d(out_2, discriminator_variables_dict['W_3'], strides=[1, 2, 2, 1], padding='SAME') out_3 = tf.nn.bias_add(out_3, discriminator_variables_dict['b_3']) out_3 = batch_norm(out_3, discriminator_variables_dict['beta_3'], discriminator_variables_dict['gamma_3'], train_phase, scope='bn_3') out_3 = tf.maximum(0.2 * out_3, out_3, 'leaky_relu_3') encode = tf.reshape(out_3, [-1, 2*IMAGE_SIZE*IMAGE_SIZE])   # Decoder out_3 = tf.reshape(encode, [-1, IMAGE_SIZE//8, IMAGE_SIZE//8, 128])   out_4 = tf.nn.conv2d_transpose(out_3, discriminator_variables_dict['W_4'],  output_shape=tf.pack([tf.shape(out_3)[0], IMAGE_SIZE//4, IMAGE_SIZE//4, 64]), strides=[1, 2, 2, 1], padding='SAME') out_4 = tf.nn.bias_add(out_4, discriminator_variables_dict['b_4']) out_4 = batch_norm(out_4, discriminator_variables_dict['beta_4'], discriminator_variables_dict['gamma_4'], train_phase, scope='bn_4') out_4 = tf.maximum(0.2 * out_4, out_4, 'leaky_relu_4')   out_5 = tf.nn.conv2d_transpose(out_4, discriminator_variables_dict['W_5'],  output_shape=tf.pack([tf.shape(out_4)[0], IMAGE_SIZE//2, IMAGE_SIZE//2, 32]), strides=[1, 2, 2, 1], padding='SAME') out_5 = tf.nn.bias_add(out_5, discriminator_variables_dict['b_5']) out_5 = batch_norm(out_5, discriminator_variables_dict['beta_5'], discriminator_variables_dict['gamma_5'], train_phase, scope='bn_5') out_5 = tf.maximum(0.2 * out_5, out_5, 'leaky_relu_5')   out_6 = tf.nn.conv2d_transpose(out_5, discriminator_variables_dict['W_6'],  output_shape=tf.pack([tf.shape(out_5)[0], IMAGE_SIZE, IMAGE_SIZE, 3]), strides=[1, 2, 2, 1], padding='SAME') out_6 = tf.nn.bias_add(out_6, discriminator_variables_dict['b_6']) decoded = tf.nn.tanh(out_6, name="tanh_6")   return encode, decoded   # mean squared errors _, real_decoded = discriminator(X) real_loss = tf.sqrt(2 * tf.nn.l2_loss(real_decoded - X)) / batch_size   fake_image = generator(noise) _, fake_decoded = discriminator(fake_image) fake_loss = tf.sqrt(2 * tf.nn.l2_loss(fake_decoded - fake_image)) / batch_size   # loss # D_loss = real_loss + tf.maximum(1 - fake_loss, 0) margin = 20 D_loss = margin - fake_loss + real_loss G_loss = fake_loss # no pt   def optimizer(loss, d_or_g): optim = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.5) #print([v.name for v in tf.trainable_variables() if v.name.startswith(d_or_g)]) var_list = [v for v in tf.trainable_variables() if v.name.startswith(d_or_g)] gradient = optim.compute_gradients(loss, var_list=var_list) return optim.apply_gradients(gradient)   train_op_G = optimizer(G_loss, 'Generator') train_op_D = optimizer(D_loss, 'Discriminator')   with tf.Session() as sess: sess.run(tf.global_variables_initializer(), feed_dict={train_phase: True}) saver = tf.train.Saver()   # 恢复前一次训练 ckpt = tf.train.get_checkpoint_state('.') if ckpt != None: print(ckpt.model_checkpoint_path) saver.restore(sess, ckpt.model_checkpoint_path) else: print("没找到模型")   step = 0 for i in range(40): for j in range(num_batch): batch_noise = np.random.uniform(-1.0, 1.0, size=[batch_size, z_dim]).astype(np.float32)   d_loss, _ = sess.run([D_loss, train_op_D], feed_dict={noise: batch_noise, X: get_next_batch(j), train_phase: True}) g_loss, _ = sess.run([G_loss, train_op_G], feed_dict={noise: batch_noise, X: get_next_batch(j), train_phase: True}) g_loss, _ = sess.run([G_loss, train_op_G], feed_dict={noise: batch_noise, X: get_next_batch(j), train_phase: True})   print(step, d_loss, g_loss)   # 保存模型并生成图像 if step % 100 == 0: saver.save(sess, "celeba.model", global_step=step)   test_noise = np.random.uniform(-1.0, 1.0, size=(5, z_dim)).astype(np.float32) images = sess.run(fake_image, feed_dict={noise: test_noise, train_phase: False})   for k in range(5): image = images[k, :, :, :] image += 1 image *= 127.5 image = np.clip(image, 0, 255).astype(np.uint8) image = np.reshape(image, (IMAGE_SIZE, IMAGE_SIZE, -1)) misc.imsave('fake_image' + str(step) + str(k) + '.jpg', image)   step += 1

 

2000step就出现了人脸的雏型，接着练吧，再改改参数。

ps.昨天做梦，梦见自己变成矩阵了，太诡异了。

如要转载，请保持本文完整，并注明作者@斗大的熊猫和本文原始地址： http://blog.topspeedsnail.com/archives/10977