基于DCGAN的动漫头像生成神经网络实现

一、前言

1、什么是DCGAN？

2、DCGAN的TensorFlow实现

3、什么是转置卷积？

4、转置卷积的Tensorflow实现

5、Batch Normalization解读

本文假设读者已经了解GAN及CNN的基本原理实现，如不清楚可参考以下文章：

基于GAN的的mnist训练集图片生成神经网络实现

基于CNN的验证码识别神经网络实现

二、实战

1、训练数据处理

(1)数据源：百度云盘 提取码：g5qa

(2)创建一个生成器

class Avatar:

def __init__(self):
self.data_name = 'faces'
self.source_shape = (96, 96, 3)
self.resize_shape = (48, 48, 3)
self.crop = True
self.img_shape = self.source_shape if not self.crop else self.resize_shape
self.img_list = self._get_img_list()
self.batch_size = 64
self.batch_shape = (self.batch_size, ) + self.img_shape
self.chunk_size = len(self.img_list) // self.batch_size

def _get_img_list(self):
path = os.path.join(os.getcwd(), self.data_name, '*.jpg')
return glob(path)

def _get_img(self, name):
assert name in self.img_list
img = scipy.misc.imread(name).astype(np.float32)
assert img.shape == self.source_shape
return self._resize(img) if self.crop else img

def _resize(self, img):
h, w = img.shape[:2]
resize_h, resize_w = self.resize_shape[:2]
crop_h, crop_w = self.source_shape[:2]
j = int(round((h - crop_h) / 2.))
i = int(round((w - crop_w) / 2.))
cropped_image = scipy.misc.imresize(img[j:j + crop_h, i:i + crop_w], [resize_h, resize_w])
return np.array(cropped_image) / 127.5 - 1.

@staticmethod
def save_img(image, path):
scipy.misc.imsave(path, image)
return True

def batches(self):
start = 0
end = self.batch_size
for _ in range(self.chunk_size):
name_list = self.img_list[start:end]
imgs = [self._get_img(name) for name in name_list]
batches = np.zeros(self.batch_shape)
batches[::] = imgs
yield batches
start += self.batch_size
end += self.batch_size

读取本地图片数据并创建一个生成器，作为后续模型数据源

2.模型参数定义

def __init__(self):
self.avatar = Avatar()
# 真实图片shape (height, width, depth)
self.img_shape = self.avatar.img_shape
# 一个batch的图片向量shape (batch, height, width, depth)
self.batch_shape = self.avatar.batch_shape
# 一个batch包含图片数量
self.batch_size = self.avatar.batch_size
# batch数量
self.chunk_size = self.avatar.chunk_size

# 噪音图片size
self.noise_img_size = 100
# 卷积转置输出通道数量
self.gf_size = 64
# 卷积输出通道数量
self.df_size = 64
# 训练循环次数
self.epoch_size = 50
# 学习率
self.learning_rate = 0.0002
# 优化指数衰减率
self.beta1 = 0.5
# 生成图片数量
self.sample_size = 64

3、输入定义

# 真实图片
real_imgs = tf.placeholder(tf.float32, self.batch_shape, name='real_images')
# 噪声图片
noise_imgs = tf.placeholder(tf.float32, [None, self.noise_img_size], name='noise_images')

我们利用随机的噪音输入来生成图片

4、生成器

def generator(self, noise_imgs, train=True):
with tf.variable_scope('generator'):
# 分别对应每个layer的height, width
s_h, s_w, _ = self.img_shape
s_h2, s_w2 = self.conv_out_size_same(s_h, 2), self.conv_out_size_same(s_w, 2)
s_h4, s_w4 = self.conv_out_size_same(s_h2, 2), self.conv_out_size_same(s_w2, 2)
s_h8, s_w8 = self.conv_out_size_same(s_h4, 2), self.conv_out_size_same(s_w4, 2)
s_h16, s_w16 = self.conv_out_size_same(s_h8, 2), self.conv_out_size_same(s_w8, 2)

# layer 0
# 对输入噪音图片进行线性变换
z, h0_w, h0_b = self.linear(noise_imgs, self.gf_size*8*s_h16*s_w16)
# reshape为合适的输入层格式
h0 = tf.reshape(z, [-1, s_h16, s_w16, self.gf_size * 8])
# 对数据进行归一化处理 加快收敛速度
h0 = self.batch_normalizer(h0, train=train, name='g_bn0')
# 激活函数
h0 = tf.nn.relu(h0)

# layer 1
# 卷积转置进行上采样
h1, h1_w, h1_b = self.deconv2d(h0, [self.batch_size, s_h8, s_w8, self.gf_size*4], name='g_h1')
h1 = self.batch_normalizer(h1, train=train, name='g_bn1')
h1 = tf.nn.relu(h1)

# layer 2
h2, h2_w, h2_b = self.deconv2d(h1, [self.batch_size, s_h4, s_w4, self.gf_size*2], name='g_h2')
h2 = self.batch_normalizer(h2, train=train, name='g_bn2')
h2 = tf.nn.relu(h2)

# layer 3
h3, h3_w, h3_b = self.deconv2d(h2, [self.batch_size, s_h2, s_w2, self.gf_size*1], name='g_h3')
h3 = self.batch_normalizer(h3, train=train, name='g_bn3')
h3 = tf.nn.relu(h3)

# layer 4
h4, h4_w, h4_b = self.deconv2d(h3, self.batch_shape, name='g_h4')
return tf.nn.tanh(h4)

DCGAN的生成器为卷积网络，使用转置卷积进行上采样，去除pooling层，利用batch normalization加快收敛速度。

5、判别器

def discriminator(self, real_imgs, reuse=False):
with tf.variable_scope("discriminator", reuse=reuse):
# layer 0
# 卷积操作
h0 = self.conv2d(real_imgs, self.df_size, name='d_h0_conv')
# 激活函数
h0 = self.lrelu(h0)

# layer 1
h1 = self.conv2d(h0, self.df_size*2, name='d_h1_conv')
h1 = self.batch_normalizer(h1, name='d_bn1')
h1 = self.lrelu(h1)

# layer 2
h2 = self.conv2d(h1, self.df_size*4, name='d_h2_conv')
h2 = self.batch_normalizer(h2, name='d_bn2')
h2 = self.lrelu(h2)

# layer 3
h3 = self.conv2d(h2, self.df_size*8, name='d_h3_conv')
h3 = self.batch_normalizer(h3, name='d_bn3')
h3 = self.lrelu(h3)

# layer 4
h4, _, _ = self.linear(tf.reshape(h3, [self.batch_size, -1]), 1, name='d_h4_lin')

return tf.nn.sigmoid(h4), h4

DCGAN的判别器为卷积网络，这里使用卷积操作对图像进行特征提取识别。

6、损失和优化

@staticmethod
def loss_graph(real_logits, fake_logits):
# 生成器图片loss
# 生成器希望判别器判断出来的标签为1
gen_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logits, labels=tf.ones_like(fake_logits)))
# 判别器识别生成器图片loss
# 判别器希望识别出来的标签为0
fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logits, labels=tf.zeros_like(fake_logits)))
# 判别器识别真实图片loss
# 判别器希望识别出来的标签为1
real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=real_logits, labels=tf.ones_like(real_logits)))
# 判别器总loss
dis_loss = tf.add(fake_loss, real_loss)
return gen_loss, fake_loss, real_loss, dis_loss

@staticmethod
def optimizer_graph(gen_loss, dis_loss, learning_rate, beta1):
# 所有定义变量
train_vars = tf.trainable_variables()
# 生成器变量
gen_vars = [var for var in train_vars if var.name.startswith('generator')]
# 判别器变量
dis_vars = [var for var in train_vars if var.name.startswith('discriminator')]
# optimizer
# 生成器与判别器作为两个网络需要分别优化
gen_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(gen_loss, var_list=gen_vars)
dis_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(dis_loss, var_list=dis_vars)
return gen_optimizer, dis_optimizer

7、开始训练

# 开始训练
saver = tf.t
b8c7
rain.Saver()
step = 0
# 指定占用GPU比例
# tensorflow默认占用全部GPU显存 防止在机器显存被其他程序占用过多时可能在启动时报错
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(self.epoch_size):
batches = self.avatar.batches()
for batch_imgs in batches:
# generator的输入噪声
noises = np.random.uniform(-1, 1, size=(self.batch_size, self.noise_img_size)).astype(np.float32)
# 优化
_ = sess.run(dis_optimizer, feed_dict={real_imgs: batch_imgs, noise_imgs: noises})
_ = sess.run(gen_optimizer, feed_dict={noise_imgs: noises})
_ = sess.run(gen_optimizer, feed_dict={noise_imgs: noises})
step += 1
print(datetime.now().strftime('%c'), epoch, step)

8、结果



跑了50个循环大概用了5个小时，笔者GPU比较一般，就不继续训练了。可以看到，到这里已经生成了不错的效果。

三、其他

具体代码可以在我的github上找到：https://github.com/lpty/tensorflow_tutorial