tensorflow输出网络结构图

1. 对抗网络GAN的网络结构

对抗网络包含了2个子网络：生成网络（Generator，G）和判别网络（Discriminator，D），其中生成网络负责学习样本的真实分布，判别网络负责将生成网络采样的样本与真实样本区分开来。

2. 对抗网络GAN的训练方法

对于判别网络 D，它的目标是能够很好地分辨出真样本𝒙𝑟与假样本𝒙𝑓。以图片生成为例，它的目标是最小化图片的预测值和真实值之间的交叉熵损失函数：𝑚𝑖𝑛 𝜃ℒ = 𝐶𝑟𝑜𝑠𝑠𝑒𝑛𝑡𝑟𝑜𝑝𝑦(𝐷𝜃(𝒙𝑟),𝑦𝑟,𝐷𝜃(𝒙𝑓),𝑦𝑓)

对于生成网络G(𝒛)，我们希望𝒙𝑓 = 𝐺(𝒛)能够很好地骗过判别网络 D，假样本𝒙𝑓在判别 网络的输出越接近真实的标签越好。也就是说，在训练生成网络时，希望判别网络的输出 𝐷(𝐺(𝒛))越逼近 1 越好，此时的交叉熵损失函数为： 
𝑚𝑖𝑛 𝜙ℒ = 𝐶𝑟𝑜𝑠𝑠𝑒𝑛𝑡𝑟𝑜𝑝𝑦(𝐷(𝐺𝜙(𝒛)),1) = −𝑙𝑜𝑔𝐷(𝐺𝜙(𝒛)) 

3. 常用的GAN衍生方法

（1）DCGAN

GAN网络：基于全连接层实现生成器和判别器，由于图片维度较高，网络参数巨大，训练效果不好

DCGAN网络：采用转置卷积层实现生成器，普通卷积层实现判别器，大大降低了网络参数量，同时图片的生成效果大幅提升。

（２）InfoGAN

使用无监督的方式学习输入ｘ的可解释隐向量ｚ表示方法，即希望隐向量ｚ能够对应到数据的语义特征。

（３）CycleGAN

CycleGAN是无监督方式进行图片相互转换的算法。

（4）WGAN/WGAN-GP

从理论层面分析了原始的GAN使用JS散度存在的缺陷，并提出用Wasserstein距离来解决这个问题。

（5）Self-Attention GAN

基于自我注意力机制的GAN。

（6）BigGAN

利用正则化等技巧保证训练大数据过程的稳定性。

4. 对抗网络DCGAN样例

# 整合数据集

import tensorflow as tf
import glob
import os
import numpy as np
from PIL import Image

resize = 64
batch_size = 64

def preprocess(path):
    img = tf.io.read_file(path)
    img = tf.image.decode_jpeg(img, channels=3)  # fix channels to 3
    img = tf.image.resize(img, [resize, resize])
    img = tf.clip_by_value(img, 0, 255)
    img = img / 127.5 - 1
    return img

# 获取数据集
img_paths = glob.glob('F:/tensorflow/TESTCode/faces/*.jpg')
# print(img_paths)
dataset = tf.data.Dataset.from_tensor_slices(img_paths)
dataset = dataset.map(preprocess)
dataset = dataset.batch(batch_size)
print(dataset)

# 构建生成器网络和对抗器网络

class Generator(tf.keras.Model):
    # 生成器网络
    def __init__(self):
        super(Generator, self).__init__()
        filter = 64

        #转置卷积层1
        self.conv1 = tf.keras.layers.Conv2DTranspose(filter*8, 4, 1, padding='valid', use_bias=False)
        self.bn1 = tf.keras.layers.BatchNormalization()

        # 转置卷积层2
        self.conv2 = tf.keras.layers.Conv2DTranspose(filter * 4, 4, 2, padding='same', use_bias=False)
        self.bn2 = tf.keras.layers.BatchNormalization()

        # 转置卷积层3
        self.conv3 = tf.keras.layers.Conv2DTranspose(filter * 2, 4, 2, padding='same', use_bias=False)
        self.bn3 = tf.keras.layers.BatchNormalization()

        # 转置卷积层4
        self.conv4 = tf.keras.layers.Conv2DTranspose(filter * 1, 4, 2, padding='same', use_bias=False)
        self.bn4 = tf.keras.layers.BatchNormalization()

        # 转置卷积层5
        self.conv5 = tf.keras.layers.Conv2DTranspose(3, 4, 2, padding='same', use_bias=False)

    def call(self, inputs, training=None):
        x = inputs
        x = tf.reshape(x, (x.shape[0], 1, 1, x.shape[1]))
        x = tf.nn.relu(x)

        x = tf.nn.relu(self.bn1(self.conv1(x), training=training))
        x = tf.nn.relu(self.bn2(self.conv2(x), training=training))
        x = tf.nn.relu(self.bn3(self.conv3(x), training=training))
        x = tf.nn.relu(self.bn4(self.conv4(x), training=training))
        x = self.conv5(x)
        x = tf.tanh(x)

        return x

class Discriminator( tf.keras.Model ):
    def __init__(self):
        super(Discriminator, self).__init__()
        filter = 64
        #卷积层1
        self.conv1 = tf.keras.layers.Conv2D(filter, 4, 2, 'valid', use_bias=False)
        self.bn1 = tf.keras.layers.BatchNormalization()

        # 卷积层2
        self.conv2 = tf.keras.layers.Conv2D(filter*2, 4, 2, 'valid', use_bias=False)
        self.bn2 = tf.keras.layers.BatchNormalization()

        # 卷积层3
        self.conv3 = tf.keras.layers.Conv2D(filter*4, 4, 2, 'valid', use_bias=False)
        self.bn3 = tf.keras.layers.BatchNormalization()

        # 卷积层4
        self.conv4 = tf.keras.layers.Conv2D(filter*8, 3, 1, 'valid', use_bias=False)
        self.bn4 = tf.keras.layers.BatchNormalization()

        # 卷积层5
        self.conv5 = tf.keras.layers.Conv2D(filter*16, 3, 1, 'valid', use_bias=False)
        self.bn5 = tf.keras.layers.BatchNormalization()

        # 全局池化层
        self.pool = tf.keras.layers.GlobalAveragePooling2D()

        # 特性打平层
        self.flatten = tf.keras.layers.Flatten()

        # 2分类全连接层
        self.fc = tf.keras.layers.Dense(1)

    def call(self, inputs, training=None):
        x = tf.nn.leaky_relu(self.bn1(self.conv1(inputs), training=training))

        x = tf.nn.leaky_relu(self.bn2(self.conv2(x), training=training))

        x = tf.nn.leaky_relu(self.bn3(self.conv3(x), training=training))

        x = tf.nn.leaky_relu(self.bn4(self.conv4(x), training=training))

        x = tf.nn.leaky_relu(self.bn5(self.conv5(x), training=training))

        x = self.pool(x)

        x = self.flatten(x)

        logits = self.fc(x)

        return logits

# 构建误差函数

def celoss_ones(logits):
    y = tf.ones_like(logits)
    loss = tf.keras.losses.binary_crossentropy(y, logits, from_logits=True)
    return  tf.reduce_mean(loss)

def celoss_zeros(logits):
    y = tf.zeros_like(logits)
    loss = tf.keras.losses.binary_crossentropy(y, logits, from_logits=True)
    return tf.reduce_mean(loss)

def d_loss_fn(generator, discriminator, batch_z, batch_x, is_training):
    # 计算判别器的误差函数
    # 采样生成图片
    fake_image = generator(batch_z, is_training)
    # 判定生成图片
    d_fake_logits = discriminator(fake_image, is_training)
    # 判定真实图片
    d_real_logits = discriminator(batch_x, is_training)
    # 真实图片与1之间的误差
    d_loss_real = celoss_ones(d_real_logits)
    # 生成图片与0之间的误差
    d_loss_fake = celoss_zeros(d_fake_logits)
    # 合并误差
    loss = d_loss_fake + d_loss_real
    return loss

def g_loss_fn(generator, discriminator, batch_z, is_training):
    #采样生成图片
    fake_image = generator(batch_z, is_training)
    # 在训练生成网络时，需要迫使生成图片判定为真
    d_fake_logits = discriminator(fake_image, is_training)
    # 计算生成图片与1之间的误差
    loss = celoss_ones(d_fake_logits)

    return loss

# 保存图片
def save_images(valout, name):
    def preprocess(img):
        img = ((img + 1.0) * 127.5).astype(np.uint8)
        return img
    # imgs数据格式[100,64,64,3]
    imgs = preprocess(valout)

    # 新建一张RGB的Image，接收三通道数据
    new_im = Image.new('RGB', (640, 640))
    index = 0
    for i in range(0, 640, 64): # 10 行图片阵列,i从0开始到640，间隔为64
        for j in range(0, 640, 64): # 10 列图片阵列,i从0开始到640，间隔为64
            im = imgs[index]
            im = Image.fromarray(im, mode='RGB')
            new_im.paste(im, (i, j)) # 写入对应位置
            index += 1     # 保存图片阵列

    new_im.save(name)

# 训练网络

z_dim = 100
learning_rate = 0.0002
db_iter = iter(dataset)
epochs = 300
is_training = True
# 创建生成器
generator = Generator()
generator.build(input_shape=(4, z_dim))
# 创建判别器
discriminator = Discriminator()
discriminator.build(input_shape=(4, 64, 64, 3))
# 分别为生成器和判别器创建优化器
g_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
d_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)

# generator.load_weights('generator.ckpt')
# discriminator.load_weights('discriminator.ckpt')
# print('Loaded chpt!!')

d_losses, g_losses = [], []
for epoch in range(epochs):
    # 1. 训练判别器
    for _ in range(1):        
        # 采样隐藏向量
        batch_z = tf.random.normal([batch_size, z_dim])
        batch_x = next(db_iter) #采样真实图片
        if epoch %100 ==0:
            print(batch_x.shape)
        # 判别器前向计算
        with tf.GradientTape() as tape:
            d_loss = d_loss_fn(generator, discriminator, batch_z, batch_x, is_training)
            if epoch %100 ==0:
                print('d_loss: ', d_loss)

        grads = tape.gradient(d_loss, discriminator.trainable_variables)
        g_optimizer.apply_gradients(grads_and_vars=zip(grads, discriminator.trainable_variables))

    #2.训练生成器
    #采样隐藏向量
    batch_z = tf.random.normal([batch_size, z_dim])
    batch_x = next(db_iter)
    #生成器前向计算
    with tf.GradientTape() as tape:
        g_loss = g_loss_fn(generator, discriminator, batch_z, is_training)
    grads = tape.gradient(g_loss, generator.trainable_variables)
    g_optimizer.apply_gradients(zip(grads, generator.trainable_variables))

    if epoch %100 ==0:
        print(epoch, 'd_loss:', float(d_loss), 'g_loss:', float(g_loss))
        #可视化
        z = tf.random.normal([100, z_dim])
        fake_image = generator(z, training=False)
        # img_path = os.path.join('gan_images','gan-d%.png'%epoch)
        save_images(fake_image.numpy(), 'gan-%d.png'%epoch)
        d_losses.append(float(d_loss))
        g_losses.append(float(g_loss))

        if epoch % 100 == 1:
            # print(d_losses)
            # print(g_losses)
            generator.save_weights('generator.ckpt')
            discriminator.save_weights('discriminator.ckpt')