CNN和GAN混合结构的图像去雾算法代码

以下是一个简单的CNN和GAN混合结构的图像去雾算法代码示例，供参考：

import tensorflow as tf
from keras.models import Model
from keras.layers import Input, Conv2D, Conv2DTranspose, concatenate, LeakyReLU, BatchNormalization
from keras.initializers import RandomNormal
from keras.optimizers import Adam

def build_generator():
  # Encoder
  input_img = Input(shape=(None, None, 3))
  conv1 = Conv2D(32, (3, 3), padding='same', kernel_initializer=RandomNormal(stddev=0.02))(input_img)
  conv1 = BatchNormalization()(conv1)
  conv1 = LeakyReLU(alpha=0.2)(conv1)
  conv2 = Conv2D(64, (3, 3), padding='same', strides=(2, 2), kernel_initializer=RandomNormal(stddev=0.02))(conv1)
  conv2 = BatchNormalization()(conv2)
  conv2 = LeakyReLU(alpha=0.2)(conv2)
  conv3 = Conv2D(128, (3, 3), padding='same', strides=(2, 2), kernel_initializer=RandomNormal(stddev=0.02))(conv2)
  conv3 = BatchNormalization()(conv3)
  conv3 = LeakyReLU(alpha=0.2)(conv3)
  
  # Decoder
  deconv1 = Conv2DTranspose(64, (3, 3), padding='same', strides=(2, 2), kernel_initializer=RandomNormal(stddev=0.02))(conv3)
  deconv1 = BatchNormalization()(deconv1)
  deconv1 = LeakyReLU(alpha=0.2)(deconv1)
  deconv1 = concatenate([deconv1, conv2])
  deconv2 = Conv2DTranspose(32, (3, 3), padding='same', strides=(2, 2), kernel_initializer=RandomNormal(stddev=0.02))(deconv1)
  deconv2 = BatchNormalization()(deconv2)
  deconv2 = LeakyReLU(alpha=0.2)(deconv2)
  deconv2 = concatenate([deconv2, conv1])
  output_img = Conv2D(3, (3, 3), padding='same', activation='tanh', kernel_initializer=RandomNormal(stddev=0.02))(deconv2)

  model = Model(inputs=input_img, outputs=output_img)
  return model

def build_discriminator():
  input_img = Input(shape=(None, None, 3))
  conv1 = Conv2D(32, (3, 3), padding='same', strides=(2, 2), kernel_initializer=RandomNormal(stddev=0.02))(input_img)
  conv1 = LeakyReLU(alpha=0.2)(conv1)
  conv2 = Conv2D(64, (3, 3), padding='same', strides=(2, 2), kernel_initializer=RandomNormal(stddev=0.02))(conv1)
  conv2 = BatchNormalization()(conv2)
  conv2 = LeakyReLU(alpha=0.2)(conv2)
  conv3 = Conv2D(128, (3, 3), padding='same', strides=(2, 2), kernel_initializer=RandomNormal(stddev=0.02))(conv2)
  conv3 = BatchNormalization()(conv3)
  conv3 = LeakyReLU(alpha=0.2)(conv3)
  conv4 = Conv2D(256, (3, 3), padding='same', strides=(2, 2), kernel_initializer=RandomNormal(stddev=0.02))(conv3)
  conv4 = BatchNormalization()(conv4)
  conv4 = LeakyReLU(alpha=0.2)(conv4)
  output = Conv2D(1, (3, 3), padding='same', activation='sigmoid')(conv4)

  model = Model(inputs=input_img, outputs=output)
  return model

def build_gan(generator, discriminator):
  discriminator.trainable = False
  input_img = Input(shape=(None, None, 3))
  generated_img = generator(input_img)
  validity = discriminator(generated_img)
  model = Model(inputs=input_img, outputs=[generated_img, validity])
  return model

# 定义超参数
lr = 0.0002
beta_1 = 0.5
epochs = 200
batch_size = 16

# 构建生成器、判别器和GAN模型
generator = build_generator()
discriminator = build_discriminator()
gan = build_gan(generator, discriminator)

# 编译GAN模型
optimizer = Adam(lr, beta_1)
gan.compile(loss=['mean_absolute_error', 'binary_crossentropy'], loss_weights=[1, 1], optimizer=optimizer)

# 加载数据集
# ...

# 开始训练GAN模型
for epoch in range(epochs):
  for batch_i in range(batch_count):
    # 获取一个批次的数据
    # ...
    
    # 训练判别器
    # ...
    
    # 训练生成器
    # ...
    
  # 保存模型
  # ...
  
  # 显示训练进度
  # ...

需要注意的是，这只是一个简单的代码示例，具体的实现可能会因为数据集、网络结构等因素而有所不同，读者可以根据自己的需求进行修改和优化。