生成对抗网络（GAN）在图像生成中的应用与实践

 import tensorflow as tf

    
 from tensorflow.keras import layers
    
 import numpy as np
    
 import matplotlib.pyplot as plt
    
  
    
 # 生成器网络
    
 def build_generator():
    
     model = tf.keras.Sequential()
    
     model.add(layers.Dense(128, activation='relu', input_dim=100))
    
     model.add(layers.Dense(784, activation='sigmoid'))
    
     model.add(layers.Reshape((28, 28, 1)))
    
     return model
    
  
    
 # 判别器网络
    
 def build_discriminator():
    
     model = tf.keras.Sequential()
    
     model.add(layers.Flatten(input_shape=(28, 28, 1)))
    
     model.add(layers.Dense(128, activation='relu'))
    
     model.add(layers.Dense(1, activation='sigmoid'))
    
     return model
    
  
    
 # GAN模型
    
 def build_gan(generator, discriminator):
    
     discriminator.trainable = False
    
     gan_input = layers.Input(shape=(100,))
    
     x = generator(gan_input)
    
     gan_output = discriminator(x)
    
     gan = tf.keras.Model(gan_input, gan_output)
    
     return gan
    
  
    
 # 加载MNIST数据
    
 (x_train, _), (_, _) = tf.keras.datasets.mnist.load_data()
    
 x_train = x_train / 255.0
    
 x_train = np.expand_dims(x_train, axis=-1)
    
  
    
 # 创建生成器和判别器
    
 generator = build_generator()
    
 discriminator = build_discriminator()
    
  
    
 # 编译判别器
    
 discriminator.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    
  
    
 # 创建并编译GAN模型
    
 gan = build_gan(generator, discriminator)
    
 gan.compile(optimizer='adam', loss='binary_crossentropy')
    
  
    
 # 训练过程
    
 epochs = 10000
    
 batch_size = 64
    
 half_batch = batch_size // 2
    
  
    
 def plot_generated_images(epoch, generator, examples=10, dim=(1, 10), figsize=(10, 1)):
    
     noise = np.random.normal(0, 1, (examples, 100))
    
     generated_images = generator.predict(noise)
    
     plt.figure(figsize=figsize)
    
     for i in range(examples):
    
     plt.subplot(dim[0], dim[1], i + 1)
    
     plt.imshow(generated_images[i, :, :, 0], interpolation='nearest', cmap='gray')
    
     plt.axis('off')
    
     plt.tight_layout()
    
     plt.savefig(f'gan_generated_image_epoch_{epoch}.png')
    
     plt.close()
    
  
    
 # 训练循环
    
 for epoch in range(epochs):
    
     idx = np.random.randint(0, x_train.shape[0], half_batch)
    
     real_images = x_train[idx]
    
     noise = np.random.normal(0, 1, (half_batch, 100))
    
     fake_images = generator.predict(noise)
    
  
    
     d_loss_real = discriminator.train_on_batch(real_images, np.ones((half_batch, 1)))
    
     d_loss_fake = discriminator.train_on_batch(fake_images, np.zeros((half_batch, 1)))
    
     d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
    
  
    
     noise = np.random.normal(0, 1, (batch_size, 100))
    
     valid_y = np.ones((batch_size, 1))
    
     g_loss = gan.train_on_batch(noise, valid_y)
    
  
    
     if epoch % 1000 == 0:
    
     print(f"{epoch} [D loss: {d_loss[0]} | D accuracy: {100*d_loss[1]}] [G loss: {g_loss}]")
    
     plot_generated_images(epoch, generator)
    
    
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/tJsSG47k8KjboyHgIzTn1DmcLMx6.png)