GANmatlab代码

以下是一个简单的 GAN 的 MATLAB 代码示例：

% 设置超参数
num_epochs = 200;
batch_size = 64;
learning_rate = 0.0002;
latent_dim = 100;

% 加载数据
% ...

% 定义生成器
generator = Sequential([
    Dense(256, 'relu', input_shape=[latent_dim]),
    Dense(512, 'relu'),
    Dense(1024, 'relu'),
    Dense(784, 'tanh')
])

% 定义判别器
discriminator = Sequential([
    Dense(1024, 'relu', input_shape=[784]),
    Dropout(0.3),
    Dense(512, 'relu'),
    Dropout(0.3),
    Dense(256, 'relu'),
    Dropout(0.3),
    Dense(1, 'sigmoid')
])

% 定义优化器和损失函数
generator_optimizer = Adam(learning_rate)
discriminator_optimizer = Adam(learning_rate)
loss = BinaryCrossentropy()

% 训练模型
for epoch in range(num_epochs):
    for i in range(dataset_size // batch_size):
        % 训练判别器
        noise = tf.random.normal([batch_size, latent_dim])
        generated_images = generator(noise)
        real_images = dataset[i * batch_size: (i + 1) * batch_size]
        combined_images = tf.concat([generated_images, real_images], axis=0)
        labels = tf.concat([tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0)
        with tf.GradientTape() as tape:
            predictions = discriminator(combined_images)
            d_loss = loss(labels, predictions)
        grads = tape.gradient(d_loss, discriminator.trainable_variables)
        discriminator_optimizer.apply_gradients(zip(grads, discriminator.trainable_variables))
        
        % 训练生成器
        noise = tf.random.normal([batch_size, latent_dim])
        labels = tf.ones((batch_size, 1))
        with tf.GradientTape() as tape:
            generated_images = generator(noise)
            predictions = discriminator(generated_images)
            g_loss = loss(labels, predictions)
        grads = tape.gradient(g_loss, generator.trainable_variables)
        generator_optimizer.apply_gradients(zip(grads, generator.trainable_variables))

这是一个简单的 GAN 实现，你可以根据自己的需求和数据集来进行修改和调整。