python 无监督生成模型的示例

import tensorflow as tf  
from tensorflow.keras.layers import Dense  
from tensorflow.keras.models import Sequential  
# 生成器模型  
class Generator(tf.keras.Model):  
    def __init__(self):  
        super(Generator, self).__init__()  
        self.model = Sequential([  
            Dense(256, activation='relu', input_shape=(100,)),  
            Dense(2, activation='tanh')  # 假设我们生成二维数据  
        ])  
    def call(self, inputs):  
        return self.model(inputs)  
# 判别器模型  
class Discriminator(tf.keras.Model):  
    def __init__(self):  
        super(Discriminator, self).__init__()  
        self.model = Sequential([  
            Dense(256, activation='relu', input_shape=(2,)),  
            Dense(1, activation='sigmoid')  # 二分类问题，真实或生成  
        ])  
    def call(self, inputs):  
        return self.model(inputs)  
# 实例化模型  
generator = Generator()  
discriminator = Discriminator()  
# 定义优化器和损失函数  
generator_optimizer = tf.keras.optimizers.Adam(1e-4)  
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)  
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)

def train_step(real_data, batch_size):  
    # ---------------------  
    #  训练判别器  
    # ---------------------  
    # 真实数据  
    noise = tf.random.normal([batch_size, 100])  
    generated_data = generator(noise, training=False)  
    real_loss = cross_entropy(tf.ones_like(discriminator(real_data)), discriminator(real_data))  
    fake_loss = cross_entropy(tf.zeros_like(discriminator(generated_data)), discriminator(generated_data))  
    d_loss = real_loss + fake_loss  
    with tf.GradientTape() as tape:  
        d_loss = d_loss  
    grads_d = tape.gradient(d_loss, discriminator.trainable_variables)  
    discriminator_optimizer.apply_gradients(zip(grads_d, discriminator.trainable_variables))  
    # ---------------------  
    #  训练生成器  
    # ---------------------  
    noise = tf.random.normal([batch_size, 100])  
    with tf.GradientTape() as tape:  
        gen_data = generator(noise, training=True)  
        # 我们希望生成的数据被判别器判断为真实数据  
        valid_y = tf.ones((batch_size, 1))  
        g_loss = cross_entropy(valid_y, discriminator(gen_data))  
    grads_g = tape.gradient(g_loss, generator.trainable_variables)  
    generator_optimizer.apply_gradients(zip(grads_g, generator.trainable_variables))  
# 假设我们有真实的二维数据 real_data，但在此示例中我们仅使用随机数据代替  
real_data = tf.random.normal([batch_size, 2])  
# 训练 GAN  
num_epochs = 10000  
batch_size = 64  
for epoch in range(num_epochs):  
    train_step(real_data, batch_size)  
    # 打印进度或其他监控指标  
    # ...