GAN算法python实现

GAN是生成式对抗网络（Generative Adversarial Network）的缩写，是一种深度学习的算法。下面是一个简单的GAN算法Python实现示例：

# 导入必要的库
import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.layers import Dense, Dropout, Input
from keras.models import Model, Sequential
from keras.optimizers import Adam

# 定义生成器模型
def build_generator():
    generator = Sequential()
    generator.add(Dense(256, input_dim=100, activation='relu'))
    generator.add(Dense(512, activation='relu'))
    generator.add(Dense(784, activation='tanh'))
    generator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return generator

# 定义判别器模型
def build_discriminator():
    discriminator = Sequential()
    discriminator.add(Dense(512, input_dim=784, activation='relu'))
    discriminator.add(Dropout(0.3))
    discriminator.add(Dense(256, activation='relu'))
    discriminator.add(Dropout(0.3))
    discriminator.add(Dense(1, activation='sigmoid'))
    discriminator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return discriminator

# 定义GAN模型
def build_gan(generator, discriminator):
    discriminator.trainable = False
    gan = Sequential()
    gan.add(generator)
    gan.add(discriminator)
    gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return gan

# 加载MNIST数据集
(train_data, _), (_, _) = mnist.load_data()

# 数据预处理
train_data = train_data.reshape(train_data.shape[0], 784)
train_data = train_data / 127.5 - 1.

# 定义超参数
epochs = 10000
batch_size = 128
sample_interval = 1000

# 构建GAN模型
generator = build_generator()
discriminator = build_discriminator()
gan = build_gan(generator, discriminator)

# 开始训练
for epoch in range(epochs):
    # 训练判别器
    idx = np.random.randint(0, train_data.shape[0], batch_size)
    real_imgs = train_data[idx]
    noise = np.random.normal(0, 1, (batch_size, 100))
    fake_imgs = generator.predict(noise)
    d_loss_real = discriminator.train_on_batch(real_imgs, np.ones((batch_size, 1)))
    d_loss_fake = discriminator.train_on_batch(fake_imgs, np.zeros((batch_size, 1)))
    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

    # 训练生成器
    noise = np.random.normal(0, 1, (batch_size, 100))
    g_loss = gan.train_on_batch(noise, np.ones((batch_size, 1)))

    # 打印训练结果
    print("Epoch %d [D loss: %f] [G loss: %f]" % (epoch, d_loss, g_loss))

    # 保存生成器的图片
    if epoch % sample_interval == 0:
        noise = np.random.normal(0, 1, (25, 100))
        gen_imgs = generator.predict(noise)
        gen_imgs = 0.5 * gen_imgs + 0.5
        fig, axs = plt.subplots(5, 5)
        cnt = 0
        for i in range(5):
            for j in range(5):
                axs[i,j].imshow(gen_imgs[cnt, :].reshape(28, 28), cmap='gray')
                axs[i,j].axis('off')
                cnt += 1
        fig.savefig("gan_mnist_%d.png" % epoch)

该示例代码中，首先定义了一个生成器模型和一个判别器模型，然后通过这两个模型构建出GAN模型。接着加载MNIST数据集，并对数据进行预处理。最后开始训练GAN模型，每隔一定的时间就保存一些生成器生成的图片。