GAN的一个简单 例子

gan_example.py

import numpy as np
import keras
from keras.models import Sequential, Model
from keras.layers import Dense, Input, LeakyReLU, Dropout
from keras.optimizers import Adam

# 超参数设置
latent_dim = 100
img_shape = (28, 28, 1)

# 构建生成器
def build_generator():
    model = Sequential()
    model.add(Dense(256, input_dim=latent_dim))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(512))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(1024))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(np.prod(img_shape), activation='tanh'))
    return model

# 构建判别器
def build_discriminator():
    model = Sequential()
    model.add(Dense(512, input_dim=np.prod(img_shape)))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(256))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.4))
    model.add(Dense(1, activation='sigmoid'))
    return model

# 创建生成器和判别器
generator = build_generator()
discriminator = build_discriminator()
discriminator.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5), metrics=['accuracy'])

# 构建组合模型
z = Input(shape=(latent_dim,))
img = generator(z)
discriminator.trainable = False
valid = discriminator(img)
combined = Model(z, valid)
combined.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))

# 准备训练数据（MNIST数据集）
from keras.datasets import mnist
(X_train, _), (_, _) = mnist.load_data()
X_train = (X_train.astype(np.float32) - 127.5) / 127.5
X_train = np.expand_dims(X_train, axis=3)

# 训练GAN
batch_size = 128
epochs = 10000
for epoch in range(epochs):
    # 随机选择一个真实图像批次
    idx = np.random.randint(0, X_train.shape[0], batch_size)
    imgs = X_train[idx]

    # 生成一个假图像批次
    noise = np.random.normal(0, 1, (batch_size, latent_dim))
    gen_imgs = generator.predict(noise)

    # 训练判别器
    d_loss_real = discriminator.train_on_batch(imgs, np.ones((batch_size, 1)))
    d_loss_fake = discriminator.train_on_batch(gen_imgs, np.zeros((batch_size, 1)))
    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

    # 生成一个假图像批次
    noise = np.random.normal(0, 1, (batch_size, latent_dim))

    # 训练生成器（让判别器错误地将生成的图像判断为真实图像）
    g_loss = combined.train_on_batch(noise, np.ones((batch_size, 1)))

    # 输出训练进度信息
    print("Epoch %d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100 * d_loss[1], g_loss))

    # 每隔一定间隔保存生成的图像
    if epoch % 1000 == 0:
       save_imgs(epoch)