当使用Keras实现Adversarial Autoencoder(对抗自编码器)模型生成MNIST数字图像时,需要按照以下步骤进行操作。
首先,导入所需的库和模块:
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
from keras.datasets import mnist
import matplotlib.pyplot as plt
import numpy as np
接下来,定义生成器模型和判别器模型:
def build_generator():
model = Sequential()
model.add(Dense(256, input_dim=100))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(1024))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(np.prod((28, 28, 1)), activation='tanh'))
model.add(Reshape((28, 28, 1)))
model.summary()
noise = Input(shape=(100,))
img = model(noise)
return Model(noise, img)
def build_discriminator():
model = Sequential()
model.add(Flatten(input_shape=(28, 28, 1)))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(1, activation='sigmoid'))
model.summary()
img = Input(shape=(28, 28, 1))
validity = model(img)
return Model(img, validity)
然后,定义Adversarial Autoencoder模型:
def build_adversarial_autoencoder(generator, discriminator):
discriminator.trainable = False
autoencoder = Sequential()
autoencoder.add(generator)
autoencoder.add(discriminator)
return autoencoder
接下来,加载MNIST数据集并进行预处理:
(X_train, _), (_, _) = mnist.load_data()
X_train = X_train / 127.5 - 1.
X_train = np.expand_dims(X_train, axis=3)
定义一些超参数:
epochs = 20000
batch_size = 32
sample_interval = 1000
然后,编译生成器、判别器和Adversarial Autoencoder模型:
generator = build_generator()
discriminator = build_discriminator()
adversarial_autoencoder = build_adversarial_autoencoder(generator, discriminator)
discriminator.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))
adversarial_autoencoder.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))
接下来,定义训练过程:
for epoch in range(epochs):
# ---------------------
# 训练判别器
# ---------------------
# 从训练集中随机选择一批图像
idx = np.random.randint(0, X_train.shape[0], batch_size)
imgs = X_train[idx]
# 生成一批噪声数据
noise = np.random.normal(0, 1, (batch_size, 100))
# 使用生成器生成一批假图像
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)
# ---------------------
# 训练生成器
# ---------------------
# 生成一批噪声数据
noise = np.random.normal(0, 1, (batch_size, 100))
# 训练生成器
g_loss = adversarial_autoencoder.train_on_batch(noise, np.ones((batch_size, 1)))
# 打印损失值
print("%d [D loss: %f] [G loss: %f]" % (epoch, d_loss, g_loss))
# 每隔一段时间保存并输出生成的图像样本
if epoch % sample_interval == 0:
r, c = 5, 5
noise = np.random.normal(0, 1, (r * c, 100))
gen_imgs = generator.predict(noise)
gen_imgs = 0.5 * gen_imgs + 0.5
fig, axs = plt.subplots(r, c)
cnt = 0
for i in range(r):
for j in range(c):
axs[i, j].imshow(gen_imgs[cnt, :, :, 0], cmap='gray')
axs[i, j].axis('off')
cnt += 1
plt.show()
plt.close()
最后,运行训练过程:
build_adversarial_autoencoder(generator, discriminator)
这是一个简单的使用Keras实现Adversarial Autoencoder模型生成MNIST数字图像的示例。请注意,这只是一个基础的实现,您可以根据需要进行修改和改进。