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import tensorflow as tf
from tensorflow.keras.layers import Dense, Reshape, Flatten, Conv2D, Conv2DTranspose, LeakyReLU, Dropout
from tensorflow.keras.models import Sequential
import numpy as np
import matplotlib.pyplot as plt
# 生成器模型
def build_generator():
model = Sequential()
model.add(Dense(256, input_dim=100))
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(28 * 28 * 1, activation='tanh'))
model.add(Reshape((28, 28, 1)))
return model
# 判别器模型
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(Dropout(0.3))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.3))
model.add(Dense(1, activation='sigmoid'))
return model
# 编译GAN模型
def compile_gan(generator, discriminator):
discriminator.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
discriminator.trainable = False
gan_input = tf.keras.Input(shape=(100,))
gan_output = discriminator(generator(gan_input))
gan = tf.keras.Model(gan_input, gan_output)
gan.compile(optimizer='adam', loss='binary_crossentropy')
return gan
# 实例化并编译模型
generator = build_generator()
discriminator = build_discriminator()
gan = compile_gan(generator, discriminator)
# 示例训练过程
def train_gan(gan, generator, discriminator, epochs=10000, batch_size=128, save_interval=1000):
(X_train, _), (_, _) = tf.keras.datasets.mnist.load_data()
X_train = (X_train.astype(np.float32) - 127.5) / 127.5
X_train = np.expand_dims(X_train, axis=3)
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
for epoch in range(epochs):
idx = np.random.randint(0, X_train.shape[0], batch_size)
imgs = X_train[idx]
noise = np.random.normal(0, 1, (batch_size, 100))
gen_imgs = generator.predict(noise)
d_loss_real = discriminator.train_on_batch(imgs, valid)
d_loss_fake = discriminator.train_on_batch(gen_imgs, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
noise = np.random.normal(0, 1, (batch_size, 100))
g_loss = gan.train_on_batch(noise, valid)
if epoch % save_interval == 0:
print(f"{epoch} [D loss: {d_loss[0]} | D accuracy: {100 * d_loss[1]}] [G loss: {g_loss}]")
save_images(generator, epoch)
def save_images(generator, epoch, examples=10, dim=(1, 10), figsize=(10, 1)):
noise = np.random.normal(0, 1, (examples, 100))
generated_images = generator.predict(noise)
generated_images = generated_images.reshape(examples, 28, 28)
plt.figure(figsize=figsize)
for i in range(examples):
plt.subplot(dim[0], dim[1], i + 1)
plt.imshow(generated_images[i], interpolation='nearest', cmap='gray')
plt.axis('off')
plt.tight_layout()
plt.savefig(f"gan_generated_image_epoch_{epoch}.png")
# 开始训练
train_gan(gan, generator, discriminator)

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